Tissue treatment system

Abstract

A cardiac information dynamic display system has one or more electrodes and a cardiac information console. The one or more electrodes record a set of potential data representative of cardiac activity at a plurality of time intervals. The cardiac information console includes a signal processor and a user interface module. A signal processor calculates a set of cardiac activity data at a plurality of time intervals using the recorded set of potential data. The cardiac activity data is associated with a surface location of one or more cardiac cavities. The user interface module displays information related to the cardiac activity data. The information is presented relative to a graphical representation of the surface of the one or more cardiac cavities.

Description

[0001] Cross-references to related applications This application claims priority to U.S. Provisional Patent Application No. 63 / 467,453, filed May 18, 2023, entitled “TISSUE TREATMENT SYSTEM”, which is incorporated herein by reference.

[0002] While this application does not claim priority to the following application, it may refer to priority to U.S. Application No. 18 / 582,299, filed February 20, 2024, entitled “CARDIAC ANALYSIS USER INTERFACE SYSTEM AND METHOD”, which is incorporated herein by reference.

[0003] While this application does not claim priority to the following applications, it may refer to: U.S. Application No. 18 / 275,501, filed August 2, 2023, entitled “Energy Delivery Systems With Ablation Index,” which is a U.S. national phase application of Patent Cooperation Treaty Application No. PCT / US2022 / 016722, filed February 17, 2022, entitled “Energy Delivery Systems With Ablation Index,” which claims priority to U.S. Provisional Application No. 63 / 150,555, filed February 17, 2021, entitled “Energy Delivery Systems With Ablation Index,” both of which are incorporated herein by reference.

[0004] While this application does not claim priority to the following applications, it may relate to priority to: U.S. Application No. 18 / 291,340, filed January 23, 2024, entitled “TISSUE TREATMENT SYSTEM,” which is a national phase application of Patent Cooperation Treaty Application No. PCT / US22 / 038464, filed July 27, 2022, entitled “TISSUE TREATMENT SYSTEM,” which claims priority to U.S. Provisional Application No. 63 / 203,606, filed July 27, 2021, entitled “TISSUE TREATMENT SYSTEM,” and U.S. Provisional Application No. 63 / 203,606, filed April 28, 2022, entitled “TISSUE… Priority to U.S. Provisional Application No. 63 / 335,939, “TREATMENT SYSTEM”, which is incorporated herein by reference.

[0005] While this application does not claim priority to the following applications, it may relate to U.S. Application No. 18 / 291,334, filed January 23, 2024, entitled "ENERGY DELIVERY SYSTEMS WITH LESION INDEX," which is a national phase application of Patent Cooperation Treaty Application No. PCT / US22 / 038461, filed July 27, 2022, entitled "ENERGY DELIVERY SYSTEMS WITH LESION INDEX," which claims priority to U.S. Provisional Application No. 63 / 226,040, filed July 27, 2021, entitled "ENERGY DELIVERY SYSTEMS WITH LESION INDEX," and U.S. Provisional Application No. 63 / 226,040, filed April 28, 2022, entitled "ENERGY DELIVERY..." Priority to U.S. Provisional Application No. 63 / 336,245, “Systems with Lesion Index”, which is incorporated herein by reference.

[0006] While this application does not claim priority to the following applications, it may relate to priority to U.S. continuation application filed June 1, 2023, entitled "Ablation System with Force Control," Serial No. 18 / 204,467, which claims priority to U.S. application filed March 22, 2019, entitled "Ablation System with Force Control," Serial No. 16 / 335,893, which is a 35 USC 371 national phase application filed October 11, 2017, entitled "Ablation System with Force Control," application No. PCT / US2017 / 056064, which claims priority to U.S. application filed October 11, 2016, entitled "Ablation System with Force Control." The priority of U.S. Provisional Application No. 62 / 406,748 entitled "Ablation System with ForceControl", filed on May 10, 2017, and U.S. Provisional Application No. 62 / 504,139 entitled "Ablation System with ForceControl", is incorporated herein by reference.

[0007] While this application does not claim priority to the following applications, it may refer to: U.S. Application No. 16 / 097,955, filed October 31, 2018, entitled “Cardiac Information Dynamic Display System and Method,” which is a 35 USC 371 national phase application of Patent Cooperation Treaty Application No. PCT / US2017 / 030915, entitled “Cardiac Information Dynamic Display System and Method,” filed May 3, 2017, which claims priority to U.S. Provisional Application No. 62 / 331,351, filed May 3, 2016, entitled “Cardiac Information Dynamic Display System and Method,” both of which are incorporated herein by reference.

[0008] While this application does not claim priority to the following applications, it may relate to U.S. Patent Application Serial No. 16 / 861,814, filed April 29, 2020, entitled "Catheter System and Methods of Medical Uses of Same, including Diagnostic and Treatment Uses for the Heart," which is a continuation of U.S. Patent Application No. 10,667,753, filed June 19, 2018, entitled "Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart." U.S. Patent Application No. 10,667,753 is a continuation of U.S. Patent Application No. 10,667,753, filed February 20, 2015, entitled "Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart." The Heart (Catheter System and Methods of Medical Uses of the Heart, Including Diagnostic and Treatment Uses for the Heart), U.S. Application No. 10,004,459, is a continuation of Patent Application No. 10,004,459, filed August 30, 2013, entitled "Catheter System and Methods of Medical Uses of the Heart, Including Diagnostic and Treatment Uses for the Heart," application No. PCT / US2013 / 057579, under the Patent Cooperation Treaty 35 USC 371. This national phase application claims priority to U.S. Provisional Patent Application No. 61 / 695,535, filed August 31, 2012, entitled "System and Method for Diagnosing and Treating Heart Tissue," both of which are incorporated herein by reference.

[0009] While this application does not claim priority to the following applications, it may relate to U.S. Patent Application No. 17 / 887,779, filed August 15, 2022, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," which is a continuation of U.S. Patent Application No. 16 / 242,810, filed January 8, 2019, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," which in turn is a continuation of U.S. Patent Application No. 16 / 242,810, filed July 23, 2015, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways." This is a continuation of U.S. Patent Application No. 10,201,311, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," filed February 7, 2014, under Patent Cooperation Treaty Application No. PCT / US2014 / 015261, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways." This national phase application claims priority to U.S. Provisional Patent Application Serial No. 61 / 762,363, filed February 8, 2013, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," both of which are incorporated herein by reference.

[0010] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application Serial No. 16 / 533,028, filed August 6, 2019, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," which is a continuation of U.S. Patent Application No. 10,413,206, filed June 21, 2018, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," which is a continuation of U.S. Patent Application No. 10,413,206, filed February 17, 2017, entitled "Method and Device for Determining and Presenting..." This is a continuation of U.S. Patent Application No. 10,376,171, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed September 25, 2015. U.S. Patent Application No. 10,376,171 is a continuation of U.S. Patent Application No. 9,610,024, filed November 19, 2014, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls." This is a continuation of U.S. Patent Application No. 9,167,982, entitled "Walls (Method and Apparatus for Determining and Presenting Surface Charge and Dipole Density on the Heart Wall)," Application No. 9,167,982.U.S. Patent No. 982 is a continuation of U.S. Patent Application No. 8,918,158, filed February 25, 2014, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls." U.S. Patent Application No. 8,918,158 is a continuation of U.S. Patent Application No. 8,700,119, filed April 8, 2013, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls." U.S. Patent Application No. 8,700,119 is a continuation of U.S. Patent Application No. 8,700,119, filed February 3, 2009, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls." This is a continuation of U.S. Patent Application No. 8,417,313, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed August 3, 2007. U.S. Patent Application No. 8,417,313 is a national phase application of PCT 35 USC 371, filed August 3, 2007, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," PCT / CH2007 / 000380. PCT claims priority to Swiss Patent Application No. 1251 / 06, filed August 3, 2006. Both applications are incorporated herein by reference.

[0011] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 11,116,438, filed September 12, 2019, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall"; U.S. Patent Application No. 11,116,438 is a continuation of U.S. Patent Application No. 10,463,267, filed January 29, 2018, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall"; and U.S. Patent Application No. 10,463,267 is a continuation of U.S. Patent Application No. 10,463,267, filed October 25, 2016, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall". This is a continuation of U.S. Patent Application No. 9,913,589, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed October 19, 2015. U.S. Patent Application No. 9,504,395 is a continuation of U.S. Patent Application No. 9,192,318, filed July 19, 2013, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall."U.S. Patent No. 318 is a continuation of U.S. Patent Application No. 8,512,255, filed July 16, 2010, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall." U.S. Patent Application No. 8,512,255 is a 35 USC 371 national phase application filed January 16, 2009, entitled "A Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," application No. PCT / IB2009 / 000071, under the Patent Cooperation Treaty (PCT). The PCT claims priority to Swiss Patent Application No. 00068 / 08, filed January 17, 2008. Both applications are incorporated herein by reference.

[0012] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application Serial No. 17 / 673,995, filed February 17, 2022, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," which is a continuation of U.S. Patent Application No. 11,278,209, filed April 19, 2019, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," which is a continuation of U.S. Patent Application No. 11,278,209, filed March 20, 2018, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall." This is a continuation of U.S. Patent Application No. 10,314,497, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed August 8, 2017. U.S. Patent Application No. 9,968,268 is a continuation of U.S. Patent Application No. 9,757,044, filed September 6, 2013, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall."U.S. Patent No. 044 is a Patent Cooperation Treaty (PCT) application, filed March 9, 2012, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," application number PCT / US2012 / 028593, under the Patent Cooperation Treaty (PCT) 35 USC 371 national phase application. PCT claims priority to U.S. Provisional Patent Application No. 61 / 451,357, filed March 10, 2011. Both applications are incorporated herein by reference.

[0013] While this application does not claim priority to the following applications, it may relate to: U.S. Design Patent Application No. 29 / 681,827, filed February 28, 2019, entitled "Set of Transducer-Electrode Pairs for a Catheter," which is a divisional application of U.S. Design Patent Application No. 29 / 681,827, filed February 6, 2017, entitled "Set of Transducer-Electrode Pairs for a Catheter," which is a divisional application of U.S. Design Patent Application No. D851,774, filed December 2, 2013, entitled "Transducer-Electrode Pair for a Catheter." The U.S. design patent application D782,686, entitled "Catheter (Transducer-Electrode Pair for Catheter)," is a divisional application of the U.S. design patent application D782,686, filed on August 30, 2013, entitled "Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart," application number PCT / US2013 / 057579, under the Patent Cooperation Treaty (PCT). PCT claims priority to the U.S. provisional patent application filed on August 31, 2012, entitled "System and Method for Diagnosing and Treating Heart Tissue," serial number 61 / 695,535. Both applications are incorporated herein by reference.

[0014] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 16 / 111,538, filed August 24, 2018, entitled "Gas-Elimination Patient Access Device," which is a continuation of U.S. Patent Application No. 10,071,227, filed July 14, 2016, also entitled "Gas-Elimination Patient Access Device," which is a continuation of U.S. Patent Application No. 10,071,227, filed January 14, 2015, entitled "Gas-Elimination Patient Access Device," and filed PCT / US2015 / 011312, under the Patent Cooperation Treaty (PCT 35 USC). The 371 national phase application, which claims priority to U.S. Provisional Patent Application No. 61 / 928,704, filed January 17, 2014, entitled “Gas-Elimination Patient Access Device,” is incorporated herein by reference.

[0015] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 17 / 578,522, filed January 19, 2022, entitled "Cardiac Analysis User Interface System and Method," which is a continuation of U.S. Patent Application No. 11,278,231, filed September 23, 2016, entitled "Cardiac Analysis User Interface System and Method," which is a continuation of U.S. Patent Application No. 11,278,231, filed March 24, 2015, entitled "Cardiac Analysis User Interface System and Method," and filed PCT / US2015 / 022187, which is a 35% USC application of the Treaty of Peaceful Relations and Cooperation in the Hong Kong Special Administrative Region. The 371 National Phase Application, which claims priority to U.S. Provisional Patent Application No. 61 / 970,027, filed March 25, 2014, entitled “Cardiac Analysis User Interface System and Method,” is incorporated herein by reference.

[0016] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application Serial No. 17 / 063,901, filed October 6, 2020, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," which is a continuation of U.S. Patent Application No. 10,828,011, filed March 2, 2016, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," which is a continuation of U.S. Patent Application No. 10,828,011, filed September 10, 2014, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface." This application is a 35 USC 371 national phase application under the Patent Cooperation Treaty (PCT / US2014 / 054942) entitled “Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface,” which claims priority to U.S. Provisional Patent Application No. 61 / 877,617, filed September 13, 2013, entitled “Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface,” which is incorporated herein by reference.

[0017] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 16 / 849,045, filed April 15, 2020, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is a continuation of U.S. Patent Application No. 10,653,318, filed October 26, 2017, also entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is a continuation of U.S. Patent Application No. 10,653,318, filed May 13, 2016, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information." National Phase 35 USC 371 of the Patent Cooperation Treaty (PCT / US2016 / 032420), entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," claims priority to U.S. Provisional Patent Application No. 62 / 161,213, filed May 13, 2015, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is incorporated herein by reference.

[0018] While this application does not claim priority to the following application, it may relate to: U.S. Patent Application No. 15 / 569,231, filed October 25, 2017, entitled “Cardiac Virtualization Test Tank and Testing System and Method,” which is a 35 USC371 national phase application of the Patent Cooperation Treaty (PCT) filed May 11, 2016, with application number PCT / US2016 / 031823, which claims priority to U.S. Provisional Patent Application No. 62 / 160,501, filed May 12, 2015, entitled “Cardiac Virtualization Test Tank and Testing System and Method,” which is incorporated herein by reference.

[0019] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 17 / 735,285, filed May 3, 2022, entitled "Ultrasound Sequencing System and Method," which is a continuation of U.S. Patent Application No. 15 / 569,185, filed October 25, 2017, entitled "Ultrasound Sequencing System and Method," which is a national phase application (35 USC 371) of the Patent Cooperation Treaty (PCT) filed May 12, 2016, with application number PCT / US2016 / 032017, which claims priority to U.S. Patent Application No. 17 / 735,285, filed October 25, 2017, entitled "Ultrasound Sequencing System and Method." Priority is claimed in U.S. Provisional Patent Application No. 62 / 160,529, entitled “Method (Ultrasound Sequencing System and Method),” which is incorporated herein by reference.

[0020] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 17 / 858174, filed July 6, 2022, entitled "Cardiac Mapping System with Efficiency Algorithm," which is a continuation of U.S. Patent Application No. 16 / 097,959, filed October 31, 2018, entitled "Cardiac Mapping System with Efficiency Algorithm," which is a continuation of U.S. Patent Application No. 16 / 097,959, filed May 3, 2017, entitled "Cardiac Mapping System with Efficiency Algorithm," application number PCT / US2017 / 030922, under the Patent Cooperation Treaty (PCT) 35 USC. The Patent Cooperation Treaty (PCT) claims priority to U.S. Provisional Patent Application No. 62 / 413,104, filed October 26, 2016, entitled “Cardiac Mapping System with Efficiency Algorithm,” and U.S. Provisional Patent Application No. 62 / 331,364, filed May 3, 2016, both of which are incorporated herein by reference.

[0021] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 16 / 961,809, filed July 13, 2020, entitled "System for Identifying Cardiac Conduction Patterns," which is a 35 USC 371 national phase application under the Patent Cooperation Treaty (PCT) entitled "System for Identifying Cardiac Conduction Patterns," filed January 22, 2019, entitled PCT / US2019 / 014498, which claims U.S. Provisional Patent Application No. 62 / 619,897, filed January 21, 2018, entitled "System for Recognizing Cardiac Conduction Patterns," and filed May 8, 2018, entitled "System for Identifying Cardiac Conduction." Priority to U.S. Provisional Patent Application No. 62 / 668,647, entitled “Patterns (Systems for Identifying Cardiac Conduction Patterns),” is hereby cited and incorporated herein by reference.

[0022] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 17 / 048,151, filed October 16, 2020, entitled “Cardiac Information Processing System,” which is the 35 USC371 national phase application of Patent Cooperation Treaty (PCT) entitled “Cardiac Information Processing System,” filed May 7, 2019, entitled “Cardiac Information Processing System,” which claims U.S. Provisional Application No. 62 / 668,659, filed May 8, 2018, entitled “Cardiac Information Processing System,” and U.S. Provisional Application No. 62 / 668,659, filed February 28, 2019, entitled “Cardiac Information Processing System.” Priority to U.S. Provisional Patent Application No. 62 / 811,735, entitled “System (Heart Information Processing System),” is hereby cited and incorporated herein by reference.

[0023] While this application does not claim priority to the following applications, it may refer to the Patent Cooperation Treaty (PCT) filed November 8, 2019, entitled "Systems and Methods for Calculating Patient Information," application number PCT / US2019 / 060433, which claims priority to U.S. Provisional Application filed November 9, 2018, entitled "Systems and Methods for Calculating Patient Information," serial number 62 / 757,961, all of which are incorporated herein by reference.

[0024] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 17 / 601,661, filed October 5, 2021, entitled "System for Creating a Composite Map," which is a 35 USC 371 national phase application under the Patent Cooperation Treaty (PCT) entitled "System for Creating a Composite Map," filed April 17, 2020, entitled "System for Creating a Composite Map," application number PCT / US2020 / 028779; the PCT claims U.S. Provisional Application No. 62 / 835,538, filed April 18, 2019, entitled "System for Creating a Composite Map," and U.S. Provisional Application No. 62 / 835,538, filed October 23, 2019, entitled "System for Creating a Composite Map." Priority claims to U.S. Provisional Application No. 62 / 925,030, entitled “Map (System for Creating Composite Maps)”, which are incorporated herein by reference.

[0025] While this application does not claim priority to the following applications, it may refer to: U.S. Patent Application No. 17 / 613,249, filed November 22, 2021, entitled "Systems and Methods For Performing Localization Within A Body," which is a 35 USC 371 national phase application of the Patent Cooperation Treaty (PCT) entitled "Systems and Methods For Performing Localization Within A Body," filed June 4, 2020, entitled "Systems and Methods For Performing Localization Within A Body," application No. PCT / US2020 / 036110, which claims priority to U.S. Provisional Application No. 62 / 857,055, filed June 4, 2019, entitled "Systems and Methods For Performing Localization Within A Body," both of which are incorporated herein by reference.

[0026] While this application does not claim priority to the following applications, it may relate to: U.S. Patent Application No. 17 / 777,104, filed May 16, 2022, entitled "Tissue Treatment Systems, Devices, and Methods," which is a 35 USC 371 national phase application under the Patent Cooperation Treaty (PCT) entitled "Tissue Treatment Systems, Devices, and Methods," filed November 20, 2020, entitled PCT / US2020 / 061458; which in turn claims U.S. Provisional Application No. 62 / 939,412, filed November 22, 2019, entitled "Tissue Treatment Systems, Devices, and Methods," and filed September 7, 2020, entitled "Tissue Treatment Systems, Devices, and Methods." Priority to U.S. Provisional Application No. 63 / 075,280, entitled “Methods (tissue therapy systems, devices, and methods),” which is incorporated herein by reference. Technical Field

[0027] The present invention relates generally to systems, apparatus, and methods for ablating tissue, and particularly for ablating tissue of a patient's heart. Background Technology

[0028] Many medical procedures involve mapping or other diagnostics of tissue, or delivering energy to ablate or otherwise treat tissue. Achieving the desired specificity and efficacy for tissue diagnosis and treatment can be challenging, and failure to do so may result in suboptimal outcomes.

[0029] Systems, methods, and devices are needed to achieve improved tissue therapy by delivering energy. Summary of the Invention

[0030] According to one aspect of the present invention, a dynamic display system for cardiac information includes: one or more electrodes configured to record a dataset of electrical potentials representing cardiac activity at multiple time intervals; and a cardiac information console. The cardiac information console includes a signal processor configured to calculate a set of cardiac activity data at the multiple time intervals using the recorded potential dataset, and the cardiac activity data is associated with surface locations of one or more cardiac chambers. The system also includes a user interface module configured to display information related to the cardiac activity data, presented relative to a graphical representation of the surfaces of the one or more cardiac chambers.

[0031] In some embodiments, the system further includes a dynamic reference routine configured to mitigate the effects of impedance artifacts and / or motion artifacts.

[0032] In some embodiments, the system further includes one or more catheters, each catheter including at least one electrode from one or more electrodes, and the system is configured to position the one or more catheters, and is also configured to determine an optimal fit shape for at least one of the one or more catheters positioned by the system. The system implements a B-spline to determine the optimal fit shape. In some embodiments, the system is configured to implement a virtual location reference, and the virtual location reference includes a location reference anchor and one or more data corrections.

[0033] In some embodiments, the system is configured to perform positioning in a hybrid impedance-based positioning mode. The hybrid impedance-based positioning mode can be configured to perform positioning based on both magnetically based positioning data and impedance-based positioning data.

[0034] In some embodiments, a graphical representation of the surface of one or more cardiac chambers includes an editable digital anatomical model, and the system is configured to generate an editable digital anatomical model.

[0035] In some embodiments, the system is configured to record potential datasets from both contact and non-contact sources.

[0036] In some embodiments, information related to cardiac activity data includes one or more trigger locations. These trigger locations may include locations where conduction velocities are slower than those in healthy heart tissue.

[0037] In some embodiments, the system is also configured to determine various conduction properties. Activation data can be processed to quantify different conduction properties.

[0038] In some embodiments, the system is also configured to perform spatiotemporal analysis. The system can be configured to perform various forms of spatiotemporal analysis.

[0039] In some embodiments, the system is also configured to analyze the spatiotemporal sequence of activation by visualizing the activation region of a time reference.

[0040] In some embodiments, the system is also configured to analyze the spatiotemporal sequence of activation via visualization of the activation region of an amplitude reference.

[0041] In some embodiments, the system is also configured to perform cardiac information analysis, including data aggregation and statistical analysis.

[0042] In some embodiments, the system is also configured to perform one or more cardiac activation analyses. The system can be configured to perform one or more cardiac activation analyses while performing a fusion of clinical measurements and / or computational modeling.

[0043] The techniques described herein, along with their properties and accompanying advantages, will be best recognized and understood in light of the following detailed description taken in conjunction with the accompanying drawings, in which representative embodiments are described by way of example.

[0044] By incorporating via reference All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the extent that each individual publication, patent, or patent application is expressly and individually indicated to be incorporated herein by reference in its entirety. For all purposes, the contents of all publications, patents, and patent applications mentioned in this specification are incorporated herein by reference in their entirety. Attached Figure Description

[0045] Figure 1 A schematic diagram of an embodiment of a system consistent with the concept of the present invention is shown, which is configured to perform medical procedures on a patient.

[0046] Figure 2A -F shows a side view of various embodiments of mapping catheter arrays consistent with the concept of this invention.

[0047] Figures 3A-3E A graph of data recorded by a cardiac mapping system consistent with the concept of this invention is shown.

[0048] Figure 4 A 3D diagram showing the recorded electrode positions is presented, consistent with the concept of this invention.

[0049] Figure 5 A voxel grid with positioning electrode locations is shown, consistent with the concept of this invention.

[0050] Figure 6 A model of an expanded basket catheter consistent with the concept of this invention is shown.

[0051] Figures 7-11 Block diagrams of various modules of a catheter navigation system consistent with the concept of this invention and screenshots of the GUI of the catheter navigation system are shown.

[0052] Figure 12 A schematic diagram of an algorithm for generating a digital model of a patient's anatomical structure, consistent with the concept of this invention, is shown.

[0053] Figure 13 A schematic diagram of a set of steps for a method of converting a first anatomical model surface type to a second anatomical model surface type, consistent with the concept of the present invention, is shown.

[0054] Figure 13A and Figure 13B A 3D “internal polyhedron” method for classifying dense volumetric data is shown, consistent with the concept of this invention.

[0055] Figure 14 A user interface for managing one or more chambers of a patient's heart, consistent with the concept of this invention, is shown.

[0056] Figures 15A-15D A schematic diagram illustrating an example user interaction with an anatomical model, consistent with the concept of this invention, is shown.

[0057] Figure 16 An example of domain delineation consistent with the concept of this invention is shown.

[0058] Figure 17 A rendering of a portion of a cardiac mapping system consistent with the concept of this invention is shown, highlighting a portion of the graphical user interface.

[0059] Figures 18A-18D and Figure 19 Various mapping plots and graphs related to cardiac activity data recorded by a cardiac mapping system are shown, consistent with the concept of this invention.

[0060] Figure 20A and Figure 20B Schematic and illustrative examples of the processes for deriving cardiac data, namely reduction maps and refraction maps, consistent with the concept of this invention, are shown respectively.

[0061] Figure 21 Screenshots of a GUI provided by a cardiac mapping system, consistent with the concept of this invention, are shown. Detailed Implementation

[0062] Reference will now be made in detail to embodiments of the present technology, examples of which are illustrated in the accompanying drawings. Similar reference numerals may be used to refer to similar components. However, this specification is not intended to limit the disclosure to the specific embodiments, and it should be construed as including various modifications, equivalents, and / or substitutions to the embodiments described herein.

[0063] It will be understood that, when used herein, the words “comprise” (and any form of “comprise”, such as “comprise” and “comprises”), “have” (and any form of “have”, such as “have” and “has”), “include” (and any form of “includes”, such as “includes” and “include”), or “contain” (and any form of “contains”, such as “contains” and “contain”) specify the presence of the said feature, integer, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0064] It should also be understood that although the terms first, second, third, etc., may be used herein to describe various limitations, elements, components, regions, layers, and / or portions, these limitations, elements, components, regions, layers, and / or portions should not be limited by these terms. These terms are used only to distinguish one limitation, element, component, region, layer, or portion from another limitation, element, component, region, layer, or portion. Therefore, without departing from the teachings of this application, the first limitation, element, component, region, layer, or portion discussed below may be referred to as a second limitation, element, component, region, layer, or portion.

[0065] It should also be understood that when an element is described as being "on" another element, "attached to," "connected to," or "coupled to" another element, it may be directly on or above the other element, directly connected to or coupled to the other element, or there may be one or more intermediate elements. Conversely, when an element is described as being "directly on" another element, "directly attached to," "directly connected to," or "directly coupled to" another element, there are no intermediate elements. Other terms used to describe relationships between elements should be interpreted in a similar manner (e.g., "between" and "directly between," "adjacent" and "directly adjacent," etc.).

[0066] It should also be understood that when the first element is referred to as being “in”, “on”, and / or “within” the second element, the first element can be positioned: within the internal space of the second element; within a portion of the second element (e.g., within the wall of the second element); positioned on the outer and / or inner surface of the second element; and one or more combinations thereof.

[0067] As used herein, when used to describe the proximity of a first component or first location to a second component or second location, the term "proximity" will be considered to include: one or more locations close to the second component or second location, and locations within, above, and / or within the second component or second location. For example, a component located close to an anatomical site (e.g., a target tissue location) should include: a component located close to the anatomical site, and a component located within, above, and / or within the anatomical site.

[0068] For example, as shown in the figures, spatially relative terms such as “below,” “lower,” “below,” “above,” and “upper” can be used to describe the characteristic relationship of an element and / or to another element and / or feature. It should also be understood that spatially relative terms are intended to include different orientations of the device in use and / or operation besides those depicted in the figures. For example, if the device in the figures is flipped, an element described as “below” and / or “below” other elements or features would be oriented as “above” other elements or features. The device may be oriented in other ways (e.g., rotated 90 degrees or in other orientations), and the spatially relative descriptions used herein shall be interpreted accordingly.

[0069] The terms “reduce,” “reduction,” “reduction,” etc., used in this document will include reduction of quantity, including reduction to zero. The possibility of reduction occurring should include prevention of occurrence. Accordingly, the terms “prevent,” “stop,” and “avoid” should include the actions of “reduce,” “reduction,” and “reduction,” respectively.

[0070] As used herein, the term “and / or” should be understood as a specific disclosure of each of two specified features or components with or without the other. For example, “A and / or B” should be understood as a specific disclosure of (i) A, (ii) B, and (iii) each of A and B, as if each were stated separately herein.

[0071] As used herein, the term "one or more" can refer to one, two, three, four, five, six, seven, eight, nine, ten or more, up to any number.

[0072] The terms “and combinations thereof” and “and combinations thereof” may be used individually or collectively after a list of included items. For example, a set of one or more components, processes and / or other items selected from A, B, C and combinations thereof will include: one, two, three or more items in item A; one, two, three or more items in item B; and / or one, two, three or more items in item C.

[0073] In this specification, unless otherwise expressly stated, “and” can mean “or” and “or” can mean “and”. For example, if a feature is described as having A, B, or C, then the feature can have A, B, and C, or any combination of A, B, and C. Similarly, if a feature is described as having A, B, and C, then the feature can have only one or two of A, B, or C.

[0074] As used herein, when a quantifiable parameter is described as having a value between a first value X and a second value Y, it will include parameters having the following values: at least X, not greater than Y, and / or at least X and not greater than Y. For example, a length between 1 and 10 should include a length of at least 1 (including values ​​greater than 10), a length of less than 10 (including values ​​less than 1), and / or a value greater than 1 and less than 10.

[0075] The term "configuration (or setting)" as used in this disclosure may be used interchangeably with, for example, terms such as "applicable to," "capable of," "designed to," "adapted to," "made of," and "capable of." The term "configuration (or setting)" does not necessarily mean "specifically designed" in hardware. Alternatively, in some cases, the term "device is configured to" may mean that the device "can" operate with another device or component.

[0076] As used herein, the term "threshold" refers to a maximum level, minimum level, and / or range of a value associated with a desired or undesirable state. In some embodiments, system parameters are maintained above a minimum threshold, below a maximum threshold, within a threshold range of values, and / or outside a threshold range of values, such as to induce a desired effect (e.g., effective treatment) and / or to prevent or otherwise reduce (hereinafter referred to as "prevent") undesirable events (e.g., device and / or clinical adverse events). In some embodiments, system parameters are maintained above a first threshold (e.g., above a first temperature threshold to induce a desired therapeutic effect on tissue) and below a second threshold (e.g., below a second temperature threshold to prevent undesirable tissue damage). In some embodiments, the threshold is determined to include a safety margin to account for patient variability, system variability, tolerability, etc. As used herein, "exceeding a threshold" refers to a parameter exceeding a maximum threshold, falling below a minimum threshold, within a threshold range, and / or outside a threshold range.

[0077] As described herein, “indoor pressure” should refer to the pressure of the environment surrounding the systems and devices of this invention. Positive pressure includes pressures above indoor pressure or simply greater than another pressure, such as positive differential pressure across fluid path components (such as valves). Negative pressure includes pressures below indoor pressure or less than another pressure, such as negative differential pressure across fluid path components (such as valves). Negative pressure may include vacuum, but does not mean pressure below vacuum. As used herein, the term “vacuum” may be used to refer to a complete or partial vacuum, or any negative pressure as described above.

[0078] The term “diameter” used in this document to describe non-circular geometries is considered to be the diameter of an imaginary circle that approximates the geometry being described. For example, when describing a cross-section (such as the cross-section of a component), the term “diameter” should be considered to refer to the diameter of an imaginary circle having the same cross-sectional area as the cross-section of the component being described.

[0079] The terms “major axis” and “minor axis” used in this article refer to the length and diameter, respectively, of the smallest imaginary cylinder that can completely enclose the component.

[0080] As used herein, the term "functional element" is considered to include one or more elements constructed and arranged to perform a function. A functional element may include sensors and / or transducers. In some embodiments, a functional element is configured to deliver energy and / or otherwise treat tissue (e.g., a functional element configured as a therapeutic element). Alternatively or additionally, a functional element (e.g., a functional element including sensors) may be configured to record one or more parameters, such as patient physiological parameters, patient anatomical parameters (e.g., tissue geometry parameters), patient environmental parameters, and / or system parameters. In some embodiments, sensors or other functional elements are configured to perform diagnostic functions (e.g., collecting data for diagnostic purposes). In some embodiments, a functional element is configured to perform therapeutic functions (e.g., delivering therapeutic energy and / or therapeutic agents). In some embodiments, a functional element includes one or more elements constructed and arranged to perform a function selected from: delivering energy, extracting energy (e.g., to cool components), delivering drugs or other agents, manipulating system components or patient tissue, recording or otherwise sensing parameters such as patient physiological parameters or system parameters, and combinations of one or more of these. A functional element may include fluids and / or fluid delivery systems. Functional elements may include reservoirs, such as inflatable airbags or other fluid-retaining reservoirs. A "functional component" may include components constructed and arranged to perform functions, such as diagnostic and / or therapeutic functions. A functional component may include scalable components. A functional component may include one or more functional elements.

[0081] As used herein, the term "transducer" is considered to include any component or combination of components that receives energy or any input and produces an output. For example, a transducer may include electrodes that receive electrical energy and (e.g., based on electrode size) distribute that electrical energy to tissue. In some configurations, a transducer converts an electrical signal into any output, such as light (e.g., a transducer comprising a light-emitting diode or light bulb), sound (e.g., a transducer comprising a piezoelectric crystal configured to deliver ultrasonic energy), pressure (e.g., applied pressure or force), heat, cryogenic energy, chemical energy, mechanical energy (e.g., a transducer comprising a motor or solenoid), magnetic energy, and / or different electrical signals (e.g., input signals different from those of the transducer). Alternatively or additionally, a transducer may convert a physical quantity (e.g., a change in a physical quantity) into an electrical signal. A transducer may include any component that delivers energy and / or agents to a tissue, such as a transducer configured to deliver one or more of the following: electrical energy to the tissue (e.g., the transducer includes one or more electrodes), optical energy to the tissue (e.g., the transducer includes a laser, a light-emitting diode, and / or an optical component such as a lens or prism), mechanical energy to the tissue (e.g., the transducer includes a tissue manipulation element), acoustic energy to the tissue (e.g., the transducer includes a piezoelectric crystal), chemical energy, electromagnetic energy, magnetic energy, and combinations thereof.

[0082] As used herein, the term "fluid" can refer to a liquid, gas, gel, or any flowable material, such as a material that can be propelled through cavities and / or openings.

[0083] As used herein, the term "material" can refer to a single material or a combination of two, three, four or more materials.

[0084] It should be understood that, for clarity, certain features of the inventive concept described in the context of a single embodiment may also be provided in combination in a single embodiment. Conversely, for brevity, various features of the inventive concept described in the context of a single embodiment may also be provided individually or in any suitable sub-combination. For example, it should be understood that all features set forth in any claim (whether independent or dependent) may be combined in any given manner.

[0085] It should be understood that at least some of the figures and descriptions of the inventive concept have been simplified to focus on elements relevant to a clear understanding of the inventive concept, while other elements that may also be part of the inventive concept and would be recognized by those skilled in the art for clarity have been omitted. However, because such elements are well known in the art and because they do not necessarily contribute to a better understanding of the inventive concept, descriptions of such elements are not provided herein.

[0086] The terminology defined in this disclosure is used only to describe specific embodiments of the disclosure and is not intended to limit the scope of the disclosure. Unless the context clearly indicates otherwise, terms provided in the singular are also intended to include the plural forms. Unless otherwise defined herein, all terms used herein (including technical or scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. Unless expressly defined herein, terms defined in generally used dictionaries should be interpreted as having the same or similar meaning as in the context of the related art and should not be interpreted as having an ideal or exaggerated meaning. In some cases, the terms defined in this disclosure should not be construed as excluding embodiments of the disclosure.

[0087] This document provides systems, devices, and methods for treating target tissues in patients, such as providing therapeutic benefits to patients. An energy delivery console can be configured to deliver various “dose” of energy delivered by one or more energy delivery devices to ablate target tissue, induce necrosis of target tissue, and / or otherwise therapeutically alter target tissue. One or more energy delivery devices may include catheters and / or surgical instruments comprising electrodes and / or other energy delivery elements. In some embodiments, multiple interdependent energy doses are delivered to a common tissue location, such as providing improved therapeutic benefits to patients. An initial dose may be configured to warm the tissue, such as through the delivery of radio frequency (RF), heat, and / or other energy. Subsequent doses may include energy doses configured to irreversibly electroporate previously warmed tissue, for example, when the tissue is in an elevated temperature state (e.g., above body temperature).

[0088] See now Figure 1This illustration shows a schematic diagram of an embodiment of a system configured to perform medical procedures on a patient (e.g., a human or other living mammal) in accordance with the inventive concept. The medical procedure may include diagnostic procedures, therapeutic procedures, or a combination of diagnostic and therapeutic procedures that can be performed by a clinician and / or other user (hereinafter referred to as an "operator" or "user"). System 10 includes a console 100, which includes one or more discrete components (e.g., separate housings) connected to various components of the system to provide power thereto, record information from System 10 as described herein, and / or otherwise implement one or more functions of System 10 as described herein. System 10 also includes one or more diagnostic catheters, such as mapping catheter 200 shown. In some embodiments, System 10 includes: one or more therapeutic catheters, therapeutic catheter 310; one or more functional catheters, functional catheter 320; one or more additional diagnostic catheters, diagnostic catheter 330; one or more patient patches, patch 340; one or more patient leads, EKG (electrocardiogram) leads 350; and / or one or more delivery devices, sheath 360. The console 100 is operably attached (e.g., electrical, mechanical, fluid, acoustic, and / or optical) to one or more conduits or other devices 200, 310, 320, 330, 340, 350, and / or 360.

[0089] The mapping catheter 200 may include an array of elements (array 210, handle 202) and elongated filaments (shaft 201) between them. Array 210 may include a radially expandable array, such as an array elastically biased in a radially expandable geometry. In some embodiments, array 210 may include a plurality of radially expandable arms (splines 213). Alternatively or additionally, array 210 may include an airbag, a radially expandable cage, or other expandable structure. Array 210 may include one or more functional elements, such as one or more electrodes (electrodes 211), one or more ultrasonic transducers (UST (ultrasound transducer) 212), and / or one or more other functional elements (functional elements 219).

[0090] The mapping catheter 200 may have a similar construction and arrangement to similar components described in the following co-pending applications filed by the applicant: U.S. Patent Application No. 16 / 861,814, filed April 29, 2020, entitled “Catheter System and Methods of Medical Uses of Same, including Diagnostic and Treatment Uses for the Heart”; U.S. Patent Application No. 17 / 887,779, filed August 15, 2022, entitled “Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways”; and U.S. Patent Application No. 17 / 735,285, filed May 3, 2022, entitled “Ultrasound Sequencing System and Method”.

[0091] The treatment catheter 310 may include an elongated filament (shaft 311) with a handle 312 at its proximal end. The treatment catheter 310 may include one or more functional elements (functional elements 319) positioned on the distal portion of the shaft 311 (e.g., at least one functional element 319 positioned on the distal end of the shaft 311). In some embodiments, the functional element 319 includes one or more electrodes configured to deliver electrical energy (e.g., RF energy) to tissue, such as by thermally ablating the tissue. Alternatively or additionally, the functional element 319 may include one or more electrodes configured to generate an electric field therebetween, such as to perform electroporation (e.g., irreversible electroporation) of the tissue within that field.

[0092] The treatment catheter 310 may have a similar construction and arrangement to similar components described in the following co-pending applications filed by the applicant: U.S. Application No. 16 / 335,893, filed March 22, 2019, entitled "Ablation System with Force Control"; U.S. Application No. 17 / 777,104, filed May 16, 2022, entitled "Tissue Treatment Systems, Devices, and Methods"; and U.S. Patent Application No. 18 / 275,501, filed April 11, 2024, entitled "Energy Delivery Systems with Ablation Index".

[0093] The functional catheter 320 may include an elongated filament (shaft 321) with a handle 322 at its proximal end. The functional catheter 320 may include one or more functional elements (functional elements 329) positioned on the distal portion of the shaft 321 (e.g., an array of at least 10 functional elements 329 positioned on the distal portion of the shaft 321, such as the 13 elements shown). In some embodiments, the functional element 329 includes one or more electrodes, such as one or more electrodes configured to record biopotential signals and / or other electrical signals from cardiac tissue.

[0094] The diagnostic catheter 330 may include a catheter comprising one or more functional elements (functional elements 339). In some embodiments, the diagnostic catheter 330 includes a coronary sinus (CS) mapping catheter constructed and positioned within the CS of the heart (e.g., to position the functional element 339 within the CS). The functional element 339 may include one or more electrodes configured to record biopotential signals and / or other electrical signals from cardiac tissue.

[0095] Patch 340 may include one or more patches configured for application to a patient's skin (e.g., on the patient's torso). Patch 340 may include one or more functional elements (functional elements 349). Functional element 349 may include electrodes configured to generate an electric field within the patient (e.g., an electric field generated between at least two patches 340). System 10 may be configured to position one or more of its devices within and / or on the patient by measuring the electric field generated between the patches 340 (e.g., via impedance-based positioning as described herein).

[0096] EKG lead 350 may include one or more patient patches configured to record electrical signals (e.g., cardiac electrical signals) from the patient. Multiple EKG lead 350s may be positioned around the patient's torso as shown.

[0097] The sheath 360 may include an elongated tube (shaft 361) having at least one lumen (lumen 363) extending through it. The sheath 360 may include at least one functional element, such as the illustrated functional element 369. The sheath 360 may be constructed and arranged to advance within a blood vessel into a chamber of the heart (e.g., into the left atrium of the heart via a transatrial septal puncture). The lumen 363 of the sheath 360 may slidably receive one or more devices of the system 10, such as the distal portion of the mapping catheter 200 (e.g., when the array 210 is in a radially collapsed geometry), such that the device may advance from the distal end of the lumen 363 into a chamber of the heart. For example, the array 210 of the mapping catheter 200 may advance through the lumen 363 of the sheath 360 in a radially collapsed geometry, exit the lumen 363 into the left atrium of the heart, and transition to a radially expanding geometry. In some embodiments, lumen 363 includes two or more lumens configured to slidably receive devices of system 10, and / or lumen 363 is constructed and arranged to receive multiple devices simultaneously, such as allowing multiple devices (e.g., mapping catheter 200, treatment catheter 310, and / or functional catheter 320) to be inserted into the left atrium via a single interatrial septal puncture.

[0098] As used herein, devices 310, 320, 330, 340, 350 and / or 360 may be referred to individually or collectively as patient device 300.

[0099] The console 100 may include a patient interface module 101 configured to operatively attach one or more patient devices (e.g., one or more catheters or other devices described herein) to one or more components of the console 100. The patient interface module 101 may include circuitry configured to protect the patient (e.g., from unwanted electric shocks caused by the console 100) and / or protect components of the console 100 from electric shocks (e.g., from electric shocks caused by defibrillation pulses or other energy delivered to the patient).

[0100] Console 100 may include a processing unit 110. Processing unit 110 may include at least one microprocessor, computer, and / or another electronic controller (processor 111). Processing unit 110 may also include one, two, or more algorithms, such as algorithm 115 shown. Processing unit 110 may include a memory 112 for storing instructions for executing algorithm 115. Processor 111 may execute one or more of the processes described herein via algorithm 115, such as processes executed in response to one or more commands input by a user into system 10 (e.g., via user interface 120 described herein). Processing unit 110 may receive signals, such as signals from one, two, or more functional elements of devices 200 and / or 300 (e.g., signals from one, two, or more sensor-based functional elements of these devices). Processing unit 110 may be configured to perform one or more mathematical operations based on the received signals and produce results related to the patient's physiological parameters and / or operational parameters associated with at least one device of system 10.

[0101] Console 100 may include an interface (user interface 120) for providing information to and / or receiving information from the user of system 10. User interface 120 may include one, two, or more user input and / or user output components. For example, user interface 120 may include a joystick, keyboard, mouse, microphone, touchscreen, and / or other input devices. Additionally or alternatively, user interface 120 may include a speaker, haptic feedback device, indicator lights, and / or other output devices. In some embodiments, user interface 120 includes one or more displays, such as a touchscreen or other display for providing graphical visual information to the user. Processing unit 110 may provide a graphical user interface (GUI) 125 to be presented to the user via user interface 120.

[0102] System 10 may include one or more modules for generating output signals (e.g., signals to be delivered to devices 200 and / or 300), receiving data (e.g., one or more recorded signals from devices 200 and / or 300), processing the received data (e.g., via algorithm 115), and / or generating output data based at least in part on the processed data. For example, system 10 may include a biopotential module 130, a localization module 140, an anatomy module 150, an imaging module 160, a mapping module 170, and / or a treatment module 180.

[0103] The biopotential module 130 can generate one or more outputs related to the patient's electrical activity, such as dipole density information, surface charge information, and / or voltage information related to the activity of the patient's heart. The biopotential module 130 may have a similar construction and arrangement to similar components described in the following applications filed by the applicant: U.S. Patent No. 11,013,444, filed August 6, 2019, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls"; U.S. Patent No. 11,116,438, filed September 12, 2019, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall"; and U.S. Patent No. 11,116,438, filed February 28, 2019, entitled "Set of Transducer-Electrode Pairs for a U.S. design patent application D954,970 entitled "Cathode-Electrode Pair Set for Catheter"; and co-pending U.S. patent application Serial No. 17 / 858,174 entitled "Cardiac Mapping System with Efficiency Algorithm" filed on July 6, 2022.

[0104] The positioning module 140 can generate one or more outputs related to the position of one or more components of the system 10 relative to the patient P (such as relative to a coordinate system established by the positioning module 140). The positioning module 140 may have a similar construction and arrangement to similar components described in the following co-pending application of the applicant: U.S. Patent Application Serial No. 16 / 849,045, filed April 15, 2020, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information".

[0105] Anatomy module 150 can generate one or more outputs related to the anatomy of patient P, such as the size, shape, and / or structure of at least a portion (e.g., a chamber) of patient P's heart H. Anatomy module 150 may have a similar construction and arrangement to similar components described in the following applications of the applicant: U.S. Design Patent Application No. D954970, filed June 14, 2022, entitled "Set of Transducer-Electrode Pairs for a Catheter"; and U.S. Patent Application Serial No. 17 / 735,285, filed May 3, 2022, entitled "Ultrasound Sequencing System and Method".

[0106] Imaging module 160 can provide, generate, acquire, update, store, and maintain at least one image of the heart or at least one chamber of the heart H. Imaging module 160 may include at least one imaging device configured to record image data. For example, imaging module 160 may include imaging devices selected from: computed tomography (CT) scanners; fluorescence microscopes; X-ray imagers; magnetic resonance imaging (MRI) scanners; ultrasound imagers; and combinations thereof. In some embodiments, imaging module 160 receives and / or stores image information from imaging devices. Alternatively or additionally, imaging module 160 may be configured to receive image data from imaging devices separate from system 10. In some embodiments, imaging module 160 and anatomy module 150 are configured to provide, generate, and / or update anatomical models, such as anatomical models of at least a portion of a patient's heart, based on image data from imaging devices.

[0107] Mapping module 170 can receive cardiac activity information (e.g., information recorded from device 200 and / or 300 by biopotential module 130) and generate one or more mappings of cardiac electrical activity. For example, mapping module 170 can generate one or more mappings of dipole density, surface charge, and / or voltage based on the recorded cardiac electrical activity. Mapping module 170 can have a similar construction and arrangement to similar components described in the following co-pending applications: U.S. Patent Application No. 17 / 673,995, filed February 17, 2022, entitled “Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall”; and U.S. Patent Application No. 16 / 097,955, filed October 31, 2018, entitled “Cardiac Information Dynamic Display System and Method”.

[0108] The treatment module 180 may be configured as a device (e.g., treatment catheter 310) to initiate or drive the system 10 for delivering therapeutic energy to one or more locations on the patient's heart. In some embodiments, the treatment module 180 provides closed-loop energy delivery based on mapping and / or other information generated by the system 10. Treatment module 180 may have a similar construction and arrangement to similar components described in the following co-pending applications filed by the applicant: U.S. Patent Application No. 16 / 861,814, filed April 29, 2020, entitled “Catheter System and Methods of Medical Uses of Same, including Diagnostic and Treatment Uses for the Heart”; U.S. Patent Application No. 16 / 335,893, filed June 1, 2023, entitled “Ablation System with Force Control”; and U.S. Patent Application No. 17 / 777,104, filed May 16, 2022, entitled “Tissue Treatment Systems, Devices, and Methods”.

[0109] In some embodiments, system 10 is configured to generate one or more mappings of cardiac activity (e.g., via mapping module 170) based on information collected in a non-contact manner (e.g., information recorded from one or more electrodes positioned within the heart chambers without contacting the heart wall, referred to herein as "non-contact data"). Alternatively or additionally, system 10 may be configured to generate one or more mappings of cardiac activity based on information collected in a contact manner (e.g., information recorded from one or more electrodes positioned to contact the heart wall, referred to herein as "contact data"). In some embodiments, system 10 is configured to generate one or more "hybrid" mappings of cardiac activity based on both contact data and non-contact data.

[0110] The positioning module 140 may provide signals to one or more devices of the system 10 and / or record signals from one or more devices of the system 10. For example, the positioning module 140 may provide one or more signals to the patch 340, such as to establish an electric field within the patient. The positioning module 140 may record signals from one or more devices of the system 10 related to the established electric field to determine the position and / or orientation of the device within the field (to “position” the device) via an impedance-based method. Alternatively or additionally, such as when the positioning module 140 is constructed and arranged to establish a magnetic field within at least a portion of the patient (e.g., via one or more permanent magnets and / or electromagnets), the positioning module 140 may position one or more devices of the system 10 via a magnetic method, wherein one or more functional elements of the system 10 include magnetic coils or other elements configured to detect the magnetic field.

[0111] In some embodiments, the positioning module 140 is configured to position one or more devices of the system 10 that have been placed in a static position to have minimal further intentional movement (e.g., operator movement), such as a diagnostic catheter 330 when placed within a CS. The positioning module 140 may be configured to track the position of this static device and to use this device as a “physical reference” (e.g., a locatable physical point within the patient that cannot be moved relative to the heart, as described below). In some embodiments, the positioning module 140 may position one or more other devices of the system 10 (e.g., any or all other devices present within the patient, such as those located within or near the heart) at least in part based on the physical reference’s relative position to an additional device being positioned.

[0112] System 10 can be configured to navigate (e.g., provide navigation information and / or auto-navigate) one or more conduits and / or other devices of system 10. System 10 can perform device navigation (e.g., via positioning module 140) using impedance measurements recorded by system 10, as described herein. System 10 can perform impedance measurements in many different ways. In some embodiments, system 10 uses multiple pairs (e.g., at least three pairs) of patch sets (e.g., three pairs of patches 340) to deliver current through the body. The three pairs of patches can deliver unique frequencies (e.g., simultaneously). Sensors (e.g., electrodes) within the body can measure the potential at each of three unique positioning frequencies relative to reference measurements taken elsewhere on the body (e.g., patches on low body surfaces on the torso).

[0113] In some embodiments, system 10 may perform one or more field characterizations (e.g., characterizing electric fields within the body). For example, system 10 may implement one or more nonlinear functions (e.g., algorithm 115 includes one or more nonlinear functions) to characterize the mapping of system 10's device to a three-dimensional location (e.g., a three-dimensional location within the body). Alternatively or additionally, system 10 may implement one or more linear functions, each including one or more linear terms. In some embodiments, one or more functions of system 10 may include both linear and nonlinear terms, for example, taking into account changes in the electric field (e.g., changes caused by changes in patient anatomy (such as changes between patients), and / or changes caused by anatomical movements (such as movements caused by respiration)). In some embodiments, the functions of system 10 configured to characterize mappings include B-spline functions, radial basis functions, and / or piecewise linear functions.

[0114] System 10 can be configured to perform impedance-based device navigation, such as by using an "adaptive reference," while reducing the effects of motion artifacts and / or other artifacts. In some embodiments, such as when positioned in a location where minimal further movement is expected (e.g., minimal manipulation by the operator is expected, such as when placed in a coronary sinus or other intracardiac location where further manipulation is not required and / or is expected), System 10 tracks a designated set of electrodes (e.g., one or more catheters and / or other devices from System 10) that may (e.g., are highly likely to) be placed in a "static position." System 10 can be configured to track those electrode positions and use them as a "physical reference." For example, System 10 can track other devices (e.g., catheters to be manipulated) by using the physical reference as an "anchor" (e.g., by subtracting the real-time position of the physical reference). System 10 can be configured to compensate for physical displacement of any electrode used as a physical reference, where such displacement causes a shift in the coordinate system. In some embodiments, system 10 uses a dynamic adaptive learning model to train a set of measurements from surface sensors (e.g., patch 340) to create a virtual intracardiac measurement set that quantitatively reconstructs signals equivalent to physical devices (“virtual references” herein). For example, system 10 can track other devices (e.g., catheters) by using the virtual references as anchors (e.g., by subtracting the real-time position of the virtual references). These virtual references of system 10 are not susceptible to the types of physical motion that may be encountered when using devices inserted into a patient’s body (e.g., intracardiac catheters as described above), but they are susceptible to interference associated with the surface sensors themselves, positioning measurement references (e.g., reference patch electrodes), and / or to electrical interference introduced into a single device (e.g., a single catheter), multiple devices (e.g., multiple catheters), and / or the entire system (e.g., associated with a single device, multiple devices, and / or the entire system). In some embodiments, system 10 may be configured to store (e.g., stored in memory) the temporal history of all tracked locations (e.g., device locations, physical reference locations, and / or virtual reference locations), such as for recalling (e.g., and using) the previous location of any device (e.g., for positioning and / or positioning compensation of one or more devices of system 10). This stored information can be used to detect differences between systemic interference and interference specific to a single device (e.g., a single conduit). For example, electrical interference can affect a single device, multiple devices, and / or the entire system, and device movement will be unique for each individual device. Each of these methods can be calculated simultaneously and / or sequentially.

[0115] In some embodiments, system 10 is configured to perform an impedance-based device (e.g., conduit) navigation method using a "position agreement algorithm" (e.g., the algorithm of algorithm 115) configured to resolve motion artifacts and / or other measurement artifacts. If system 10 utilizes more than one device tracking method (e.g., two or more of those methods described above), system 10 may implement the position agreement algorithm to cross-compare the solutions of each of the multiple methods to determine one or more subsets of "consistent" solutions. Alternatively or additionally, system 10's position agreement algorithm may determine one or more subsets of solutions indicating aberrations. The position agreement algorithm may, based on the determined consistency and / or the finding of aberration indications, cause system 10 to prioritize and / or de-prioritize (e.g., ignore) one or more of these methods. In some embodiments, prioritization and / or de-prioritization can be performed by assigning weighting factors (e.g., quantitative weighting factors) to each of these positioning methods. In some embodiments, the position agreement algorithm may specify when de-prioritized methods (e.g., methods that are not currently used) will be restored (e.g., reused). Location consistency algorithms can use a predefined set of logical rules to determine the root cause of potential disturbances, and then make appropriate adjustments to mitigate them. If the physical reference, virtual reference, and the time history of the location all fall within a specified distance (e.g., a geofence), system 10 can determine that all tracking methods are consistent. However, if the physical reference and virtual reference are consistent, but the most recently stored location in the time history is inconsistent, a sudden electrical disturbance may affect the location of at least a specified reference device. Comparing the location of other devices (e.g., catheters) within the body to their time history locations can help distinguish catheter-specific disturbances from system-wide disturbances. The overall quantitative impact of disturbances on location can be mitigated by correcting the locations of the tracked physical and virtual references to align with the most recently valid time history location. For example, if the physical reference and the time history of the physical reference are consistent, but the virtual reference is inconsistent, the virtual reference may be anomalous and can be ignored by system 10 (e.g., until it recovers consistency with the physical reference and the time history of the physical reference, after which it can be recovered).

[0116] In some embodiments, system 10 is configured to perform an impedance-based device (e.g., catheter) navigation method using a "position determination algorithm" (e.g., the algorithm of algorithm 115). The "position determination algorithm" is configured to utilize the results of the aforementioned tracking method and position consistency algorithm to determine (e.g., and display) the positions of one or more (e.g., all) of the devices in use by system 10 (e.g., the positions of one or more electrodes and / or other devices of one or more catheters located within and / or on the patient). In some embodiments, the position determination algorithm is configured to generate a "probability score" including confidence and / or other probability measurements (e.g., quantitative output) regarding the determined position of one or more devices. The probability score may be displayed to the user via the displayed alphanumeric value and / or by modifying (e.g., enhancing) the way the displayed devices are shown (e.g., hash marking, color changes, brightness changes, and / or other visual differences in the body and / or another part of the associated catheter and / or other devices). In some embodiments, system 10 includes various thresholds (e.g., quantitative thresholds) used by algorithm 115 to classify the predicted position of the devices by comparing the probability score to one or more thresholds.

[0117] In some embodiments, system 10 includes a “dynamic reference routine” configured to mitigate the effects of impedance artifacts and / or motion artifacts. System 10 may monitor the distance between the “active” position of the “position reference catheter” (PRC) (e.g., the current position determined by positioning the PRC) and the recorded position of the PRC, an “anchoring position” such as the “position reference anchoring” (PRA) position, like the coronary sinus, when the PRC is in a relatively stable position. System 10 may be configured to determine the “drift” of the recorded real-time position of the PRC from the PRC anchoring position recorded in the PRA (e.g., when the PCR has not moved, but the recorded real-time position indicates displacement). Drift may include slow, stable displacement. System 10 may also be configured to determine the “offset” of the recorded real-time position of the PRC from the anchoring position, where offset includes rapid displacement. In some embodiments, system 10 periodically performs an “anchoring event” where the anchoring position is updated by recording the position of the PRC at the PRA position. If it can be assumed that the PRC has not moved since the anchoring event, system 10 may correct for drift and offset. For example, regarding drift, such as temporal drift, system 10 can register the PRC to the PRA to correct for differences between the PRC and PRA positions. Following this registration, system 10 can implement a "virtual position reference" (VPR), where the virtual reference position is the PRA position to which any corrections have been applied. In another example, for shifting, system 10 can switch to a "physical position reference" (PPR), where the PRA position is set to match the recorded PRC position. When the offset no longer exists, system 10 can correct any recorded drift, and the VPR can be restored.

[0118] In some embodiments, system 10 includes a "data quality routine" configured to evaluate the data quality of one or more connected measurement devices from system 10. The data quality routine may be configured to determine the "state" of devices in system 10, such as whether a device is electrically disconnected, has been removed from its body, is not deployed (e.g., not fully deployed), and / or has degraded sensors. In some embodiments, the data quality routine of system 10 (e.g., implemented as by algorithm 115) determines the state of the device by comparing parameters of measurements and / or calculations associated with the state (e.g., as determined by the sensors of system 10) with thresholds of system 10 associated with the state.

[0119] In some embodiments, system 10 (e.g., algorithm 115) is configured to perform "dynamically bounded adaptive impedance tracking" using a monitored location reference, such as using one or more of the methods described above (e.g., using one or more electrodes to calculate a virtual reference). In some embodiments, system 10 uses one or more sensors (e.g., electrodes) located within the body and designated as the monitored location reference. A physical device may be expected to remain in a static position for a period of time (e.g., a period of time longer than a minimum amount of time). System 10 may monitor this device to determine whether the device has been physically displaced and / or whether electrical interference has affected the device (e.g., a single device), one or more other devices (e.g., multiple devices), or the entire system. System 10 may be configured to determine whether any additional devices besides the monitored location reference are located (e.g., currently located) within the body. When more than one device is available in the body, system 10 may determine whether any device has been moved due to user manipulation and / or independently affected by electrical interference. System 10 may be configured, for example, to track the historical location of the monitored location reference (e.g., and other devices) when no electrical interference has occurred. System 10 can create a set of virtual measurements (e.g., virtual intracardiac measurements) that quantitatively reconstruct signals equivalent to the monitored location reference. System 10 can construct this virtual reference using a complete set of body surface measurements (e.g., functional elements of System 10 including a 12-lead ECG). System 10 can (e.g., alternatively) use a set of body surface pairs (less than the entire set) to construct the virtual reference, wherein the set of body surface pairs is selected to most tightly (quantitatively) constrain the position data of the monitored location reference device. This method can be more accurate and flexible for larger disturbances to catheter position. System 10 can track the monitored location reference device, the virtual reference, and / or all other devices located (e.g., at least partially located) in the coordinate system used by System 10. System 10 can adaptively mitigate physical and electrical disturbances. For example, when the monitored location reference device is physically displaced, the virtual reference can be recalculated to match, and time history data can receive valid, updated positions of the monitored location reference device. Alternatively or additionally, when electrical interference affects the tracking position of the virtual reference and / or the monitored positioning reference, the virtual reference can be recalculated (e.g., by algorithm 115). By computationally realigning the tracked position of the monitored positioning reference with the last known good position in the time history, position offsets caused by interference can be mitigated.

[0120] In some embodiments, system 10 is configured to perform an impedance-based device (e.g., catheter) navigation method, wherein compensation is performed to address patient breathing. As described herein, system 10 can use electrodes of the device in a static position to create a “physical respiratory reference.” System 10 can be configured to filter motion of the associated device to a breathing-related frequency range, such as frequencies less than or equal to 1.0 Hz, 0.5 Hz, and / or 0.3 Hz. System 10 can use this measured respiratory signal to compensate for respiratory motion, such as by subtracting the measured respiratory signal from the original positioning signal on any electrode. Alternatively or additionally, system 10 can use one or both of a “gating method” and / or a “dynamic learning model” to interpret patient breathing. For example, system 10 can use a gating method that uses measurements from the body surface, from within the patient, or both to establish a consistent time period of the respiratory cycle. This gating method simplifies data collection by using only data acquired during the gating range of the respiratory cycle, such as discrete acquisitions for collection of contact mapping points (e.g., the anatomical "shell" and / or EGM); and / or performing sampling on device positions from one gating cycle to the next and holding them for time-continuous measurements (e.g., simultaneously employing interpolation between gating positions). System 10 can use a dynamic learning model (e.g., an adaptive model) to train a set of surface measurements (e.g., via patch 340) to create a set of virtual in vivo (e.g., intracardiac) respiratory measurements that preserve the respiratory component of motion (e.g., using filtering as a technique to preserve only the respiratory component). System 10 can use the measured respiratory signal to compensate for respiratory motion, such as by subtracting the measured respiratory signal from the raw positioning signal on any electrode. System 10 can be configured to perform two or more of the above respiratory compensation methods simultaneously and / or sequentially. In some embodiments, system 10 performs multiple respiratory compensation methods (e.g., as described herein), wherein each method is assigned a weighting factor used by system 10 to prioritize one method over another and / or otherwise apply different levels of importance and / or influence (“priority ranking” herein).

[0121] In some embodiments, system 10 is configured to use a "respiratory tracking consistency algorithm" (e.g., the algorithm of algorithm 115) to track, monitor, analyze, and / or compensate for patient breathing. If system 10 employs more than one of the above-described respiratory compensation methods, system 10 may use the respiratory tracking consistency algorithm to cross-compare the solutions of each method to determine one or more subsets of consistent solutions and / or to determine one or more subsets of solutions indicating aberrations. As described herein, such a respiratory tracking consistency algorithm may specify methods that are temporarily "ignored" and also determine when methods that should be "ignored" should be included again.

[0122] In some embodiments, system 10 is configured to compensate for patient breathing using a “breathing compensation algorithm” (e.g., the algorithm of algorithm 115), such as being configured to determine (e.g., and display) one or more (e.g., all) of the devices in use by system 10 (e.g., the location of one or more electrodes and / or other devices of one or more catheters located within and / or on the patient) using the breathing compensation method described above and the breathing tracking consistency algorithm. System 10 can perform compensation by directly subtracting the tracked respiratory motion from all associated device (e.g., electrode) locations. System 10 can perform compensation by tracking respiratory motion differently at different anatomical locations and by subtracting local respiratory motion from the device (e.g., electrode) locations displayed in the associated locations. Different anatomical locations can be organized on a regular spatial grid (e.g., using voxels). In some embodiments, the breathing compensation algorithm is configured to generate a “probability score” including confidence, and / or other probabilistic measures (e.g., quantitative scores) regarding compensation applied to determine the location of one or more devices (e.g., the location determined using breathing compensation). For example, the probability score could be based on the probability of encountering interference, and / or it could be based on the degree to which uncompensated residual respiratory movements are maintained. The probability score can be displayed to the user via a displayed alphanumeric value and / or by modifying (e.g., enhancing) how the displayed device is shown (e.g., hashing, color changes, brightness changes, and / or other visual differences in the body and / or associated catheters and / or other devices). In some embodiments, the respiratory compensation algorithm of system 10 (e.g., implemented by algorithm 115) compares the probability score to a threshold of system 10 associated with the respiratory compensation algorithm (e.g., classifying compensation and / or modifying how the displayed device is shown).

[0123] In some embodiments, system 10 is configured to compensate for patient breathing using an "impedance navigation accuracy optimization and scaling algorithm" (e.g., the algorithm of algorithm 115). Variations in the impedance of body structures and components can affect the accuracy of navigation performed by system 10. These variations can be computationally compensated for by tracking their effects on measurement distances at different locations within the body. The process performed by system 10 to estimate the relationship at a given location between a set of impedance measurements and a corresponding set of known distances (e.g., physical spacing between electrodes) is referred to as "scaling." Impedance navigation becomes more accurate by tracking these variations and computationally adjusting them through variable scaling at different locations within the body. Initially, when a device (e.g., a catheter) measurement is first performed within the body, finite impedance data is available. This finite data can be used to initially estimate scaling within the body. As the device is manipulated throughout the body, scaling information becomes more discretely measured, and the scaling information becomes more granular. This process of system 10 is referred to as "dynamic scaling." Impedance navigation accuracy optimization and scaling algorithms may include an "impedance scaling optimization algorithm" (e.g., the algorithm of algorithm 115) that processes a set of discrete scaling measurements to create a cohesive coordinate space where the measured impedance data can be directly mapped to a unique coordinate location. As more measurements are performed, the impedance scaling optimization algorithm can be executed iteratively (e.g., at 1-second or 5-second intervals) to provide increasingly accurate navigation for the device. As navigation accuracy improves, previously collected location information can be retrospectively updated. Any data determined using the location information stored and / or calculated by system 10 can be recalculated (e.g., "reconstructed") accordingly. This data includes anatomical measurements, electrical measurements, and / or marker locations derived from the device location. Some information not directly derived from the device location can also be recalculated by system 10. For example, marker locations placed in the coordinate space that are not constituted by the measured device location (e.g., when a marker is placed on an anatomy with a mouse) can be saved with the corresponding "equivalent impedance" based on the scaling information available at the time. When the location data is recalculated, the locations of these markers can be recalculated using the equivalent impedance, and these locations can be displayed (e.g., in a new location on the screen).

[0124] In some embodiments, system 10 includes one or more components configured to provide magnetic data (e.g., data recorded and / or derived by magnetic components), such as for hybrid positioning systems. System 10 may utilize a magnetic navigation system to simultaneously track devices in the body using a second (e.g., different) measurement mode (e.g., different from those described above). System 10 (e.g., algorithm 115) may be configured to perform hybrid mitigation of interference. System 10 may utilize measurement redundancy to mitigate interference to the impedance tracking subsystem and / or interference to the magnetic tracking subsystem. Monitoring algorithms (e.g., one or more algorithms of algorithm 115) may be used for each mode to determine (e.g., iteratively and / or relatively continuously) whether interference affecting any mode has occurred. When interference is detected by algorithm 115, the affected subsystem may temporarily detach from the display of the tracked device (e.g., a catheter) until the system determines that the associated interference has been mitigated and / or no longer occurs. System 10 may be configured to perform hybrid navigation accuracy optimization and scaling. When used in conjunction with an impedance navigation subsystem, the accuracy of the magnetic subsystem is less affected by variations in body structure and composition. Therefore, when the device is manipulated throughout the body, the magnetic navigation subsystem can provide (e.g., more immediately) accurate navigation data that can be used to construct impedance scaling data (e.g., more quickly). System 10 can utilize magnetic data as the computational backbone for impedance scaling optimization, instead of using "known distances" (e.g., physical spacing between electrodes), to indirectly map impedance changes having a direct measurement correspondence between accurate location in space and impedance measurement. System 10 can navigate devices that include only impedance sensors, only magnetic sensors, or both. Devices equipped with both types of sensors can be used to directly construct impedance correspondence mapping maps onto a displayed coordinate system. In areas where both magnetic and impedance data have already been directly measured, impedance scaling optimization may be unnecessary.

[0125] In some embodiments, system 10 is configured to provide a “continuous field estimate” that defines the properties of a positioning field within a patient’s body. The continuous field estimate may be updated periodically by system 10, e.g., continuously or nearly continuously (e.g., the field estimate may be updated at least once per second). In some embodiments, system 10 includes one or more catheters comprising magnetic positioning elements and impedance-based positioning elements (e.g., magnetic coils and electrodes, respectively). System 10 may be configured to update the continuous field estimate using magnetic and impedance-based positioning information recorded from various catheters of system 10 inserted into the patient’s body. In some embodiments, the continuous field estimate includes at least one linear term (e.g., a variable associated with linear scaling of the positioning field). System 10 may be configured to incorporate positioning information recorded from one or more catheters having a known spacing between positioning elements (e.g., electrodes and / or coils) to update the linear term of the continuous field estimate. System 10 may provide a user with one or more functions to update and / or adjust the continuous field estimate. For example, system 10 may provide a “play / pause” function, a “rewind” function, and / or both, to enable the user to adjust the continuous field estimate. For example, the play / pause function of the continuous field estimation feature of System 10 can (respectively) enable and disable the addition of new data to the field estimation (e.g., updating and / or pausing updates to the continuous field estimation). In some embodiments, the rewind function of the continuous field estimation allows a user to return the field estimation to a previous state (e.g., excluding information from the most recently recorded updated field estimation). In some embodiments, the rewind function restores the field estimation from a user-defined time period before the rewind function is activated to a previous state, such as 30 seconds before the rewind function is activated.

[0126] In some embodiments, system 10 is configured to operate in multiple positioning operation modes. For example, system 10 may operate in an impedance-based positioning mode, as described herein, wherein the positioning device of system 10 is positioned to a corresponding three-dimensional (3D) location using the latest field estimate. Alternatively or concurrently, system 10 may operate in a hybrid impedance-based positioning mode, wherein the positioning device of system 10 is positioned to a corresponding 3D location using the field estimate present when positioning data is recorded by system 10. In some embodiments, the rigid connection of the conduit of system 10 to one or more electrodes of a magnetic sensor (e.g., a coil) to a magnetic sensor may be positioned using impedance field estimation and an extrapolated position relative to a magnetic sensor (e.g., when the magnetic sensor is magnetically positioned).

[0127] In some embodiments, positioning data may be gated, for example, when the data is gated based on respiration and / or cardiac phase. For example, positioning data may be gated using cardiac electrical data recorded from lead 350 and / or one or more catheter electrodes (e.g., data recorded to determine cardiac phase). In some embodiments, the gating timing is determined by the QRS wave of system 10 relative to ventricular activation. Alternatively or additionally, positioning data may be gated using respiration determined by impedance recordings collected from lead 350 and / or one or more catheter electrodes (e.g., data recorded to determine respiratory rhythm). In some embodiments, gating information may be used to determine a gating window, such as a window with upper and lower limits. In some embodiments, the gating window is configured to be adjusted by the user.

[0128] System 10 can be configured to "reconstruct" and store a portion of the patient's anatomy, such as to provide a reconstruction of one or more parts of the patient's heart. The stored anatomy can be used as input to: data computation algorithms (e.g., one or more algorithms of algorithm 115 configured to perform inverse calculations); processing and display algorithms (e.g., one or more algorithms of algorithm 115 configured to calculate and / or otherwise determine contact point acceptance criteria, the nearest surface location and / or orientation, and / or estimates of therapeutic delivery into the tissue); and / or as a visual "canvas" on which data in many forms can be displayed. Anatomical information may include one or more anatomical components (such as different anatomical structures), such as to distinguish different chambers of the heart, veins, arteries, and / or appendages. The stored anatomical information may include point location data, surface (e.g., shell) data, volume data, data from direct measurements of anatomical characteristics (e.g., density, thickness, tissue type, tissue composition), and data from calculations and / or estimates of anatomical characteristics (e.g., from the normal direction of the surface, the angle of incidence to the object, conduction properties such as fiber orientation, scar isomerism, preferred paths or connections to other structures, etc.). System 10 may be configured to record, store, process, and / or display image data to collect anatomical information. The stored anatomical information may include data recorded by system 10 and / or data provided to system 10. In some embodiments, image data is determined by locating the object in the body. The location of the object can be determined by using one or more points at a time to determine imaging points, which can be processed to form an imaging surface and / or imaging volume.

[0129] In some embodiments, system 10 includes one or more devices (e.g., catheters and / or external devices) that transmit and / or receive ultrasound signals, such that the resulting ultrasound data can be converted into image data, such as the anatomical “shell” of a patient’s heart wall tissue and / or other tissues of the patient (e.g., non-blood tissue). Imaging points can be created from ultrasound reflection data. In some embodiments, ultrasound data includes reflection data from one or more transducers. The location of the point from which ultrasound is reflected can be used to determine the location of an object within the heart (“imaging point”), such as the heart wall. System 10 can be configured to determine the point from which ultrasound is reflected by determining three basic elements: the origin (transducer location), the direction of transmission and detection (vector), and the extent to the target.

[0130] System 10 can be configured to update and / or recalculate (e.g., reconstruct) one or more imaging data points. System 10 can track each imaging point (the calculated ultrasound point) and its three basic elements. In some embodiments, System 10 can determine updated information (e.g., location information with higher accuracy), which can be retrospectively used to modify the basic elements used to calculate each imaging point. System 10 can then recalculate the updated imaging points based on the updated basic elements. For example, if one or more device navigation algorithms of System 10 (e.g., Algorithm 115) provide an updated device position, the origin and / or transmission and detection orientation of each imaging point can be updated accordingly. System 10 can then recalculate the anatomical surface based on the updated imaging points. Subsequent calculations made by System 10 (as described herein) can be updated based on the new anatomical surface and / or based on the imaging point information.

[0131] System 10 can be configured to create imaging points based on various forms of image data. System 10 can utilize various image data from ultrasound imaging devices (e.g., B-mode ultrasound imaging devices), CT scanners, X-ray imagers, and / or MRI imaging devices to determine and integrate imaging points. Such image data includes data that can distinguish objects based on intensity, color, brightness, and / or other quantitative values ​​in a 2D plane or 3D volume. The image data can be converted into imaging points by determining the orientation and alignment of the image data within a coordinate space tracked by System 10. System 10 can select one or more ranges of values ​​in the image data representing the object of interest, determine corresponding imaging points in the system's coordinate space, and integrate new imaging points. In some embodiments, System 10 utilizes data from two or more imaging devices described herein. In these embodiments, weighting factors can be applied to assign different levels of importance and / or influence to data obtained from one imaging device compared to data obtained from another imaging device.

[0132] System 10 can be configured to organize imaging points into a data structure. System 10 can track positional information in a coordinate space (e.g., a three-dimensional linear and / or Cartesian coordinate system). Within the coordinate space, System 10 can organize the imaging point data into a data structure, such as a 3D voxel space, where, for example, each voxel specifies a unique extent of volume space in the coordinate system, and each voxel can contain: no imaging points, a single imaging point, or multiple imaging points. Voxels can be uniform in each direction (e.g., when a portion of a 0.5mm × 0.5mm × 0.5mm volume and / or a 1mm × 1mm × 1mm volume). The size of voxels can be consistent and / or the size of voxels can vary. Voxels can be adaptively merged and / or partitioned to efficiently organize and process the contained data. Merging and / or partitioning can be based on the data contained within a voxel, such as the location of the data within each voxel and / or the quantity or density of the data within each voxel. The data structures created by System 10 may include octree data structures and / or other efficient data architectures, for example, to implement efficient data search and / or data processing algorithms (e.g., one or more algorithms of Algorithm 115). Additionally or alternatively, for example, when the manifold neighborhood is explicit in the grid definition used by System 10 and / or must be determined by System 10 after the grid is created, the data structure may take the form of structured computational grids (e.g., linear grids) and / or unstructured computational grids (e.g., tetrahedral grids).

[0133] System 10 can be configured to determine attributes and / or quantitative measures that can be used to analyze imaging points. Data structures created by System 10 can track the attributes and / or quantitative measures of each voxel, such as to achieve processing efficiency. In some embodiments, each voxel has attributes associated with (e.g., tracking attributes associated with): being filled or empty; the number of imaging points within the voxel; and / or the density of points within the voxel. Each voxel can maintain tracking of its geometric center. Each voxel can maintain tracking of the geometric centroid of the points contained within it. When imaging data is added, removed, and / or changed, System 10 can update the attributes and / or quantitative measures accordingly. System 10 can track multiple parallel data structures, such that points can be assigned to different data structures to track independent sets of points. For example, System 10 can assign points designated as originating from the left ventricle of the heart to a first data structure and points designated as originating from the right ventricle of the heart to a second data structure. Alternatively, System 10 can use a single data structure to organize all points and track such assignments using voxel attributes and / or individual points.

[0134] System 10 can be configured to identify artificially created imaging points. These imaging points can be artificially created within anatomical structures. In some embodiments, imaging points located within a threshold distance of any previous location of an intracardiac device can be excluded from further calculations. In some embodiments, system 10 includes multiple threshold distances (e.g., multiple thresholds related to different device types or other differences) for excluding one or more devices.

[0135] System 10 can be configured to generate surfaces (e.g., shells or portions of shells), such as surfaces representing the walls of a patient's heart and / or other tissues. System 10 can create surfaces based on a set of imaging points. In some embodiments, the surface is calculated using a Poisson surface based on a set of points. In some embodiments, System 10 uses a voxel data structure to organize a set of imaging points. "Filled" voxels are used to generate the surface. Organizing imaging points into voxels improves the efficiency of surface computation because the data density of the original set of imaging points can far exceed the necessary resolution for creating the surface. In some embodiments, System 10 (e.g., Algorithm 115 of System 10) uses the locations of the filled voxels (e.g., the center point of each voxel) as surface guide points. Each surface guide point is associated with an ultrasound vector, the direction of which is estimated to be in the normal direction of the desired computed surface. In some embodiments, System 10 approximates the normal vector using a normalized radial projection from the center point in the coordinate system to the center of each voxel. The center point is preferably within and near the center of the desired closed surface. In anatomical applications, it is appropriate to select a point at the center of an anatomical structure. The center point can be reassigned by System 10 as desired and / or required. In some embodiments, the system 10 can calculate the surface by first establishing a set of Gaussian functions, the Gaussian functions being positioned approximately at each surface guide point. Based on the Gaussian functions, along with the normal vector, the solution to the Poisson equation defining the surface is obtained (hereinafter): The solutions φ of the governing equations are obtained discretely using an inverse method, which produces the “intensity” of each Gaussian function. In short, one implementation of the governing equations is written for each surface guide point, and the divergence of the normal at that point is represented as a linear sum of contributions from nearby surface guide points. To limit the influence region around each surface guide point, the basis functions are constrained with compact supports. This produces a sparse influence matrix that facilitates fast solutions. The calculated surfaces are chosen as closed isosurfaces with a value of φ = 0.5.

[0136] The surface can be represented as a triangular mesh. The resulting surface will be a closed surface that follows the guide points as closely as possible. In areas with no guide points or a limited number of guide points, the accuracy of the calculated surface may be poor, and it can be removed from the triangular mesh. In some embodiments, system 10 does not calculate the surface until a sufficient number (e.g., 48, 64, or 100) of guide points distributed in the coordinate system are collected. In some embodiments, a sufficiency algorithm of system 10 (e.g., one or more algorithms of algorithm 115) determines when the number and distribution of guide points are sufficient to initiate surface calculation (e.g., when a threshold is exceeded). In some embodiments, the calculated surface is biased toward the interior or exterior of a set of surface guide points. A scaling algorithm of system 10 (e.g., one or more algorithms of algorithm 115) can be applied, which estimates the signed offset of each surface guide point to the calculated surface and repositions each vertex of the calculated surface such that the average offset vanishes. In some embodiments, the surface calculation process of system 10 generates isolated segments of the surface, and algorithm 115, which includes an isolated segment removal algorithm, eliminates these structures.

[0137] In some embodiments, system 10 can create a visualization of a surface based on imaging points. System 10 can be configured to iteratively calculate and display the imaging surface as imaging point data is continuously collected. System 10 can display raw imaging points, surface guide points, surface meshes, or any combination thereof. In some embodiments, the surface mesh can be displayed as a single color. In some embodiments, the surface mesh can be visualized differentially based on properties and / or quantitative data of nearby surface guide points (e.g., via variations in color, transparency, intensity, etc.). For example, the surface can be colored and / or otherwise graphically differentiated based on the density of imaging points within nearby voxels. This visual differentiation allows the user to observe areas where sufficient data has been collected and / or areas where data collection has been limited, allowing the user to adjust data collection accordingly. The opacity of portions of the anatomical model mesh (e.g., triangles) can also be based on the density of imaging points within nearby voxels. During live data collection and / or iterative calculation of the surface, the visualized surface can have an initial appearance to facilitate user interpretation of the data collection process and / or optimize computational efficiency and processing speed. When not actively collecting data, the visualized surface provided by system 10 can have an optimized appearance. In some embodiments, system 10 re-initiates the recalculation and updating of the display at regular intervals (such as intervals not exceeding 0.5 seconds, 1 second, and / or 2 seconds). In some embodiments, the recalculation and updating of the display are re-initiated asynchronously with a set of additional imaging points. In some embodiments, the "update re-initiation algorithm" of system 10 (e.g., one or more algorithms of algorithm 115) is used to estimate and / or calculate the degree of change introduced by newly acquired data, and the algorithm can determine when the acquisition of new data exceeds a threshold for initiating the recalculation of the surface and the corresponding update of the display. In some embodiments, system 10 uses a combination of re-initiation methods (e.g., where a weighting factor is assigned to each re-initiation method, such as applying a distinguishing importance to each method). In some embodiments, system 10 uses shorter intervals of any of the combinations of re-initiation methods.

[0138] System 10 can be configured to create anatomical reconstructions that include volumetric reconstruction. In some applications, anatomy can be used as a volumetric structure rather than a surface structure. If a surface structure has already been created, System 10 can compute the volumetric structure by filling the internal volume of the surface with points and generating a tetrahedral volumetric mesh to represent the volumetric object. In some embodiments, a set of internal points can be determined (e.g., rigorously determined) by testing each point on a regular 3D mesh to determine whether it falls inside or outside the surface. In some embodiments, the “internal pointplacement algorithm” of System 10 (e.g., one or more algorithms of Algorithm 115) can efficiently search for points inside the surface and can determine a set of internal points at a density sufficient to create a tetrahedral volumetric mesh. In some embodiments, the internal pointplacement algorithm can use a first inoculation point inside the closed boundary of the surface to construct large tetrahedral segments with portions of the surface (e.g., uniform or other similarly sized) and iteratively subdivide the tetrahedrons into increasingly smaller sizes until a threshold (e.g., a desired number of internal points, internal point density, average tetrahedral edge length, or volume) is met. Alternatively, the interior point placement algorithm can use compartment subdivision to perform a search to test points on a regular 3D mesh, where the data space is divided into large compartments, and each boundary point of a compartment is tested to be classified as either inside or outside the surface. One or more compartments with boundary points inside are subdivided, and this process is repeated for each subdivision of a previous compartment. In some embodiments, previously tested boundary points are no longer tested. The subdivision process can continue until a threshold (e.g., a desired number of interior points, interior point density, and / or average tetrahedral edge length or volume) is met. In some embodiments, system 10 includes multiple such thresholds that can be used to assess the quality (e.g., resolution, accuracy, etc.) of the output of the classification (e.g., quantization or limiting) process.

[0139] In some embodiments, multiple tetrahedral meshes (e.g., low-resolution and / or high-resolution meshes) are pre-computed, and when the system 10 completes the dissecting shell, the bounding boxes of the meshes can be estimated (e.g., estimated by algorithm 115 of system 10), and the pre-computed meshes can be transformed into the dissected bounding boxes. In some embodiments, after the transformation of the pre-computed meshes, each node of the mesh can be evaluated (e.g., evaluated by algorithm 115 of system 10) and classified as falling inside or outside the dissected mesh.

[0140] In some embodiments, the pre-computed mesh includes a varying resolution, where dense tetrahedrons are located near the center of the bounding box and sparse tetrahedrons face outwards from the bounding box. This resolution parameterization can be determined based on the population-level average shape and metrics computed from it, such as a distance function (e.g., determined by algorithm 115 of system 10).

[0141] System 10 can be configured to create anatomical reconstructions by generating anatomical data based on the location of tracking catheters and / or other devices. System 10 can create anatomical data by tracking the location of the navigated device and determining the outer boundary of the volume “tracked out” by the different locations of the device. The overall location of the tracked device can be used by System 10 (e.g., by Algorithm 115) to form a representation of the anatomical volume.

[0142] System 10 can be configured to create anatomical data by integrating anatomical data collected from multiple methods (e.g., the methods described herein or two or more other methods). In some embodiments, System 10 integrates various (e.g., all or a portion) anatomical data collected by System 10 using multiple methods. In some embodiments, System 10 converts imaging surface data into volume data. Device tracking data can also be represented as volume data (e.g., volume data including structured and / or unstructured elements). The integration of the two datasets can be performed (e.g., by Algorithm 115) by merging the volume data in the same coordinate space and processing the union of the two datasets into a cohesive whole.

[0143] System 10 can be configured to edit (e.g., allow operator-based and / or automated editing) anatomical structures. System 10 can be configured to enable users to add, remove, and / or edit anatomical structures. In some embodiments, surface or volume data can be deleted and / or modified (e.g., “shaked”). Surface or volume data can be reassigned to different structures. Surface data can be modified by cutting holes in a surface mesh. In some embodiments, user-selectable geometries (e.g., predefined geometries stored in a library of System 10) are used to facilitate the creation of anatomy. For example, one, two, three, or more of each of spherical, elliptical, teardrop, and / or other shapes can be provided by System 10 and used to facilitate the initial creation of anatomy.

[0144] System 10 (e.g., via algorithm 115) can be configured to perform cardiac signal (e.g., EGM) optimization. For example, system 10 can be configured to perform far-field compensation. In some applications, such as by inversely solving atrial activation, the far-field effects of activation in chambers other than the atria being diagnosed and / or treated (e.g., ventricles or relative atria) can be destructive. Isolating relevant components of the EGM from the chamber of interest before computing the inverse mapping can be advantageous. As an example, separation and exclusion of the ventricular component (QRS) from the atrial component can greatly benefit the quality of the data generated by system 10 (e.g., mapping data generated by system 10). System 10 can perform various signal processing functions, such as processing the ECG (e.g., in a self-optimizing arrangement) to inversely model the QRS or PVC. In some embodiments, body surface ECGs can be used by system 10 to create a coarse model of ventricular depolarization and repolarization. In some embodiments, system 10 (e.g., via algorithm 115) can create the model using the inverse solution of dynamically detected ventricular activity (e.g., QRS) on body surface leads, calculating an inverse solution estimate of ventricular activity for each ventricular beat (as would be measured at the location of the intracardiac catheter), and subtracting the calculated ventricular activity from each corresponding beat of the intracardiac signal. In addition to and / or instead of ECG-based parameterization, ventricular templates can be automatically (e.g., by algorithm 115) identified based on the intracardiac signal, and / or the templates can be determined by the user. Where the ventricular components are inversely calculated by system 10 independently of the atrial components, the forward matrix can be estimated by system 10 (e.g., by algorithm 115) using subject-specific anatomy, rule-based averages, and / or geometric primitives. This combination of estimation and subtraction aims to preserve as much of the atrial component of the intracardiac signal as possible while removing as much of the ventricular component as possible. The “atrium-only” EGM can then be processed (e.g., by Algorithm 115) using the inverse solution for whole-ventricle non-contact mapping, resulting in a mapping map with minimal ventricular artifacts or erroneous annotations due to the presence of ventricular artifacts. The ventricular estimation performed by System 10 can also be self-optimized by evaluating the residuals of the removed ventricular signals and then updating the estimation accordingly to minimize the residuals. Additionally, the estimation can be refined by System 10 (e.g., via Algorithm 115) by first applying grouping, clustering, and / or classification to ensure that the collected heartbeats (also referred to herein as “heartbeats” or “beats”) used for estimation are similar to and well-matched with the beats to which the estimation is applied. Heartbeats with different characteristics will be classified into different groups and thus will have different estimates to be applied.

[0145] System 10 can be configured to perform measurements (e.g., direct measurements) in an arrangement that reduces artifacts introduced by far-field activity. System 10 can perform measurements based on a first electrode (e.g., a device of System 10 inserted into a heart chamber) and use a second electrode (e.g., the same or a different device of System 10) as a near reference. The first electrode can be configured to touch tissue for measurement (e.g., configured to perform contact measurements). The second electrode can be configured not to touch tissue when performing measurements (e.g., configured to perform non-contact measurements). By subtracting the signal from the second electrode from the signal from the first electrode, the far-field component measured by both electrodes will be suppressed, but the local signal measured only by the contacting electrode will be preserved (e.g., and is not significantly suppressed).

[0146] System 10 may include one or more catheters or other devices including stacked electrodes (e.g., stacked microfabricated electrodes). For example, in some embodiments, the first and second electrodes are configured in a stacked orientation, wherein the stacked electrodes are separated by a small spacing distance. In this configuration, the electrodes may be printed on flexible circuitry and may be deployed on a maneuverable catheter, wherein in most deployment configurations, only the first electrode contacts the tissue.

[0147] System 10 can be configured to perform measurements from far-field contributors (such as those in the ventricles) when treating and / or diagnosing the atria. In some embodiments, the ventricular estimation creation and subtraction process of System 10 may utilize one or more direct measurements from the relative chambers. Direct measurements may be used in place of the inverse solver template, or these measurements may be used to cross-check the inverse solver template.

[0148] System 10 may include algorithm 115, which includes one or more machine learning, neural network, and / or other artificial intelligence algorithms (referred to herein as "AI algorithms"). System 10 may include machine learning algorithms or other AI algorithms (e.g., one or more of the algorithms in algorithm 115) for determining EGM benchmarks, categories, and other parameters. System 10 may be configured to store, share, and / or learn user interventions (e.g., user interventions in automated processes). System 10 may also be configured to adaptively self-optimize automated processes in response to learning. System 10 may maintain a self-updating dataset covering any user inputs to be automatically detected or measured by the system. User selections and corresponding raw data may be treated as labeled data and stored. Some examples of these labels include, but are not limited to: measurement channels to be excluded; threshold changes to be made; time annotation changes to be made; jumps to be included and / or excluded; points to be included and / or excluded; and so on. The database of labeled data may be used for local "learning" (e.g., identifying or evaluating) of fundamental features in the data to produce more accurate automated results. System 10 can also be configured to automatically share the database and / or transfer the database to a main storage library, which can be shared with one or more other System 10 units (e.g., in another location, such as when shared via a secure wired or wireless connection).

[0149] As described herein, System 10 can be configured to perform various forms of cardiac electrical mapping. System 10 can process cardiac signals to detect and analyze cardiac events, such as heartbeats and / or cycles. System 10 can be configured to perform “rhythm tracking,” for example, when heartbeats are classified. System 10 (e.g., via Algorithm 115) can automatically distinguish and / or classify each individual heartbeat based on real-time or near real-time (“real-time” herein) and / or in a post-processing step, based on each individual heartbeat having similar characteristics. Groups can be used to map patterns that utilize multiple heartbeats to sequentially aggregate data. Some mapping patterns can utilize beats that are not suitable for pre-existing groups, and the ability to group beats helps automate the identification of these unique beats (e.g., via “triggered mapping patterns” of System 10 as described herein). System 10 can perform heartbeat detection, annotating a reference beat at an initial time point (time T0). In some mapping patterns, System 10 performs time alignment of heartbeats on a time reference, such as. System 10 can detect each beat by looking for signal features on one or more cardiac signals recorded by System 10. The cardiac signal analyzed by system 10 for beat detection can be any mathematical combination of unipolar, bipolar, omnipolar, Laplace operator, and / or one or more signals. The analyzed signal can also be the derivative, envelope, energy function, histogram, and / or other result of a mathematical operation performed on the measured cardiac signal. System 10 can acquire these signals from one or more devices of system 10. The signal (e.g., the processed signal) can be a mathematical composite of multiple signals. For example, when system 10 analyzes both unipolar and bipolar signals (e.g., simultaneously or sequentially), system 10 can combine and analyze more than one of the aforementioned signals (e.g., including the processed signal). Signal features identified by system 10 can include one or more features selected from: crossing a threshold (positive, negative, or absolute value); local maximum or minimum peak value (positive, negative, or absolute value); local maximum slope (positive or upslope and / or negative or downslope). System 10 can use the time of the detected features for T0 alignment. System 10 can optionally apply a time offset to the detected features. System 10 may combine more than one of the aforementioned signal features, such as features where a cardiac event both crosses a threshold and has a sufficiently large local maximum negative slope. If system 10 uses more than one signal type (e.g., simultaneously), the signal features for each signal type may be the same or different for each signal type. In some embodiments, system 10 may require the positive peak of the filtered bipolar signal to be of sufficient magnitude to establish independent beats and establish a T0 reference time. Alternatively or additionally, system 10 may use the average, median, and / or maximum values ​​of the envelope or energy function of one or more bipolar signals to establish the T0 reference time.

[0150] In some embodiments, conduction velocity is estimated as a global optimization function, thereby regularizing and modulating the global velocity by system 10 (e.g., by algorithm 115) based on physiological range and loss function, which are defined based on deviations from the mean physiological value and / or stratified based on a series of averages compiled from the pathological population.

[0151] In some embodiments, the solution is regularized by system 10 (e.g., by algorithm 115), such as by spatial filtering on the graph and / or grid using techniques such as arithmetic mean, arithmetic median, and / or projections to graph-based basis functions. In the case of conduction velocity, filtering can be applied to the velocity vector in its canonical form and / or in its quaternion form. Filtering can also be applied via nonlinear techniques, such as via neural networks and / or locally nonlinear filters, such as median.

[0152] System 10 can be configured to perform cardiac beat classification, wherein reference channel exclusion is performed. System 10 can perform reference channel exclusion selected from: excluding (e.g., automatically excluding) one or more channels; disabling (e.g., automatically disabling) one or more channels used as T0 reference time; disabling (e.g., automatically disabling) one or more channels used for detecting and / or classifying cardiac beats; and combinations thereof. These different channel exclusions can be made due to electrical disconnection and / or lack of detectable features on those channels, such as due to poor performance when used as T0 reference time and / or for classifying beats. If a sufficient amount (e.g., a significant amount) of a specific frequency (e.g., 60 Hz) is present on the channel, System 10 can automatically disconnect one or more channels, as this condition can typically be an indication that the sensor (e.g., electrode) is electrically disconnected and / or "noisy" (e.g., experiencing electrical interference or other signal noise). System 10 may be configured to automatically exclude channels if the signal amplitude is below a threshold (e.g., if the sensor is in a suboptimal position to measure cardiac signals) and / or if the signal amplitude is above a threshold (e.g., when pacing results in a large pacing artifact much larger than the baseline cardiac signal). System 10 may be configured to exclude a sensor (e.g., an electrode) if its position is uncertain, inconsistent, and / or abnormal, as this may indicate poor-quality electrical connections and / or the influence of external systems that could degrade performance. System 10 may be configured to automatically exclude channels based on: statistical data analysis; data mining; and / or machine learning or predictive analytics to analyze the measured signal of a channel against a model and / or library of previously accepted and / or excluded signals (e.g., leading to the determination of a set of channels to be excluded). In some embodiments, the model and / or library used by the machine learning and / or predictive analytics algorithms of System 10 (e.g., the AI ​​algorithm of Algorithm 115) includes labeled signals based on operator decisions to accept and / or exclude.

[0153] System 10 can be configured to perform "optimal reference channel selection". System 10 can automatically select and / or suggest the best signal or set of signals for use as a T0 reference and / or for cardiac classification. System 10 can use amplitude and / or timing interval (e.g., period length) stability and / or consistency ranges and / or other thresholds. System 10 can utilize confidence metrics, which are used to evaluate multiple signal characteristics, including but not limited to: period length stability; amplitude; signal morphology (e.g., degree of grade separation or presence of certain signal components, such as a certain number of waveform deflections or a specific shape, such as an "RS" morphology); and / or the confidence score of the signal (e.g., when only signals with sufficient confidence scores are used).

[0154] System 10 can be configured to compare various characteristics of the recorded signals. System 10 can be configured to distinguish or classify heartbeats using one or more features, such as based on features selected from: stability and / or consistency of timing intervals (e.g., cycle length); signal morphology (e.g., unipolar signal morphology); envelope and / or energy function (e.g., bipolar signal envelope, for example); temporal and / or pattern across multiple signals (e.g., pattern across a time reference of a set of unipolar and / or bipolar signals from sensors at different locations in the heart); and combinations of these. System 10 can be configured to use wavelet decomposition to isolate unique signal components from the morphology of the signal, such as to quantitatively compare these components with those components of other signals. System 10 can perform cross-correlation between signal and / or wavelet components to quantify the degree of matching between two or more signals. System 10 can be configured to perform statistical analysis on cycle length (the interval between beats) to characterize the beat. Similar cycle lengths can indicate the same heart rhythm or cardiac circuit, and differences in cycle length can indicate variations in heart rhythm or cardiac circuit. System 10 can be configured to use the envelope and / or energy function of one or more signals. The signals can be unipolar, bipolar, omnipolar, Laplace operator, and / or any other mathematical combination of one or more signals. When System 10 uses multiple signals, the envelope and / or energy function can be evaluated for each individual signal, and / or the envelope and / or energy function can be evaluated for the aggregation or composite of a combined set of signals. For example, the envelope and / or energy function of each of a set of multiple bipolar signals from one or more locations in the heart can be used as a template against which the same signals from other heartbeats can be compared. System 10 can be configured to “match” a set of comparison signals that fits the template (e.g., like a key into a lock). Alternatively or additionally, System 10 can stack, aggregate, and / or otherwise create composites of multiple signals, and then determine the envelope and / or energy function of the composite to create a template. Then, for each comparison beat, a similar composite and envelope and / or energy function can be constructed and compared to the envelope / energy function of the template. In some embodiments, the cross-correlation between the template envelope and / or energy function and the envelope and / or energy function from the comparison jumps can be used by system 10 to quantitatively compare jumps. System 10 can be configured to match jumps with sufficient correlation scores. In some embodiments, a combination of multiple characteristics can be used by system 10 to distinguish jumps.

[0155] System 10 can be configured to group heartbeats. For example, System 10 can be configured to automatically create unique groups and classify individual beats into them. System 10 can perform various forms of clustering and / or classification methods, including but not limited to: linear or quadratic discriminant analysis, correlation analysis, principal component analysis, K-means (e.g., connectivity-based or centroid-based) clustering, support vector machines, kernel methods, neural networks, spectral clustering, hierarchical clustering, distribution-based clustering, density-based clustering, and / or grid-based clustering. These techniques can be configured to identify similar beats and / or classify beats into groups. In some embodiments, System 10 uses k-means clustering to process the wavelet-decomposed signal set to determine the set of clustered beat groups. System 10 can use a combination of multiple signal features and / or weighted scores to determine the overall group classification. For example, System 10 can use both period length and k-means clustering of the wavelet-decomposed signal based on a combined weighted score to group heartbeats to produce (e.g., identify) multiple groups. By employing a weighted approach, system 10 can be configured to allow a user to select the relative weights assigned to each individual score. For example, a user might prefer to increase the relative weight given to the morphology score while decreasing the importance of changes in period length in clustering / classification (or vice versa). System 10 can be configured to perform live (e.g., real-time) and / or post-processing computations. For example, upon encountering each new heartbeat, system 10 can process beat detection and classification as a post-processing step on the recorded data, or iteratively "on the fly" (e.g., live in real-time).

[0156] System 10 can be configured in a “triggered mapping mode,” such as a mode configured to perform a “unique beat detection and rapid mapping routine,” in which the routine detects unique beats and / or performs rapid mapping (e.g., based on PAC, PVC, and / or other triggers). System 10 can be configured to utilize (e.g., included in one or more analyses) beats that do not fit into pre-existing groups, such as identifying unique beats and / or beats with a small number of repetitions. System 10 can establish one or more beat groups and then identify beats that do not match the established groups. System 10 can be configured to visually specify (e.g., via graphical differentiation as described herein) and / or otherwise identify these mismatched beats (e.g., via a display of System 10). Once these unique beats are detected, System 10 can employ several mapping methods to identify values ​​derived from the signals of these beats (e.g., derived cardiac data), including but not limited to: activation time, peak-to-peak amplitude, beat or other inter-beat metric timing (e.g., cycle length, ST segment), and / or other data derived from the cardiac signals. Cardiac data can be calculated directly from measurement signals on one or more devices (e.g., catheters), and the data can be displayed as a visualization (e.g., 3D visualization) on an anatomical shell. The system 10 can derive cardiac data from cardiac signals (e.g., from individual or multiple catheters) to generate visualizations in various ways, including but not limited to: interpolation and / or direct assignment of values ​​based on electrode-shell proximity; inverse solving; and combinations thereof. The system 10 can be configured to distinguish (e.g., color-coded or otherwise graphically distinguish) on a display: sensors; objects near the sensors (shell, markers, spatial volumes); and / or combinations thereof. The displayed distinctions can be based on activation time and / or amplitude data.

[0157] System 10 can be configured to perform a “heart rhythm classification routine.” For example, System 10 can use detected and classified heartbeats to make automated “suggestions” about heart rhythm types (e.g., presented to the operator on a display of System 10). System 10 can make suggestions based on fixed metrics, such as a range of cycle lengths or the RR interval between QRSs (or any other inter-beat metric). System 10 can make suggestions based on statistical analysis; data mining; machine learning or other AI algorithms and / or predictive analysis to analyze signals using models and / or libraries of previously classified heart rhythms; and combinations thereof. For example, cardiac signals, such as those used to detect and classify individual beats (e.g., a set of reference EGMs from a catheter placed in the coronary sinus), can be processed by System 10 (e.g., via algorithm 115) using wavelet transform, which decomposes each waveform into different frequency bands while still preserving temporal information to produce a wavelet scalar graph image. This processing of cardiac signals can also be performed by System 10 in an unsupervised workflow. Image-based features in the zoomed-out images comparing beats can be evaluated by System 10 using a convolutional neural network or other AI algorithm trained on a labeled zoomed-out image library where the heart rhythm type is known. Some classifiable heart rhythms of System 10 include, but are not limited to: atrial flutter, atrial tachycardia, atrial fibrillation (AF), sinus rhythm, paced rhythm, or ventricular tachycardia. Heart rhythms can also be classified using any data duration (not just a single, detected, and classified beat) and / or any set of signals acquired by System 10 (e.g., obtained via a reference catheter, mapping catheter, surface electrodes, etc.).

[0158] System 10 can be configured to perform mapping (e.g., cardiac mapping) of the inventive concept using various forms of data collection and data analysis. System 10 can be configured to display electrical events and / or activities (e.g., cardiac) in the form of an electroanatomical map (EAM) based on data generated from electrical signals (e.g., electrograms or EGM), wherein the data is visualized on a display of anatomical structures. The EAM of System 10 can show activity data (AD) at a single location, region, chamber, the entire heart, and / or any other body volume (e.g., tissue volume). AD can include activation time, amplitude, conduction velocity, grade separation, complexity index, pattern detection, sequence detection, causality index, recurrence index, dispersion index, refractory (e.g., angle between subsequent activations) measures, and / or any results calculated from signals (e.g., cardiac signals and / or imaging signals). Similarly, integrated measures (such as measures that combine refractive measures with activation sequences) can be applied by System 10 (e.g., by Algorithm 115) to determine the spatiotemporal initiator of refractory events. The EAM generated by System 10 may also include AD based on signals from the inner surface (endocardial surface), the outer surface (epidermal surface), and / or from tissue between the two surfaces (middle myocardial tissue or transmyocardial tissue). AD may be based on signals acquired by electrodes in contact with tissue (contact data) and / or AD may be derived from calculated signals at the tissue location derived from measurements by electrodes not in contact with tissue (non-contact data). AD may be based on signals that can be any other mathematical combination of unipolar, bipolar, omnipolar, Laplace operator, and / or one or more signals, and / or other results from derivatives, envelopes, energy functions, or mathematical signal manipulations. AD may be based on signals that can be acquired from one, two, or more devices of System 10 (e.g., one, two, or more catheters, patches, and / or other components of System 10). This document describes both contact mapping and inverse-solution-based non-contact mapping. AD may be based on signals measured in contact and / or signals calculated in non-contact. EAM can include the combination, aggregation, integration, and / or fusion of one or more types of mapping data, such as simultaneous unipolar and bipolar AD, and / or simultaneous contact and non-contact signal AD. System 10 may also include a "data fusion algorithm" (e.g., one or more algorithms of algorithm 115) that can be configured to cohesively combine data from different forms and / or different signal sources. For example, the data fusion algorithm can calculate the activation time based on both bipolar signals and their corresponding unipolar signals; and / or contact and non-contact signals. The data fusion algorithm can determine whether either, both, or none is feasible, and if both are feasible, determine the activation time to be used in EAM.If activation times are inconsistent, the data fusion algorithm can use a set of rules involving different signal characteristics (such as amplitude, slope, width, morphology, energy, etc.) to select the optimal activation time to use and / or calculate intermediate values ​​to use. In these embodiments, system 10 includes one or more thresholds for evaluating signal feasibility or performing another data evaluation. Alternatively or additionally, the data fusion algorithm may include a learning model configured to determine the optimal activation time to use based on historical data (e.g., labeled data) and / or by using an unsupervised workflow (e.g., a workflow using unlabeled, non-referenced data). In some embodiments, the data fusion algorithm may combine contact bipolar amplitude with non-contact unipolar amplitude. In other embodiments, the data fusion algorithm may combine activation times from contact signals (e.g., contact bipolar signals) and from non-contact signals (e.g., non-contact unipolar signals). The data fusion algorithm may establish a relationship between data from two different types and / or different signal sources and provide data for display in cohesion measurement units (e.g., normalized percentage or equivalent measurement units).

[0159] System 10 (e.g., via a data fusion algorithm) can be configured to perform mapping directly from measurements, such as performing a "live scan" (e.g., a real-time scan). System 10 can be configured to directly compute electrical activity information from the measured signals and display them (e.g., rapidly display them) on anatomical structures (e.g., shells or other images of the anatomical structure provided on a display as described herein by System 10). In some embodiments, data is distributed or projected onto an anatomical surface on the display of System 10. Electrical activity information can be displayed on the anatomical structure when collected in close proximity (e.g., at a distance of less than 5 mm). System 10 can compute (e.g., directly compute) electrical activity information from an electrogram at a distance (e.g., a threshold distance of at least 5 mm) and project it onto the anatomical structure as a sparsely sampled, low-density mapping of the electrical activity information. In some embodiments, System 10 includes an "interpolation algorithm" (e.g., one or more algorithms of Algorithm 115) configured to compute the displayed data in areas where measurements are unavailable. Data fusion algorithms can cohesively integrate ADs collected close to the heart with ADs measured at a distance (e.g., above a threshold). In some embodiments, for example, using the steepest negative slope, unipolar signals from devices that do not contact tissue (e.g., catheters) (e.g., electrodes that do not contact tissue) are measured and local activation times are directly annotated. These activation times are projected onto the nearest surface or along a vector (such as a vector perpendicular to the electrode orientation). When the device (e.g., its associated set of electrodes) is located near the center of the chamber, the activation times can be projected around the entire chamber. When the device (e.g., its associated set of electrodes) is closer to the walls of the chamber, the activation times can be projected onto the nearest walls. Activation times can be calculated by system 10 for each detected heartbeat, and / or these times can be calculated only for a single detected beat (e.g., as described herein with reference to trigger mapping patterns and / or unique beat detection and rapid mapping routines). Once system 10 has calculated the AD, as another form of visualization, images of the measuring sensors (e.g., electrodes) can be distinguished (e.g., colored on a display or otherwise distinguished) to show the relative relationships between each sensor. For example, if the AD is a local activation time, the earliest detecting sensor can be color-coded red to designate the “early” portion of the circuit, while the latest detecting electrode can be color-coded purple to designate the “late” portion of the signal. Alternatively or additionally, the AD can be displayed on the anatomical structure and can be similarly color-coded. Other variations of visual distinction are also within the spirit and scope of this application.

[0160] System 10 can be configured to automatically place “markers of interest” on a display of information provided by System 10 (e.g., mapping and / or other information related to clinical procedures performed using System 10). System 10 can be configured to place the markers of interest (referred to herein as “markers”) in a displayed coordinate system and / or on anatomical structures, such as placement at salient locations based on AD. Markers may have visual attributes specifying the confidence level of the marker (e.g., confidence level related to salient location). For example, if AD is a local activation time, System 10 may display a large marker at the “earliest” location on the anatomical shell. For each detected beat, a new large marker may be placed on the anatomical shell. An operator may interact with the markers (e.g., via user interface 120 of System 10) to display (e.g., add) relevant information about the corresponding beat. On many consecutively detected beats, System 10 may provide (e.g., visually provide) multiple markers, such as when these markers indicate spatial consistency of marker locations.

[0161] System 10 can be configured to adjust the resolution of a mapping map (e.g., EAM), such as changing it (e.g., from a lower resolution mapping map) to (e.g., upgrading it to) a higher resolution mapping map. When used with the unique jolt detection and fast mapping routines described herein, the mapping map and markers for each unique jolt can be used to indicate the earliest activated site. The mapping map data can be slightly coarse when directly calculated. However, any directly calculated mapping map of the detected jolt can be further processed into a high-resolution mapping map using inverse solving. The corresponding display color mapping map and large markers will become more detailed, the EAM will be calculated at a higher resolution, and the markers will become smaller and more precise in location. System 10 can perform direct AD calculations on any jolt specified by the operator to form a mapping map, automatic marker placement, and further processing into a high-resolution inverse-solved mapping map.

[0162] System 10 can be configured to provide a confidence score associated with the source chamber. System 10 can be configured to perform a "chamber of origin routine" (e.g., via algorithm 115) that provides a confidence score for a given beat origin point (e.g., if one exists) located in the mapped chamber or in an adjacent chamber. The source chamber routine can use relative timing information between intracardiac and surface measurements to compute the confidence score. The routine (e.g., algorithm 115) can also use one or more morphological analyses to automatically identify characteristic morphological features in the EGM at the earliest site of activation, such as a slight positive peak in the rS pattern, as used by system 10 to determine the confidence score.

[0163] System 10 may include algorithm 115 configured to perform a “regional inverse mapping routine,” such as a routine that performs inverse computation of cardiac activity within a region. The EAM calculated by system 10 may be a whole-chamber inverse mapping map. In some embodiments, inverse computation is applied to solve for an EGM that applies only to a portion of the chamber. The calculated EGM in the region may be used by system 10 to determine AD in the region, including activation time, signal amplitude, and / or scar area. The forward matrix may be adapted (e.g., by algorithm 115) to solve for the entire chamber, such as when the entire chamber lacks certain structures (e.g., missing left atrial appendage), and / or the forward matrix may be adapted to solve for those individual structures themselves. Using the forward matrix, inverse computation may be (e.g., by algorithm 115): derived via a direct inverse method, by solving a system of equations, via one or more neural networks, and / or via iterative solutions to a regularized optimization problem. These optimizations may include residual terms that may be implemented in accordance with the measurement and regularization terms (e.g., and imposing regularity or prior knowledge on the inverse computation). The residual term can use various loss measures, including least squares, absolute difference, and / or reweighted least squares. The residual term can also be used by System 10 to constrain the solution to satisfy specific constraints, such as non-decreasing or non-increasing in time, or residing within a predetermined subspace (e.g., a subspace derived from the data via linear or nonlinear methods and / or from the domain in which the solution is defined, such as graph-based basis functions). The regularization term used by System 10 can be in the form of zero-order, first-order, and / or second-order Tikhonov regularization. Alternatively or additionally, other regularization methods can be used, such as graph-derived basis functions, dictionary-based methods, median filtering, plug-and-play methods, and / or neural network-derived regularization.

[0164] In some embodiments, system 10 may directly estimate relevant AD information without first explicitly deriving EAM. This estimation may be performed by system 10 by projecting and interpolating AD from signals captured by catheters (e.g., signals captured by one or more catheters as described herein) to a chamber (and / or its region) and / or by nonlinear iterative optimization, such as directly solving for AD given a predetermined model for electrical activity in the region of interest.

[0165] The post-processing method can be applied by system 10 (e.g., by algorithm 115) to both the EAM and AD solutions using various methods, including mean and median filtering, graph-based filtering methods, and / or neural network methods.

[0166] System 10 may include an algorithm 115 configured to perform a “supermap routine”, such as a routine configured to generate an active map derived from signals recorded over time and from an electrode array that is repositioned during the recording process.

[0167] System 10 can be configured to perform cardiac information analysis. System 10 can process activity data (AD) to compute additional metrics that can be used to analyze the electrical activity of patient tissues. System 10 (e.g., algorithm 115) can perform various analyses to identify one, two, or more clinical sites of interest. One analysis that can be performed by System 10 is the identification of conduction patterns by System 10 (e.g., by algorithm 115) using one or more of a variety of pathway-finding techniques, such as identifying streamlines of clinically relevant pathways and / or other techniques; this identification process is referred to herein as “autopathing”. System 10 can perform autopathing by taking a series of streamlines and clustering their traversals using AD (e.g., electrophysiological data and / or biophysical data) to determine descriptive pathways of electrical propagation across a given cardiac chamber (e.g., the atrial body). This process is an extension of streamline-based techniques for identifying virtually any pathway (e.g., given sufficient starting locations). Some analyses performed by System 10 (e.g., by Algorithm 115) can be configured to quantify the spatial distribution and / or temporal occurrence of one or more features of interest, such as to characterize one or more clinical sites of interest. Automated path formation performed by System 10 can operate within a modified coordinate system (e.g., other than Cartesian coordinates). System 10 can represent data in a 2D conformal data space and / or in an anatomically determined data space created from subject-specific anatomy (e.g., universal atrial coordinates). Some analyses performed by System 10 can quantify the spatial distribution and / or temporal occurrence of features of interest, such as to characterize clinical sites of interest. Feature identification includes, but is not limited to: block; isolation; isthmus; breakthrough; and / or epicardial bridging. The feature “block” can represent the disappearance of activity at a location, lacking consistency with juxtaposed sites. The feature “isolation” can represent the location of adjacent tissue that is electrically isolated from another region of tissue (e.g., the pulmonary vein is electrically isolated from the left atrium). The feature “isthmus” can represent a tissue region in which juxtaposed (but not necessarily contiguous) pathological tissue is present, and in which re-entry is more likely to occur. The feature “breakthrough” can indicate a surface location (e.g., a surface, such as the endocardium) where the source of electrical activity has not yet originated at the surface location (e.g., the earliest active site on the surface but not within the structure). The feature “epidermal bridging” can indicate a conductive path of tissue proximal to the tissue that can be vibrated or ablated.

[0168] System 10 can be configured to identify various conduction patterns, such as localized irregular activation (LIA), localized regional activation (LRA), and / or focused patterns. System 10 can be configured to detect and count the occurrence of spatiotemporal conduction patterns anywhere within a chamber (e.g., a cardiac chamber, such as the left atrium) by analyzing spatiotemporal activation sequences present in an activation mapping. At each location within the chamber (each vertex on the grid), each activation at that location can be analyzed by System 10 against the background of adjacent activations within a small surrounding region (at least 5 mm or 10 mm in diameter and / or no more than 25 mm or 15 mm in diameter). Conduction velocity can be calculated based on the activation time within this region. The activation sequence and conduction direction of each beat can be evaluated against one or more sets of rules to classify local conduction patterns. The incidence of each pattern type at each location can be quantified and displayed as a histogram (e.g., a color-differentiated histogram) on an anatomical model (e.g., a shell), where higher incidences at the same location can be visualized using distinguishing visual characteristics (e.g., greater opacity and color intensity). System 10 can simultaneously display visualizations of the incidence of multiple pattern types.

[0169] System 10 can be configured to determine various conduction properties, such as when activation data (AD) is processed to quantify different conduction properties. System 10 can be configured to determine conduction velocities. Conduction velocity through an organization is a highly correlated measure of organizational activity. System 10 can compute local conduction velocities using spatially and / or temporally distributed local activation times. In some embodiments, local activation times on a 3D shell surface mesh can be projected onto a plane, and the spatial gradient of activations in the projected plane can be used to approximate the conduction velocity. Similarly, conduction velocities can be computed by System 10 (e.g., by Algorithm 115) using similar operations (e.g., compute element-wise gradient estimates) arranged element-wise, for example, to selectively enhance performance in certain regions (e.g., increase sensitivity to small spatial structures). Gradient operations can be performed by System 10 using triangulation and / or finite difference methods for estimating gradients. Conduction velocities can be color-coded (e.g., where data is presented in a color-coded or other graphically differentiated arrangement) and / or velocities can be directly displayed. Deceleration can be computed and displayed as a conduction metric by computing the gradient of the conduction velocity. Relative conduction velocity can be determined by system 10, such as a determined conduction velocity (e.g., and provided to the operator) as a percentage change (e.g., decrease and / or increase) and / or a standardized value. Relative conduction velocity can be a useful metric to account for potential inter-patient or inter-map variations in conduction velocity. Generally, identifying the region of maximum acceleration (e.g., maximum deceleration or acceleration) may be more clinically relevant and valuable for diagnosing arrhythmias (e.g., AF) than crossing a specific velocity threshold. Relative conduction velocity can be calculated as a percentage decrease or increase, and / or velocity can be normalized to the fastest percentage of velocity in the map. Wavefront refraction can also be estimated by system 10 (e.g., by algorithm 115) using conduction velocities estimated throughout the chamber. Refraction can be based on the angle between subsequent activations of tissue regions. Using any of these parameters, in conjunction with the activation sequence, the location of the refraction map can thus be estimated by system 10 (e.g., by algorithm 115). System 10 can be configured to determine signal amplitude. For example, system 10 can use the amplitude of a local signal as a measure of conduction. In some embodiments, the peak-to-peak amplitude of a local bipolar signal can be color-coded (e.g., a mapping plot distinguished by changing color and / or other graphical properties). In some embodiments, the peak negative amplitude of a local unipolar signal can be color-coded. In some embodiments, the amplitude of a omnipolar or Laplace operator can be color-coded. In some embodiments, the amplitude of a non-contact calculated charge density signal can be correlated with the amplitude of a contact voltage signal at the same location. This correlation can be used to define a representative relationship between the charge density units and voltage units of the mapping plot. In some embodiments, this relationship can be used to create amplitude mapping plots for both charge density and voltage data types.The representative relationship between charge density calculation and millivolt equivalent can be calibrated by system 10 (e.g., by algorithm 115) using an inversely calculated potential (e.g., the potential of a signal directly sampled near the anatomical body in the blood pool).

[0170] System 10 can be configured to perform spatiotemporal analysis. System 10 can perform a variety of forms of spatiotemporal analysis. The mapping data of System 10 may include a set of spatially connected time-varying signals (electrograms) and / or temporal events (e.g., local activations). System 10 can determine multidimensional activation sequences or patterns. In some embodiments, the spatial distribution of temporal events can be plotted (e.g., and displayed) by System 10 as a multidimensional image, where time is the first dimension, and the spatial distribution of anatomical locations may be three additional dimensions, or can be reduced to fewer dimensions (e.g., by projection onto a 2D parameterized space, or by mapping to universal common coordinates (universal atrial coordinates for atrial EAM)). The reduced data space can also be computed in a conformal data space, where System 10 (e.g., Algorithm 115) uses the mitral valve as the unwinding point. Thus, the time-varying nature of electrical activity can be captured as a spatiotemporally-representative static image (SRSI), where the size of the image is largely dominated by the duration of the mapping data. Therefore, this technology allows system 10 to process highly complex multidimensional datasets using image analysis and / or comparison techniques. In some embodiments, SRSI can be analyzed by system 10 by searching for kernel patterns of a given window size that repeat in other parts of the image over a time dimension. These recurring patterns can be clinically relevant in characterizing activity and targeted therapy. The window size can vary from very small to very large to reprocess the SRSI multiple times to search for possible kernels of different sizes. In some embodiments, system 10 can analyze the SRSI to obtain spatiotemporal coupling relationships between different regions of the anatomy. In the SRSI, activation sequences between two strongly coupled regions of a chamber will follow a common and consistent vector that can be detected by various pattern detection techniques, including machine learning, deep learning, and / or other AI algorithms. In some embodiments, coupling is determined by system 10 (e.g., by algorithm 115) via analysis of temporal variations in spatial correlation.

[0171] System 10 can be configured to perform spatiotemporal analysis by performing network analysis. In some embodiments, the spatiotemporal sequence of activations can be analyzed by System 10 as a network. The network can be formed by interconnected nodes of an anatomy, where adjacent nodes are anatomically adjacent locations, and further separated nodes are further separated along the surface of the anatomy. For any activation at a node, upstream and downstream activations encode coupling relationships between anatomical regions, and downstream activations spread across a large area of ​​the anatomy through this bottleneck, which can be an effective therapeutic target for modifying or eliminating persistent arrhythmias. In some embodiments, each activation at each node can be evaluated within a window (e.g., a window of at least 25 ms or 50 ms, and / or a window of no more than 250 ms or 100 ms), such as to evaluate the region of downstream impact of activation at each node. Regions with larger downstream impacts may be more effective in the persistence of arrhythmias.

[0172] System 10 can be configured to analyze the spatiotemporal sequence of activations by visualizing the activation regions of a reference time. Activation time data in the EAM can be divided into multiple time intervals by System 10 (e.g., by Algorithm 115). The activation region of the reference time can be calculated as a region corresponding to a portion of the EAM having active time within each time interval. Alternatively or additionally, System 10 can use the number of measurements and / or points. The activation region of the reference time can be provided (e.g., visualized) as a curve, for example, as a histogram with one axis representing time and another axis representing the activation region. In some embodiments, System 10 can simultaneously display more than one visualization using the same time axis. Each visualization can show data from a different EAM. Each visualization can alternatively show data from the same EAM but indicate different data types, such as when displaying (e.g., and graphically distinguishing) non-contact data, contact data, unipolar data, and / or bipolar data.

[0173] Similarly, system 10 can be configured to analyze the spatiotemporal sequence of activation via visualization of the activation region of a reference amplitude. The activation time data in the EAM can be divided into amplitude ranges. The activation region of the reference amplitude can be calculated as a region in the EAM corresponding to the portion of the activity time within each time amplitude range. Alternatively or additionally, the number of measurements and / or points can be used. System 10 can display the activation region of the reference amplitude using visualizations similar to those described above.

[0174] System 10 can be configured to perform cardiac information analysis, which includes data aggregation and statistical analysis. A single dataset can be vulnerable to false positives and false negatives, especially when the selected metric might introduce bias solely from the measurement itself. In some embodiments, System 10 includes biases (e.g., user-configurable biases) that cause the analysis to tend towards and / or away from false positives and / or false negatives. The amplitude of a bipolar signal (one unipolar signal minus another) is often used as a substitute for measuring tissue abnormalities and is typically measured only once. However, measurement orientation, wavefront direction, and tissue rate response all affect the amplitude of the bipolar signal, making it a non-specific measure of tissue abnormalities. In some embodiments, System 10 is configured to overcome one or more of these limitations by performing multiple measurements, varying the wavefront direction, and changing the tissue rate response to remove potential inherent biases. Once these multiple measurements are performed (e.g., using System 10), understanding the spatial consistency of any abnormality measure improves the specificity of abnormality detection. System 10 can be configured to composite based on multiple measurements. For example, System 10 can perform multiple measurements under different conditions (activations on one or more mappings). Each measurement may include active data (AD) at a common set of locations (vertices of a grid) on the anatomy. Using multiple measurements (activations in one or more mappings), each location on the anatomy has a composite set of data samples that can be statistically analyzed by System 10. A composite dataset is a collection of multiple datasets aggregated into a generally analyzable structure. In some embodiments, this structure is a vertex grid of the anatomy. In some embodiments, the AD evaluated as a composite is conduction velocity. In some embodiments, the AD evaluated as a composite is signal amplitude. Thresholds can be used to perform statistical analysis or evaluation on the data in the composite mapping. For example, the composite dataset can be used to visualize minimum, average, maximum, and / or median conduction velocities and / or amplitudes at all locations within the heart chambers. System 10 can be configured to perform consistency analysis. For example, composite data can be merged by System 10 by applying thresholds. For example, if a conduction velocity threshold (e.g., a threshold of 0.3 m / s) is used as a threshold for aberrant conduction (slower is generally more aberrant), the composite data can be evaluated by counting any conduction velocities (CVs) in the composite dataset below the threshold as aberrant and / or counting any CVs above the threshold as normal. A consistency mapping map can then be displayed, which visually distinguishes regions with consistent anomalous CVs, consistent normal CVs, or inconsistent anomalous CVs in a color-coded manner. In some embodiments, the CVs are thresholded by system 10 to form the consistency mapping map. In some embodiments, the signal amplitude is thresholded by system 10 to form the consistency mapping map.In some embodiments, the threshold for anomalous activity is combined by system 10 between CV and signal amplitude to form a consistency mapping. In some embodiments, multiple metrics or thresholded metrics may be combined into a score for each activation performed by system 10, and the score may then be displayed in the composite mapping.

[0175] As described above, System 10 can be configured to perform one or more cardiac activation analyses, such as when performing a fusion of clinical measurements and / or computational modeling. System 10 can be configured to perform anatomical data co-registration, such as via a universal anatomical model and / or landmark co-registration (skeleton). Anatomical data co-registration performed by System 10 may include System 10 (e.g., Algorithm 115) that assumes a fairly consistent pairing of the four chambers of the heart and, where subject-specific (i.e., patient-specific) orientations cannot be determined, can determine the relative locations of the various chambers via population-level averages (e.g., averages from a sample of human subjects). The universal anatomical model of System 10 may utilize a global cardiac positioning system to pair the chambers of the heart relative to each other. Landmark co-registration of System 10 may utilize a computed skeleton that tracks the relationships between the four chambers of the heart in 3D space and / or a series of 2D cross-sections, such as to determine the relative orientations and locations between relative chambers. System 10 can be configured to perform “CV aberration / divergence modeling,” for example, when System 10 takes measurements from one or more locations (e.g., clinical pacing measurements) (e.g., using the overcalibrated measurement routines and / or single-location routines described herein). Activations can be mapped and analyzed to find regions of the block (e.g., isolation). System 10 can use the same chamber anatomy (e.g., as previously calculated and / or presented) and computationally apply the regions of the block. The “restitution score” determined by System 10 can include a score determined by analyzing conduction velocities at a series of sites across varying pacing rates, allowing the development of subject-specific recovery information to parameterize the subject-specific simulation or compare the degree of recovery-related changes to a population mean as an index of recovery (e.g., the recovery score). System 10 can use a model of simulated propagation to compute chamber-wide activation sequences. The model can be isotropic. In some embodiments, the model is parameterized by System 10 (e.g., by Algorithm 115) based on measured activations, so that System 10 can start the simulation from a point of change in the measured activations. For example, system 10 can use the top 10% of the measured activations, which can be compared with the remaining 90% of the measured activations for simulation. This process can elicit a number of propagation properties that exhibit anisotropic-like divergence, which can be attributed to fiber orientation or substrate-related variations. Alternatively or additionally, the model can be anisotropic (e.g., having heterogeneous properties) and / or the model can be determined based on population means and / or population atlases. The heterogeneity of the model can be based on: a standard model; and / or data from one or more measurements, such as data obtained from CT and / or MRI. For example, system 10 can use fibrosis scores and / or arrhythmia-inducing scores based on the spatial configuration of intensity and / or substrate-specific variations in CT and / or MRI data.These intensity variations can be elicited via contrast agents or through analysis of standard imaging protocols and / or clinical mapping (e.g., composite mapping of conduction velocities). System 10 can use a series of conduction velocity mappings to estimate fiber orientation across cardiac chambers (e.g., atria), and these estimates can then be used to generate anisotropic simulations that take fiber orientation into account. In some embodiments, the difference between the anisotropic simulation and the measurement tends to indicate that the simulation framework of System 10 does not account for base-related differences. System 10 can compare clinical pacing mappings and simulated propagation to determine differences in conduction behavior. System 10 can calculate directional divergence between clinical and simulated mappings, such as to show preferred conduction directions (e.g., those that may be present in anisotropic fiber orientations) or base-related variations. As described above, the estimates provided by the simulation framework of System 10 (which itself is parameterized by EAM or population mean) can be compared with the measurements themselves, such as to determine areas of maximum divergence that may indicate abnormal tissue. System 10 can calculate aberrations in the clinical mapping (e.g., aberrations that differ from the simulation framework in terms of velocity, recovery, initiation, breakthrough, and / or other propagation-related phenomena). System 10 can perform simulated pacing mapping to locate gaps, for example, to: track delivered treatment and all treatment parameters; model the effect of delivered treatment on local conduction (e.g., local conduction may be unchanged, partially / moderately altered, or completely eliminated (no conduction)); and / or simulate the onset of activation from one or more regions conjunct with the delivered treatment. For example, based on one or more System 10 thresholds (e.g., user-defined thresholds), a set of standard settings, or a combination of both, System 10 can generate electrical propagation simulations (e.g., subject-specific, rule-based, or combined) to track delivered treatment. Additionally, System 10 can be configured to model the effect of delivered treatment on local conduction. Local conduction may be unchanged, partially and / or otherwise moderately altered, or completely eliminated (e.g., no conduction), and so, in the physiological simulations described herein, it can be parameterized by System 10 (e.g., by Algorithm 115). The parameterization performed by System 10 may take into account default geometric parameters (e.g., tissue thickness), electrophysiological parameters (e.g., recovery curve data), and / or anatomical measurement data.

[0176] System 10 may include a “data management architecture”. The data management architecture (DMA) of System 10 may include an arrangement in which information (e.g., captured and / or information related to clinical procedures performed on a patient) is spatially stored. Broadly speaking, the DMA may include a volumetric regular grid large enough to encompass a typical four-chambered heart. The architecture may be formed from regularly structured linear grids, unstructured tetrahedral grids, and / or unstructured hexahedral grids. Data storage may include: one or more commonly referenced data spaces; and / or a hierarchical structure of data spaces (e.g., parent, peer, child, etc.). System 10 may perform data processing in each data space. System 10 may recompile (e.g., reconstruct) data in “child” data spaces, such as recompiling based on the “parent” data space. System 10 may provide visualization of these different data (e.g., measured, deterministic, and / or computed data).

[0177] Treatment and navigation information can be stored by system 10 on an element-by-element basis, such as for further processing and / or analysis performed by system 10 and / or by an operator using system 10. One or more shared reference data spaces (e.g., domains within a DMA) can be computationally correlated with each other, for example, to facilitate two-chamber mapping. In some embodiments, geometric displacement may be more appropriate than physiological displacement, but system 10 can be configured to constrain biophysical activities to reflect the true physiological configuration. These data spaces can be: provided by system 10 (e.g., displayed), blurred, highlighted, and / or enhanced (e.g., according to the needs of the user of system 10).

[0178] A hierarchical structure of data spaces within a data space (e.g., in a parent, peer, child arrangement) can exist, whereby several independent geometries can include dependent attributes that can be inherited based on checks in one geometry (e.g., a portion of a geometry) but not in another. These computational attribute inheritance mechanisms can be governed and facilitated by System 10 through the interrelationships between different geometries. For example, the left and right atria can have a peer relationship, where there is a relatively minimal inheritance of properties. Alternatively, the left atrial appendage can be classified as a child of the left atrium by System 10, and the parent-child relationship can have a more substantial inheritance. Data processing within each data space can utilize DMA to regularize and constrain data manipulation, for example, to facilitate analysis and visualization at the chamber-to-organ level. For example, the DMA can be used to: constrain global electrical solutions across biatrial geometries; constrain geometric manipulations and perform “shaving” of cardiac chambers (e.g., one or both atrial bodies); and / or solve piecewise inverse problems for biatrial optimization.

[0179] The recovered data in the “child” data space based on the “parent” data space can include variations based on specific criteria, and certain parameters can be delegated by system 10 (e.g., by algorithm 115) to peers and / or children of specific geometries, altering the geometry and / or anatomy based on the delegation of measurements and / or parameters.

[0180] System 10's visualization of data may include a volumetric grid used to facilitate 3D visualization, which may be partially associated with the underlying cardiac grid. These visual elements may be scalar, vector, matrix, and / or tensor-based visualizations and / or analyses. Additionally, the visual elements provided by System 10 may utilize variations in color, size, shape, and / or other variable graphical parameters to suggest characteristics of tissues and organs, and / or accuracy and / or confidence in a given metric.

[0181] Establishing a finite element method (FEM) framework for subject-specific or atlas-based simulations can include the use of structured and unstructured meshes, including DMA, and a range of FEM-based analyses. Within DMA, System 10 can perform bidomain, monodomain, pseudo-bidomain, eikonal, reaction-elution domain, and courtemanche-type simulations, for example, when System 10 simulates one or more anatomical chamber models generated by System 10. These computational models can be parameterized by subject-specific AD, and / or the models can be strictly rule-based and / or population-based.

[0182] System 10 can be configured to perform event-driven user interface control and data representation. System 10 can provide "data elements" of information throughout the clinical process, and System 10 can track these data elements, such as when tracking one, two, or more of the following: recorded signals; user actions; clinical events; EAM; delivered treatments; heart rhythm classification; beat groups; beat characteristics; and combinations thereof. Each data element can be stored along with time information (e.g., timestamps). Data elements can be displayed in a timeline in chronological order. Data elements of different data types (e.g., classified by algorithm 115 of System 10) can be displayed synchronously in the timeline. For example, data collection, beat groups, created EAMs, delivered treatments, markers, collected anatomy, case events, and / or time gauges can be shown in different parallel tracks in the synchronous timeline. Each track can be graphically distinguished (e.g., color-coded or otherwise graphically distinguished) in correspondence with the user interface representation in and / or for the remainder of System 10. System 10 operations can be initiated via one or more specific user actions (e.g., clicking an icon or pressing a hotkey), which can minimally record time and / or visualize benchmarks on the timeline as "events." Additional information can be added to the event, such as text labels and / or notes. The event can be modified into other forms of data, including time calipers, EAM, bounce labels, bounce groups, anatomical markers, and / or tags. In review, the user (e.g., a clinician or other operator) can return to the event on the timeline and observe the system environment (e.g., system parameter levels, patient physiological data, and / or other information) that existed and / or was otherwise relevant at that moment in the clinical procedure. System 10 operations initiated through the user interface of System 10 can also be recorded as events on the timeline, with corresponding attributes of the operation automatically applied to the event. User actions and changes can also be stored as events, including actions and changes such as modifications to application settings (e.g., calculation parameters), applied filters, creation of data entities (e.g., anatomical parts, jump groups, text labels, and / or graphic markers), and changes to anatomical data (e.g., shaving, cutting, and / or adding anatomical parts). Event information can also be displayed as a corresponding log or list. In some embodiments, the time-series representation of event data can be used to undo or redo user actions via a user interface (such as the graphical user interface (GUI) 125 of System 10 described herein), such as by dragging an indicator to exclude a previous user action or by clicking a previous action and deleting it. Undoing a previous event can be performed on a consecutive set of events that led to the current state of the system. Events can be deleted asynchronously, and if the deletion of an event asynchronously requires that associated events also be deleted, System 10 can notify the user and visually specify the associated events before deletion.

[0183] The applicant has conducted studies to evaluate the safety and efficacy of the systems and methods described herein for treatment. The applicant has conducted a study to evaluate the outcomes of treating non-pulmonary vein (non-PV) trigger sites (e.g., trigger sites identified by System 10 using the trigger mapping techniques described herein). Treatment of trigger sites has been shown to improve arrhythmia recurrence-free rates compared to untreated premature atrial contractions (PACs). Despite technological advancements, AF ablation success rates at 12 months remain suboptimal, ranging between 38% and 64%. The mechanism underlying AF-induced arrhythmias has been theorized to involve interactions between AF triggering factors and abnormal matrix. The applicant has compiled a prospective registry of AF patients who underwent trigger mapping using the mapping catheter 200 of System 10 and the methods described herein. In the study scenario, following pulmonary vein antral isolation (PVAI), an infusion of 20 µg / ml (over 10 minutes) of positive inotropic agent was initiated. PACs occurring at a frequency of at least five times per minute were treated by ablation, and the impact of treating these sites on acute, short-term, and long-term outcomes was evaluated.

[0184] Of the 54 study patients, non-PV triggering factors were identified in 48 patients and ablated in 45 patients. Of these 48 patients, 27 were newly diagnosed after PVI+, and 21 were repeat patients. Trigger sites were localized to 3D models (e.g., generalized biatrial models, such as...). Figure 19(As described in the preceding and subsequent sections of this document), and the conduction velocity (CV) at the trigger site was compared with that of the rest of the atrium. In 45 patients whose trigger sites were ablated, the cumulative distribution of trigger density across biatrial anatomy showed a median trigger density of 1 Tr. / cm, with less than 1% of the anatomy at the highest trigger density (e.g., greater than 10 Tr. / cm), and 37% of the anatomy having zero triggers. Further analysis of the study data was performed on 36 of the 45 patients, where both trigger mapping and sinus rhythm mapping were recorded by System 10 during the study period (e.g., left atrial mapping identifying the trigger site and left atrial mapping during sinus rhythm, respectively). This study found that the CV at the trigger site (mean CV approximately 0.49 m / s) was significantly lower (e.g., approximately 26% slower) than the CV of the rest of the atrial tissue (mean CV approximately 0.66 m / s). The region of interest at the trigger site was defined as within 1 cm of the center of the trigger site. The applicant concluded that patient-specific ablation targeting non-PV-triggered events is crucial because trigger-matrix events have strong physiological coupling and diverse localization. Targeting these trigger sites can improve the understanding of AF pathophysiology and enhance the targeted nature of arrhythmia treatment.

[0185] In some embodiments, system 10 includes dynamic reference routines, as described herein. In some embodiments, system 10 is configured to curve-fit a positioning catheter (e.g., a model based on a “B-spline,” as described herein). In some embodiments, system 10 is configured to implement virtual position reference (“VPR”), as described herein. In some embodiments, system 10 is configured to perform hybrid positioning, such as positioning based on impedance and magnetic positioning patterns, as described herein. In some embodiments, system 10 may include an anatomical engine, as described herein. In some embodiments, system 10 may be configured to create and / or update hybrid mapping maps of cardiac electrical data using contact and non-contact data, as described herein. In some embodiments, system 10 is configured to perform trigger mapping, such as mapping based on conduction velocity and / or trigger density, as described herein.

[0186] Figures 2 to 3 below Figure 21 Various examples of the above-described inventive concept are shown.

[0187] Now for reference Figure 2A-F illustrates various embodiments of mapping catheter arrays consistent with the inventive concept. In some embodiments, the mapping catheter 200 of system 10 is configured to record anatomical data such that system 10 can generate one or more anatomical models of cardiac chambers (e.g., left atrium), as described herein. The mapping catheter 200 can also be configured to record electrical activity (e.g., biopotential data) such that system 10 can generate one or more mapping maps of cardiac electrical activity, as described herein. In some embodiments, the mapping catheter 200 is configured to record anatomical data using contact mapping (e.g., where one or more portions of array 210 contact cardiac tissue to record anatomical location data) and to record biopotential data using non-contact mapping (e.g., using inverse kinematics as described herein). In some embodiments, catheter 200 does not include ultrasound elements, e.g. Figure 1 Transducer 212 is shown. Mapping catheter 200 may include a steerable catheter, such as a catheter constructed and arranged to provide unidirectional or bidirectional directional control. In some embodiments, mapping catheter 200 is compatible with sheaths as small as 8.5F. In some embodiments, mapping catheter 200 is configured to record data in the atria, ventricles, or both atria and ventricles of the heart. In some embodiments, mapping catheter 200 includes one or more electrodes (e.g., electrode 211). NC These electrodes are physically prevented from contacting tissue. For example, one or more electrodes 211 NC It can be recessed, and / or it can be located on portions of array 210 that are unlikely to contact tissue, for example, when one or more electrodes 211 NC When located inside the outer boundary formed by other parts of the mapping catheter 200 (e.g., on the inner surface of the spline 213 of the array 210). One or more electrodes 211 prevent contact with tissue. NC It can be used for signal measurement, signal delivery, as a biopotential reference electrode, as a position reference electrode, and / or as a positioning measurement electrode. For example, as a biopotential reference electrode, electrode 211 NC It can be used as a common subtraction reference (e.g., the electrode can provide a signal used as a subtraction reference by system 10) to process signals measured from other electrodes 211 of the mapping catheter 200. In some embodiments, electrode 211 NCUsed as a unipolar reference for electrocardiography, it can be used to reduce far-field cardiac signals measured on other electrodes 211 (e.g., all other electrodes 211). The position and / or spacing of the electrodes 211 on spline 213 allows the mapping catheter 200 to use a first electrode pattern and distribution for a first purpose in a first configuration and a second electrode pattern and distribution for a second purpose in a second configuration. For example, in the first configuration, the mapping catheter 200 can be fully deployed for both non-contact and contact mapping, thanks to the uniform spatial distribution of the electrodes 211. In the second configuration, the mapping catheter 200 can be partially deployed (e.g., when the array 210 is partially extended from the sheath 360), such that the distally exposed electrodes 211 of the array 210 form an electrode pattern optimal for contact mapping and / or treatment delivery (e.g., treatment including radiofrequency ablation and / or pulsed field ablation).

[0188] Figure 2A An embodiment of an array 210 of mapping catheter 200 is shown, which is constructed and arranged to transform from a flat “scraper” configuration (shown on the left) to a 3D basket-like configuration (shown on the right). In some embodiments, array 210 includes a flat plate configured to “roll up” into a basket shape. Alternatively, array 210 may include a plurality of splines configured to rotate about a central axis, such that the splines can be arranged in a single plane or rotated to form a basket shape. Array 210 may be positioned at the distal end of shaft 201 and includes the electrode 211 shown.

[0189] Figure 2B An embodiment of an array 210 of a mapping catheter 200 is shown, the array 210 comprising a bulbous shape. The distal end of the array 210 may include a large surface area for contacting tissue. For example, the distal end of the array 210 may include a surface area of ​​at least 1 cm², such as at least 4 cm², or at least 9 cm². In some embodiments, the shape of the array 210 can be derived from a sheath (e.g., sheath 360 is not shown, but...). Figure 1 Modulation is achieved by retracting and / or expanding the array (as described elsewhere herein). Alternatively or additionally, axis 201 may include a sheath from which array 210 can be expanded.

[0190] Figure 2CAn embodiment of an array 210 of mapping catheters 200 is shown, which is constructed and arranged to transform from a basket-like shape to a “flower-like” shape. The flower-like shape shown can be achieved by retracting the distal end of the array 210 inward, causing the array to bend inward, as illustrated. In some embodiments, each “petal” of the flower-like shape of the array 210 is substantially in the same plane. The flexibility and / or mechanical compliance of each spline 213 (e.g., the flexibility of each petal) can be maximized to allow the array 210 to deform, thereby minimizing the forces applied to the tissue and / or preventing tissue stretching caused by contact with the array 210.

[0191] Figure 2D An embodiment of an array 210 of a mapping catheter 200 is shown, comprising a near-spherical shape composed of a plurality of splines 213. The splines 213 may be arranged in a helical pattern as shown, forming a “twisted basket” shape. The shape of the array 210 can be modulated by drawing a wire and / or by unfolding or retracting from a sheath (e.g., when the splines 213 of the array 210 comprise a shape memory material, such as a nickel-titanium alloy). The number and / or spacing of the splines 213 can optimize the spatial distribution of the electrodes 211 for non-contact mapping, contact mapping, and / or a combination of contact and non-contact mapping, for example by uniformly distributing the spacing of the electrodes 211 across the array 210 (e.g., when the array 210 comprises a spherical or elliptical shape). In some embodiments, the array 210 may comprise 9, 16, 24, 32, or 48 electrodes 211. The optimized spatial distribution may include irregular and / or asymmetrical spacing along the splines 213 and / or on adjacent splines 213 to achieve the minimum number of electrodes required for uniform coverage. This can be advantageous for non-contact mapping using a minimum number of electrodes 211. Optimized spatial distribution can include spacing along splines 213 such that the distance between electrodes 211 on adjacent splines is consistent, for example, to maximize the number of electrode pairs with equal spacing (e.g., a spacing of approximately 1 mm or 2 mm). The position and / or spacing of the electrodes 211 on splines 213 can allow the electrodes 211 to be nested to effectively fold the array 210 into an unfolded geometry, for example, such that the array 210 can be slidably housed within a sheath (e.g., sheath 360 of system 10). In some embodiments, the position and / or spacing of the electrodes 211 on splines 213 allows one or more portions of splines 213 to deform differently from other portions, for example, when one or more portions of splines 213 are more easily deformable and / or deformable in an optimized direction (e.g., based on the position of one or more electrodes 211).

[0192] Figure 2EAn embodiment of an array 210 of a mapping catheter 200 is shown, which includes a single filament, such as a shape-setting line, configured to transform into a 3D shape as it extends from a shaft 201. In some embodiments, the set shape includes a square, a shape coil (e.g., a tapered coil), or a "stirring ball" shape.

[0193] Figure 2F An embodiment of an array 210 of mapping catheter 200 is shown, which includes a plurality of annular arms (including splines 213) configured to extend from different points on the distal portion of shaft 201, as shown.

[0194] Now for reference Figure 3A -E shows a graph of data recorded by a cardiac mapping system consistent with the inventive concept. System 10 can be configured to implement virtual position reference, as described herein. This virtual position reference can provide improvements in sensitivity and / or responsiveness to positioning performed by system 10. In some embodiments, the virtual position reference of system 10 can provide improved maintainability for one or more components of system 10, for example, due to its real-time adaptability and simple relationship between the same ECG used for respiratory compensation and drift compensation. About Figures 3A-3C , Figure 3A The diagram shows a combination of recorded ECG signals, such as ECG signals recorded using electrodes from lead 350 and / or using a diagnostic catheter 330. Figure 3B It shows Figure 3A The signal is generated by low-pass filtering of the signal (e.g., a drift-related signal, as described herein). Figure 3C It shows Figure 3A The signal is generated by high-pass filtering of the signal (e.g., a breathing-related signal, as described in this article). Figures 3A-3E The signals in the diagram correspond to: V1, V2, V1+V2, V3, V1+V3, V2+V3, and V1+V2+V3. V1 and V2 correspond to the right ventricular ECG leads, while V3 corresponds to the diaphragmatic ECG lead. CS corresponds to the signal recorded via a device placed in the coronary sinus. Figure 3D The method for determining which leads to use to estimate respiratory motion is shown. Figure 3E It shows Figure 3B signal pair Figure 3A Regression fitting of the signal is used to determine drift correction.

[0195] In some embodiments, the virtual location reference provided by system 10 can adapt in real time to different physiological events (e.g., breathing patterns, coughing, apnea, electrode patch removal, pressure applied to the patient, esophageal movement, electronic interference, etc.).

[0196] In some embodiments, the virtual location reference provided by system 10 can be configured to automatically identify a specific ECG lead from a set of multiple ECG leads for use as a system reference.

[0197] In some embodiments, the virtual position reference provided by system 10 can be configured to adjust the operation of system 10 based on the specific type of system 10 components used, such as based on the specific type of mapping catheter 200, treatment catheter 310, functional catheter 320, and / or diagnostic catheter 330 used. For example, system 10 can provide different types of catheters 200, 310, 320, and / or 330, such as catheters with different lengths, different stiffness or other properties, different numbers and / or different arrangements of sensors (e.g., electrodes and / or ultrasound sensors) and / or transducers (e.g., electrodes and / or ultrasound transducers) and / or other differences. System 10 can be configured to select one or more ECG leads (e.g., lead 350), internal catheters (e.g., devices 200 and / or 300), or combinations thereof to model respiration. Alternatively or additionally, system 10 can be configured to select one from a set of training time periods to learn a respiration model on one or more catheters, depending on the specific type of catheter used.

[0198] In some embodiments, the virtual location reference provided by system 10 can be configured to switch (e.g., seamlessly switch) between different positioning arrangements that can be performed by system 10 (e.g., between magnetic-based positioning and impedance-based positioning).

[0199] The virtual position reference of System 10 may include various functions, such as: reference; data quality assurance; stability; and combinations of one, two, or three of these functions. The reference may include: cardiac compensation; respiratory compensation; drift correction; interference recovery; and combinations of one or more of these. The reference may include maintaining the consistency of the position reference and using it as a reference for localization (e.g., all localizations) performed by System 10. Cardiac compensation may be performed via low-pass filtering of recorded cardiac motion and / or via prediction of underlying dynamic processes (e.g., Kalman filtering). Respiratory compensation may be performed via modeling of respiratory motion, for example using a source S and a training T. The source S may be an ECG lead (e.g., lead 350), an internal catheter (e.g., device 200 and / or 300), and / or a combination thereof. The training T may include a specified period of time during which the catheter remains stable. Drift correction may be performed by correcting for globally monotonically variable impedance variations at sub-fc frequencies by restoring the current impedance measurement to a previous measurement without offset and physical manipulation. Interference recovery can be performed via isolating transient interference, via a fixed baseline impedance offset, or both. Interference recovery and / or other data correction performed by system 10 can be specific to the type of catheter used (e.g., a specific type of catheter 200, 310, 320, and / or 330). Stability can include detecting impedance quality issues and determining when to switch between an ECG-based reference (e.g., a reference based on signals from electrodes at patch 340 and / or lead 350) and a reference based on a set of internal electrodes (e.g., a reference based on electrodes at the inserted catheters 200, 310, 320, and / or 330). In some embodiments, magnetic-based positioning and / or hybrid positioning (e.g., positioning performed using both impedance-based and magnetic-based positioning) are used to perform and / or improve drift correction, interference recovery, data quality assurance, and / or stability.

[0200] System 10 can be configured to place stationary internal electrodes (e.g., electrodes of a diagnostic catheter 330 located within the body, such as within the coronary sinus). Figure 3A The signal is separated into its low-pass (LP) and high-pass (HP) components, which represent "drift" and "breathing," respectively. The drift and breathing components of the internal electrodes are estimated using signals from the electrodes (e.g., electrodes at patch 340 and / or lead 350). For drift, such as... Figure 3B As shown, system 10 uses a training window (e.g., 5 minutes) and associates stationary electrodes on the body surface (e.g., electrodes of patch 340 and / or lead 350) with a position-referenced catheter (PRC) (e.g., diagnostic catheter 330). For respiration, as... Figure 3CAs shown, system 10 uses a training window (e.g., 60 seconds) to model the position reference duct. System 10 can be configured to begin generating initial respiratory compensation before the training window time is reached (e.g., 12 seconds after start). System 10 can be configured to perform this arrangement of drift and respiratory compensation in real time without using buffering. Therefore, system 10 can quickly adapt to transient events, and inaccurate or bad data will only affect the reference during the event.

[0201] In some embodiments, system 10 is configured to perform respiratory compensation in such a way that when sigma (adaptive) is low, a high-pass version of the original signal from the position reference catheter (PRC) is estimated using multiple ECGs (e.g., eight ECGs) recorded by leads 350. In some embodiments, sigma adaptation includes a measure indicating the degree of inconsistency in the respiratory model across various ECG lead combinations. System 10 periodically (e.g., at steady time intervals) repositions the PRC. When the linear least squares (LLS) solution diverges (sigma is high), system 10 uses the LLS solution with the highest correlation to the PRC until sigma decreases again.

[0202] exist Figure 3D The diagram illustrates respiratory compensation. System 10 can be configured to perform drift correction using ECG data (e.g., recorded by lead 350) for a predetermined duration (e.g., 5 minutes) to construct a low-pass drift signal (e.g., for each linear combination of ECGs) Figure 3B Principal component analysis (PCA) was performed on System 10. System 10 used an ECG index in the regression fit, which was based on which ECGs were used for respiratory compensation (see reference). Figure 3D If applicable, system 10 applies null plane correction in the x-direction, fits a line, and uses the slope of that line for drift correction (see reference). Figure 3E These steps allow drift correction to be piecewise linear.

[0203] Now for reference Figure 4 This shows a 3D diagram of the recorded electrode positions, consistent with the concept of the present invention. Figure 4 The recording location of a 20-electrode catheter (e.g., a functional catheter 320 comprising 20 functional elements 329, each including one electrode) is shown. The recorded signal shows the catheter in a double-loop configuration, and a best-fit line (the spiral shown) is added to aid in visualization of the double loops. In the example shown, the best-fit line is a third-order B-spline.

[0204] In some embodiments, system 10 is configured to provide images (e.g., images of 3D models) of various catheters (e.g., linear catheters) as a guide for the operator (e.g., images of mapping catheter 200, treatment catheter 310, and / or functional catheter 320). In some embodiments, system 10 implements a base spline (“B-spline”) to determine the best-fit shape of a catheter that has been located by system 10. The mathematical form of the model may depend on the number of feasible positioning electrodes and / or the type of catheter, as described below. For example, for non-ablation catheters, such as multi-electrode linear and / or multi-electrode loop catheters (e.g., functional catheter 320), if three or more electrodes are available (e.g., three or more electrodes are located), a B-spline can be fitted to the located electrode locations. In some embodiments, the fidelity of the B-spline to the positioning electrode locations is controlled by an “order” variable. For example, a “nominal order 3” produces a combination of matching quadratic polynomials that is not limited to any positioning location (except for the two endpoints). Increasing the order makes the model smoother but results in a larger root mean square offset relative to the positioning electrodes. A “nominal order 2” produces straight line segments between the electrodes. The B-spline model can be preserved computationally (e.g., via two endpoints) or scaled to a known physical length of the catheter. In some embodiments, one or more icons (e.g., icons representing electrodes) are displayed on the model in the correct relative positions, indicating the electrode locations of the displayed catheter. In some embodiments, if the model is scaled to the correct length (e.g., the known length of the displayed catheter), the icons are separated by the correct physical distance.

[0205] In some embodiments, the mathematical form of the model applied to the ablation catheter electrodes (e.g., the electrodes of treatment catheter 310) may depend on the number of feasible positioning electrodes (e.g., the electrodes currently positioned by system 10). For example, if electrodes 2-3-4 are all feasible, a B-spline can be applied, and the tip can be extended from electrode 2 along the tangent of the B-spline at electrode 2 by a known physical length separating electrodes 1 and 2. Alternatively or additionally, if only two electrodes are feasible (e.g., electrodes 2-3, 2-4, or 3-4), a straight line segment can be fitted, and the tip can be projected along the tangent from the most distal available electrode.

[0206] In some embodiments, system 10 is configured to allow a user to display one or more positioning catheters in various modes. For example, system 10 allows a user to display the catheter by selecting one of the following options: as an original electrode; as an original line segment; as a fitted rigid model; and / or as a fitted stretching model. For example, when the user selects to display the positioning information as an original electrode, system 10 can display the catheter by displaying the original positions of the electrode points and connecting them with straight lines. When the user selects to display the positioning information as an original line segment, system 10 can display the catheter by placing the electrodes in their respective original positions and connecting the electrodes with straight line segments. System 10 can use second-order B-splines (e.g., linear line segment fitting) to generate the straight lines. When the user selects to display the positioning information as a fitted rigid model, system 10 can use a fitted smooth curve to display the catheter while constraining the length to be fixed and matching the physical electrode spacing and / or overall size of the catheter. System 10 can use third-order B-splines (e.g., quadratic fitting) rendering methods to generate the smooth curve. Alternatively or additionally, System 10 may use Flex Poly and Bezier models to fit curves to the positioning information. In some embodiments, System 10 includes one or more default settings for displaying various catheter types. For example, an ablation catheter (e.g., treatment catheter 310) may be modeled by default using a rigid version of B-spline rendering for all non-tip electrodes, while projecting beyond the distal tip of the second electrode and the catheter segment (e.g., as described herein). When the user selects to display the positioning information as a fitted stretched model, System 10 may display the catheter using a fitted smooth curve while maintaining the electrodes defined by their precise positioning locations (e.g., the position of each electrode is always measured). System 10 may use a third-order B-spline (e.g., quadratic fitting) rendering method to generate the smooth curve. Alternatively or additionally, System 10 may use Flex Poly and Bezier models to fit curves to the positioning information. The fitted stretched model may make the catheter appear stretched as it passes through the septum or sheath. In some embodiments, the fitted stretched model is the model that System 10 defaults to for all linear and loop catheters.

[0207] Now for reference Figure 5 This illustrates a voxel grid with positioning electrode locations consistent with the inventive concept. In some embodiments, system 10 is configured to perform voxelization on a 3D space, such as a 3D region of one or more catheters of positioning system 10 within a patient's body. Voxelization performed by system 10 may include confidence levels and / or uncertainty measures, as described herein. Figure 5A 3D voxel mesh is shown, with the positioned electrode locations located within the mesh. The 3D voxel mesh comprises three levels of mesh frames (from coarse to fine). In some embodiments, the system 10 is configured to display an orthogonal 3D mesh (similar to...). Figure 5 The 3D voxels shown represent the areas where one or more devices of the system 10 are currently located (e.g., the locations of one or more catheters of the system 10 within the patient's body), and / or represent the areas where one or more devices were previously located (e.g., one or more areas sampled by moving the catheters).

[0208] A 3D mesh can include multiple mesh levels defined in physical space based on the reliability of field estimation and / or the local density of the positioning points (e.g., the density of points where one or more electrodes are positioned). In some embodiments, the 3D mesh includes at least three mesh levels. The mesh levels can be arranged in a nested sequence such that each "box" of the coarsest level (e.g., each 3D cuboid) contains eight boxes of the next level (e.g., ...). Figure 5 (As shown). In some embodiments, the boxes represent different physical dimensions, such as boxes with side lengths of 2 mm, 4 mm, 8 mm, and / or 16 mm. In some embodiments, the smallest (fineest) box is displayed in front of the coarser levels. For example, as the catheter moves in space and is positioned by system 10, the coarsest box is highlighted if the catheter is positioned in a space with a lower density of other positioning points, and second- and / or third-level boxes are highlighted as the number of nearby positioning points increases (e.g., when the catheter moves into a space with other positioning points with a higher density). In some embodiments, the 3D voxel implementation described herein enables the user to identify which parts of the space have well-defined field estimates. Voxelization information can be used (e.g., by system 10) to create various 3D geometries representing the positioning space, and / or to perform offset and / or drift corrections (e.g., correcting impedance and / or magnetic positioning data).

[0209] Now for reference Figure 6 A model of an unfolded basket-shaped conduit consistent with the concept of this invention is shown. Figure 6 A digital model of the array 210 of the mapping catheters 200 is shown, all of which are referenced Figure 1And described elsewhere in this document. The model shows array 210 fully deployed, with electrodes 211 and transducers 212 positioned on deployed splines 213. The ultrasound vector of transducer 212 is also shown. System 10 can be configured to calculate information related to a patient's anatomy and / or cardiac electrical activity based on data recorded from mapping catheter 200. For example, system 10 can be configured to reconstruct anatomical boundaries, estimate field scaling, calculate cardiac biopotentials (e.g., using an array-mediated forward-inverse method to calculate biopotentials), and / or perform one or more combinations of these. To accurately calculate this information, the shape of array 210, including the orientation of transducers 212, can be determined (e.g., estimated) by system 10.

[0210] In some embodiments, system 10 may be configured to determine the shape of spline 213 and associate the determined shape with the known positions and orientations of electrode 211 and transducer 212. The deployed or collapsed state of array 210 (e.g., based on the shape of spline 213 in the deployed state) may be determined by system 10 by measuring the positional distance between two axially mounted position sensors (e.g., functional element 219, which includes a magnetic sensor); these sensors are located on a movable central axis of conduit 200 and a fixed conduit axis proximal to array 210. In some embodiments, the movable central axis is configured to retract into the fixed axis to deploy array 210 (e.g., when the distal portion of spline 213 is fixedly attached to the distal portion of the movable axis, and the proximal portion of spline 213 is fixedly attached to the distal portion of the fixed axis). In this configuration, the distance between the two position sensors decreases as array 210 deploys and increases as array 210 collapses.

[0211] In some embodiments, system 10 includes a lookup table that associates each distance between two position sensors with a maximum array width. For example, system 10 may include a lookup table that operates under the assumption that the shape of each spline 213 is approximately represented by two cubic splines. In some embodiments, the cubic splines are slightly modified by incorporating a sinusoidal perturbation that vanishes at the proximal and distal ends and at the boundary between the two segments. The width of array 210 can vary at each spacing of the position sensors until the integral length of the resulting cubic spline matches the known length of spline 213. The physical length of spline 213 remains constant, establishing a unique association between the width of array 210 and the shape of spline 213 with each spacing of the measured position sensors. In some embodiments, using the determined spline shape, system 10 is configured to place electrodes 211 and / or transducers 212 on a digital model by matching the known positions of electrodes 211 and / or transducers 212 with the integral distance along the modified cubic spline. The orientation of transducer 212 can be determined based on the slope of the cubic spline modified at the transducer's location.

[0212] Now for reference Figures 7 to 11 The diagram shows a block diagram of various modules of a catheter navigation system consistent with the concept of this invention, and a screenshot of the system's GUI. Figure 7 A block diagram of the main routines of the "navigation engine algorithm" (e.g., the algorithm in algorithm 115) of system 10 is shown. The navigation engine algorithm may include four main routines, such as the "positioning engine routine," "reference engine routine," "heart rhythm tracker routine," and "data flow routine," which are as follows: Figure 7 As shown. The positioning engine routine can be configured to update the positioning field and position one or more devices to a 3D location, as described herein. The reference engine routine can provide offset, drift, and / or respiratory and cardiac compensation, as described herein. The heart rhythm tracker routine can be configured to track cardiac and / or respiratory phases, for example, to perform gating, as described herein. The data flow routine can provide positioning data to one or more other modules or components of the system 10. In some embodiments, the data flow routine is configured to combine data provided by the positioning engine routine, the reference engine routine, and / or the heart rhythm tracker routine to apply reference and / or gating to the mapping data.

[0213] In some embodiments, system 10 is configured to operate in a navigation mode that incorporates various optimization methods, such as magnetic optimization, catheter shape optimization, and / or basic impedance optimization. In some embodiments, the reference engine routine includes a VPR module configured to provide offset correction, drift correction, respiratory compensation, and / or cardiac compensation, such as reference... Figure 1In some embodiments, the heart rhythm tracker routine is configured to provide cardiac phase estimation and / or respiratory gating information. In some embodiments, the navigation engine routine is configured to provide a 3D voxel grid displaying confidence levels and / or uncertainty measures, such as a reference grid. Figure 5 As described elsewhere herein. In some embodiments, system 10 is configured to store impedance values ​​measured across one or more devices on a regular grid in 3D space, and then convert the measured impedance values ​​to locations in 3D space. In some embodiments, the grid is defined to have a voxel spacing of no more than 12 mm, for example no more than 6 mm, and / or no more than 3 mm. In some embodiments, system 10 is configured to use a nonlinear field function to track inhomogeneities in the local impedance field, for example, tracking inhomogeneities generated by one or more veins and / or tissues near the patient's lungs. In some embodiments, system 10 is configured to generate local field estimates by moving and tracking an unfolded basket catheter (e.g., mapping catheter 200 as described herein) within the field; moving and tracking a magnetically guided catheter within the field; and / or moving and tracking both the unfolded basket catheter and the magnetically guided catheter within the field (e.g., moving a catheter within a patient's heart chamber, which is located within the field). In some embodiments, system 10 is configured to provide scaling (e.g., field scaling), as described herein, that provides accurate impedance-based scaling without magnetism and / or without any predetermined conduit movement operations to perform scaling (also referred to as “scaling operations”). The scaling provided by system 10 may rely on successive basket-shaped field scaling to determine impedance scaling information for a particular region.

[0214] Figure 8 A schematic diagram of a routine for the navigation engine algorithm is shown, illustrating the data flow from routine to routine. The navigation engine algorithm can process data recorded by system 10 (e.g., data recorded via hardware of system 10 and / or preprocessed by the firmware of system 10), including impedance positioning data, bioelectric potential data, and / or magnetic positioning data. This data can be processed by performing reference, rhythm, and / or field updates, and the updated data can be provided to other components and / or algorithms of system 10. As shown, the “main_update” frame illustrates the processing of data from acquisition to field estimation update. The “get_locData” frame illustrates the process of retrieving data from the navigation engine algorithm (e.g., from other components of system 10).

[0215] Figures 9-11 A screenshot of the GUI provided by the navigation engine algorithm of System 10 is shown. Figure 9 This is a screenshot showing the information displayed when the field is analyzed (e.g., analyzed to determine a field estimate). The white dots shown represent the current field estimate. Figure 10A screenshot shows the information displayed when analyzing a field using a magnetically enhanced conduit (e.g., the conduit of system 10 that includes both electrodes and coils for impedance-based and magnetism-based positioning, respectively). The displayed information includes voxelization of the magnetic sampling performed. Figure 11 A screenshot shows information displayed to the user, including options for gating, compensation, and / or catheter selection (e.g., selecting which of a set of available catheters will be used for field analysis and / or used as reference catheters). Field analysis can be performed using impedance and / or magnetic data recorded from basket catheters (e.g., mapping catheters 200 including array 210) and / or other impedance and / or magnetic guidance tubes of system 10. This data is used to calculate field estimates, by... Figure 9 The white dots shown represent [the data]. In some embodiments, the displayed GUI information includes one or more timemaps, such as timemaps showing respiratory phases and / or ECG recordings, which can provide information for gating the recorded data (e.g., to perform the gating procedures described herein). The timemaps allow the user to adjust the upper and / or lower limits of the respiratory gating procedure. Additionally or alternatively, the timemaps allow the user to adjust the start and / or end times of the cardiac gating procedure. In some embodiments, system 10 is configured to allow the user to select which specific catheters are displayed, and / or select which specific catheters are used to collect data for field analysis. System 10 may be configured to allow the user to select one or more compensation and / or gating parameters, such as parameters used for field scaling, visualization, and / or mapping procedures described herein.

[0216] Now for reference Figure 12 The diagram illustrates an algorithmic approach for generating a digital model of a patient's anatomical structure, consistent with the inventive concept. System 10 may include an "anatomy engine" algorithm (e.g., the algorithm in algorithm 115 of system 10 described herein). The anatomy engine algorithm may be configured to process data recorded by various catheters and other devices of system 10 to generate an editable digital model of a portion of the patient's anatomical structure, such as a digital model of the left atrium. Figure 12 A schematic diagram of the anatomy engine algorithm is shown, including the expected inputs and outputs of the algorithm. For example, the anatomy engine algorithm may receive inputs (e.g., data inputs), including catheter location data, ultrasound data, and / or user input commands. The anatomy engine algorithm may be configured to output a visual geometric model of the anatomical structure (e.g., when the model is configured to be displayed to a user via a display of system 10, such as the display of user interface 120 described herein).

[0217] In some embodiments, the domain in which the catheter of system 10 is located is discretized into a regular grid. Sampling of this grid by system 10 can be regular, irregular, uniform, or non-uniform, and the grid's location can be static and / or dynamic. As described herein, the discretized domain can be referred to as a "grid." Cells within the grid can be referred to as "voxels." Anatomical estimates of a patient's anatomy can be referred to as a "shell" when referring to a single anatomical estimate, and as "multiple shells" when referring to multiple anatomical estimates. In some embodiments, system 10 is configured to generate an anatomical model using an "exploration-based" approach. In these embodiments, a shell (e.g., a shell wall) can represent the transition between an explored (e.g., a location where a portion of the catheter of system 10 has been located and positioned) and an unexplored (or "unexplored") spatial volume. When the entire cardiac chamber of the patient (e.g., the left atrium) has been explored, the shell represents the transition between the blood pool and myocardial tissue (e.g., the shell represents the wall of the cardiac chamber).

[0218] In some embodiments, system 10 is configured to associate one or more data identifiers (e.g., labels, markers, or other tags) with one or more anatomical points represented by an anatomical model. These data identifiers may be associated with one or more anatomical shells, may be stored in a voxel mesh associated with the shell, and may be voxel-based. In some embodiments, the data identifiers may be assigned to individual anatomical points by a user of system 10. Alternatively or additionally, system 10 may automatically assign one or more data identifiers, for example using an automatic labeling algorithm, such as an algorithm based on a universal atrial coordinate system and / or other manifold parameterization methods (e.g., spectral decomposition). Atrial coordinates can be constructed using a mesh Laplacian operator with Dirichlet boundary conditions established at automatically and / or manually determined anatomical boundaries (e.g., boundaries including the mitral valve and / or fossa ovalis). The mesh Laplacian operator can provide a smooth and parameterizable transition between one or more boundaries. Alternatively or additionally, a smooth and parameterizable transition may be achieved using Eikonal-based functions, thermal diffusion, or fast-margin formulas. In some embodiments, boundary conditions at some or all boundaries are calculated as Neumann conditions. In some embodiments, during exploration-based anatomy construction, the anatomy engine algorithm is configured to track exploration points relative to previously generated shells to determine which parts of the mesh are inside one or more other continuous anatomy structures (e.g., other continuous shells) and which parts are outside them. In some embodiments, each shell may inherit one or more data identifiers from previously generated shells.

[0219] In some embodiments, system 10 is configured to use an ultrasonic catheter (e.g., using transducer 212 of mapping catheter 200, see reference 200). Figure 1 The system 10 generates anatomical models from collected points (as described elsewhere in this document). The system may include the anatomical engine algorithm described herein, configured to process the recorded data and generate various anatomical models. In some embodiments, the anatomical engine algorithm post-processes the recorded ultrasound data (e.g., via an ultrasound post-processing algorithm) to identify and / or filter out non-physiological signals (e.g., ultrasound echoes recorded by transducer 212) recorded during anatomical data collection. The ultrasound post-processing algorithm can produce more accurate point clouds (e.g., point clouds with fewer erroneous points), requiring less manual editing by the user to produce accurate anatomical models.

[0220] In some embodiments, ultrasound post-processing may include identifying backscattering from suspended particles. For example, a certain percentage of short-range ultrasound echoes (e.g., echoes from distances less than 6 mm) may originate from backscattering of suspended particles in the blood pool. For example, "rouleaux" or transient quasi-linear aggregates of erythrocytes may have the correct size to reflect ultrasound signals (e.g., 10 MHz signals) emitted by transducer 212. Rouleaux structures form and dissipate in response to local blood shear forces (e.g., a decrease in local blood shear forces) and may be particularly pronounced during atrial fibrillation. The ultrasound post-processing algorithm may be configured to reduce rouleaux-mediated backscattering by ignoring ultrasound point cloud points with a computational range (e.g., distance from transducer 212) less than 6 mm.

[0221] In some embodiments, ultrasound post-processing may include identifying echoes from one or more surfaces of catheter 200. For example, transducers 212 that are at least partially unbonded (e.g., not properly bonded to) spline 213 of catheter 200 may allow ultrasound signals to propagate inward (e.g., toward the center of array 210), resulting in echoes being received from portions of the array (e.g., from the central axis of catheter 200). In some embodiments, these echoes correspond to distances ranging from 7 mm to 15 mm, depending on the deployed state of array 210. In some embodiments, the ultrasound post-processing algorithm may be configured to periodically (e.g., per thousand samples, or approximately every 12 seconds at a sampling rate of 78.125 Hz) calculate the cumulative distribution function (CDF) for each transducer 212. If the CDF exhibits sufficiently rapid change within a target distance (e.g., between 7 mm and 15 mm), the ultrasound post-processing algorithm may assume that the transducer is not properly bonded to spline 213 (e.g., the transducer has delaminated from the spline). If it is determined that transducer 212 has been layered, then subsequent data collected from the layered transducer will be rejected and will not be added to the point cloud.

[0222] In some embodiments, ultrasound post-processing may include filtering out anomalous data caused by electromagnetic interference (e.g., electromagnetic interference caused by other devices on system 10 and / or other devices near system 10 during clinical procedures, such as other devices or equipment within the EP laboratory). For example, a certain percentage of short-range echoes (e.g., echoes related to a distance of less than six millimeters) may originate from electromagnetic interference, such as interference from an intravenous IV pump or other equipment near the patient. The ultrasound post-processing algorithm may be configured to reject any echoes related to a distance of less than six millimeters, thereby filtering out unwanted electromagnetically mediated data.

[0223] Now for reference Figure 13 The diagram illustrates a set of steps in a method for converting one anatomical model surface type to another, consistent with the inventive concept. See also: Figure 13A and 13B This illustrates a 3D “internal polyhedron” method for classifying dense volumetric data, consistent with the inventive concept. In some embodiments, the anatomy engine algorithm of System 10 is configured to convert dense volumetric data into anatomical surfaces (e.g., shells), and / or sparse volumetric data (e.g., point cloud data) into anatomical surfaces. In some embodiments, the anatomy engine algorithm is configured to perform Kazhdan-Poisson (KP) reconstruction techniques to generate anatomical models, such as those described in the co-pending U.S. patent application filed January 23, 2024, entitled “Tissue Treatment System”, Serial No. 18 / 291,340. Figure 13 Four steps are illustrated for converting a KP model surface into an "alpha" model surface (e.g., a volumetric mesh-based surface). A KP model may comprise a model based on point cloud data (e.g., ultrasound data collected by System 10 for constructing the anatomical model). Converting a KP model to an alpha model allows for the addition and / or subtraction of anatomical surfaces at certain locations. For example, for anatomical models generated using non-exploratory methods (e.g., imaging-based and / or ultrasound-based methods as described herein), System 10 can be configured to convert the anatomical model to a type that allows the model's shell to inform the internal volume (e.g., as in models generated using exploratory methods), such as mesh-based models as described herein. In some embodiments, the point cloud-based shell (e.g., a KP surface) is converted to a mesh-based anatomy using an interior polyhedron method, where the KP surface is superimposed on a mesh (e.g., via an anatomy engine algorithm), such as... Figure 13A and Figure 13B As shown. The dissection engine algorithm of System 10 then estimates the portion of the mesh inside the KP surface (e.g., Figure 13A (as shown) and the portion outside the KP surface (e.g., Figure 13B (As shown). Once it is determined whether each mesh point is inside or outside the KP surface, various mesh-based surface generation techniques can be implemented by the dissection engine to create alpha surfaces. In some embodiments, the mesh-based surface generates a continuous mesh, where triangular surface mesh points (e.g., mesh vertices) are also part of the underlying mesh. This structure includes corresponding points between the mesh and the surface, such that the internal domains of the voxelized structure directly correspond to the implicit surface, and can be used to create discrete volume functions from any part of the manifold (e.g., the dissected shell) without searching on the underlying mesh.

[0224] Now for reference Figure 14 This illustrates a user interface for an anatomical model of one or more chambers of a patient's heart, consistent with the concept of this invention. See also... Figures 15A-15D This illustrates a schematic example of user interaction with an anatomical model consistent with the inventive concept. In some embodiments, the GUI 125 of system 10 can be configured as follows: Figure 14 As shown, the GUI 125 allows users to manage anatomical models of one, two, or more chambers of a patient's heart, such as the right atrium, left atrium, right ventricle, and / or left ventricle. In some embodiments, the GUI 125 enables users to edit one or more anatomical models, such as segmenting, annotating, and / or otherwise labeling one or more parts of the anatomy. For example, users can specify various anatomical features to be displayed in a unique manner (e.g., with unique colors, textures, or opacities, and / or using other graphical labeling methods). In some embodiments, labeling may include "z-painting," where user interaction displays the image on the model surface. Figure 15A An example of z-painting is shown. Additionally or alternatively, the label may include “contour drawing”, where the path is estimated from two or more coarse control points (e.g., points placed by the user). Figure 15B An example of contour drawing is shown. In some embodiments, GUI 125 enables a user to edit an anatomical model by “shaving” the model, where the user can convert interior points of the mesh to exterior points via one or more interactions with the model. In some embodiments, surface normals are used (e.g., inferring the exterior from the interior) to estimate a 3D function. Mesh points within the 3D function can be converted from interior to exterior and / or from exterior to interior. In some embodiments, the 3D function includes a Gaussian function, a Laplacian function, a delta function, and / or other parametric functions whose directionality can be determined by 3D directionality. Figure 15C An example of smoothing is shown. In some embodiments, the GUI125 allows the user to define a "region alpha" portion, where the user can control the surface resolution of individual parts of the anatomical model, for example... Figure 15DAs shown. User-specified labels can be used to form sub-surfaces, and then the anatomy engine algorithm can use a 3D in-polyhedron approach to determine which portions of the voxel mesh are within each sub-surface. These sub-surfaces can then be re-subdivided, and their material properties can be changed individually before being merged with one or more other sub-surfaces. In some embodiments, these material properties include mesh resolution, alpha shape radius, transparency, connectivity, lighting, conductivity, cell model, and / or tissue properties.

[0225] Now for reference Figure 16 An example of domain partitioning consistent with the inventive concept is shown. In some embodiments, the dissection engine algorithm of system 10 is configured to generate surfaces from dense volumetric data, for example using a reference... Figure 12 Dense volumetric data collected using exploration-based methods described elsewhere in this document. A densely defined mesh is used, where each element of the mesh (e.g., each voxel) includes a definition, and the algorithm can use labels to partition one or more domains of interest. For example, in a densely defined mesh, there exists a boundary between mesh portions defined as “inner” and mesh portions defined as “outer.” The algorithm can be configured to estimate triangular surfaces at this boundary. For example, a surface can be created using an alpha shape method, which includes a sphere and pivot method over the outermost extent of the inner mesh structure, where the user can define the traversal of the inner volume and define the radius of the resulting “sphere” surface. Changing the “alpha radius” can affect the fidelity of the surface mesh to conform to the volume structure in regions of curvature. For example, using a large alpha radius in a high-curvature region will produce a surface that does not conform to the inner mesh. Alternatively or additionally, a “isocontouring” (e.g., “traveling cube”) method can be used to create surfaces. In some embodiments, the traveling cube method can be used to find the outermost extent of the inner voxels, thereby producing triangular surfaces at this boundary.

[0226] Now for reference Figure 17 The image shows a rendering of a portion of a cardiac mapping system consistent with the inventive concept, highlighting a portion of the graphical user interface. In some embodiments, the GUI 125 of system 10 can be configured as follows: Figure 17As shown, and may include a heart rhythm classification interface. The heart rhythm classification interface can display data recorded and processed by system 10 to the user, such as indicating and / or helping the user distinguish between atrial fibrillation and atrial flutter. In some embodiments, system 10 is configured to track heart rhythms (e.g., continuously tracking heart rhythms while system 10 records cardiac data). System 10 can track various rhythms, such as ventricular activation, atrial activation, and / or types of atrial rhythms (e.g., atrial fibrillation, atrial tachycardia, and / or atrial flutter). Identification of atrial rhythm types tracked by system 10 can be performed using data recorded from one or more internal and / or external devices of system 10, such as ECG data recorded using lead 350 and / or biopotential data recorded using mapping catheter 200. In some embodiments, algorithm 115 of system 10 includes a machine learning algorithm configured to identify atrial rhythm types. System 10 can be configured to track respiratory rhythm using ECG data (e.g., ECG data recorded from lead 350) and / or impedance localization data. In some embodiments, system 10 is configured to group (e.g., classify and group) the recorded atrial beats, for example, by beat morphology and / or cycle length classification. System 10 can adjust the importance balance between morphology and cycle length classifications (e.g., automatically and / or by the user). In some embodiments, system 10 has a default balance, such as a 50 / 50 balance between cycle length and morphology-based atrial beat classifications. System 10 can be configured (e.g., in extreme cases of weight adjustment) to use classifications based solely on morphology or solely on cycle length (e.g., morphology only or cycle length only).

[0227] In some embodiments, system 10 may generate automated suggestions for heart rhythm types (e.g., to help a user identify atrial rhythms). System 10 may use one or more fixed metrics, such as a range of cycle lengths or the RR interval between portions of the QRS complex of a heartbeat. Additionally or alternatively, system 10 may use statistical data, data mining, machine learning, and / or predictive analytics to analyze the recorded signals. For example, the recorded signals may be compared with one or more models or libraries of previously recorded and classified data by algorithm 115. In some embodiments, the data may be processed using wavelet transform, which is configured to decompose each waveform into one or more frequency bands while preserving temporal information to produce a wavelet scalar graph. In some embodiments, image-based features of the scalar graphs of the beats are evaluated by an algorithm using a convolutional neural network trained on a labeled library of scalar graph images where the rhythm type is known. System 10 may be configured to classify various rhythms, including: atrial flutter; atrial tachycardia; atrial fibrillation; sinus rhythm; paced rhythm; ventricular tachycardia; and combinations of these. In some embodiments, when the system 10 identifies a repetitive rhythm (e.g., sinus rhythm, flutter, or tachycardia), beat clustering can be performed on one or more maps for each identified rhythm type (e.g., performed by algorithm 115).

[0228] Now for reference Figures 18A-18D and Figure 19 Various mapping plots and graphs associated with cardiac activity data recorded by a cardiac mapping system, consistent with the inventive concept, are shown. In some embodiments, system 10 is configured to locate the “trigger location” of a single beat, such as a transient phenomenon with slow conduction. Sites of triggering activity include conduction significantly slower than in other cardiac tissues. This information can be used to stratify the arrhythmogenicity of the trigger location, identify locations to be treated (e.g., using ablation or other therapeutic modalities), and / or confirm artifacts from physiological activation. System 10 can be configured to calculate conduction velocities in a local or distributed manner on an element-wise and / or node-wise basis. Figures 18A-18D Various data recorded by system 10 are shown in relation to conduction velocity and velocity at representative trigger locations. Figure 18A Activation sequences from representative trigger sites are shown, with their associated conduction velocities superimposed on an anatomical model. In some embodiments, the data may be color-coded. Additionally or alternatively, the data may be scaled, for example, when conduction velocity data is scaled based on measured conduction velocities. Figure 18B A graph showing conduction velocities outside and inside the trigger location (e.g., the area near the trigger site, or the "region of interest" surrounding the trigger location). Figure 18CA summary graph showing median conduction velocities estimated across multiple patients (e.g., data from multiple patients diagnosed and / or treated by System 10, such as in one or more clinical studies performed by the applicant). Figure 18D A graph of CDF for trigger density across anatomical structures is shown. Figure 19 The diagram shows a rear view and a front view (PA and AP, respectively) of an anatomical mapping displaying trigger site density. The data shown includes a visualization of 151 trigger sites mapped on the generalized biatrium (labeled with trigger site density per square centimeter (Tr. / cm)). The trigger site density on the biatrium is calculated for each vertex of the anatomical structure by summing the number of trigger sites within a 1 cm radius. In some embodiments, the trigger site density can be calculated on a surface using geodesic thermal methods. For example, population-level density can be used for population-level assessment of individual trigger sites. In some embodiments, individual trigger sites are used to determine, based on anatomical information, whether treatment (e.g., ablation) is likely to be effective at treating the trigger site.

[0229] Now for reference Figure 20A and Figure 20B Schematic and illustrative examples of processes for deriving deceleration mapping and refractory period mapping of cardiac data, consistent with the inventive concept, are shown. In some embodiments, system 10 includes one or more data analysis tools (e.g., one or more algorithms and / or routines configured to analyze data recorded by system 10). In some embodiments, the data analysis tools are configured for “offline” use, such as not during clinical surgery, for example, for further analysis of data recorded during one or more clinical surgeries after clinical surgery. In some embodiments, system 10 includes data analysis tools configured to generate deceleration mapping and / or refractory period mapping. For example, deceleration mapping can be derived by system 10 by calculating the absolute difference in conduction velocity magnitude and amplitude between two cycle lengths for each element (e.g., each voxel of an anatomical model). Refractory period mapping can be derived by system 10 by calculating the absolute difference in conduction velocity direction for each element. Figure 20B An example of a conduction velocity mapping recorded during pacing is shown, along with deceleration and refractory period mappings derived from the conduction velocity data. The patient who recorded the data had persistent atrial fibrillation prior to pulmonary vein isolation, which could be induced with atrial flutter after pulmonary vein isolation. This data was recorded while the patient was being paced to sinus rhythm.

[0230] Now for reference Figure 21The image shows a screenshot of a GUI provided by a cardiac mapping system consistent with the inventive concept. In some embodiments, system 10 is configured to provide continuous and / or semi-continuous mapping of repeatable rhythms. System 10 may be configured (e.g., via algorithm 115) to automatically detect time windows of atrial activation and cluster data recorded within these windows into beats with similar morphology and / or cycle length, as described herein. System 10 may update one or more mappings of recorded cardiac electrical activity generated from data including similar beats. In some embodiments, system 10 is configured to create and / or update mappings of cardiac electrical activity using non-contact data, contact data, and / or both non-contact and contact data (e.g., in a hybrid manner), for example, by referring to... Figure 1 As described elsewhere in this document. In some embodiments, the anatomical model created by system 10 (as described herein) is improved to ensure sufficient mesh quality and data density to facilitate the mapping of cardiac bioelectric potential data recorded from contact and / or non-contact sources, thereby producing a mesh with high element quality (e.g., uniform element shape and size, smooth transitions between adjacent elements, and / or “consistent” surface normals all pointing in the same direction).

[0231] Figure 21 An example of presenting a GUI 125 to a user to view continuous mapping and / or triggered mapping performed by system 10, consistent with the inventive concept, is shown. In some embodiments, system 10 is configured to automatically initiate the creation of cardiac chamber mapping maps upon detection of an irregular rhythm (e.g., upon detection of fibrillation). In some embodiments, system 10 continuously performs inverse kinematics calculations (e.g., as described herein) while recording biopotential data. In some embodiments, activation times are determined using unipolar and / or bipolar electrograms recorded by system 10. System 10 may be configured to continuously and / or semi-continuously perform beat binning (e.g., continuously while recording biopotential data). In some embodiments, while mapping cardiac chambers (e.g., atria), system 10 may identify the presence of electrical activity generated within the atria and remove that activity from the recorded data. System 10 may be configured to generate one or more mapping maps from continuous and / or discrete spaces using a forward matrix approach (e.g., as described herein). Rows of the forward matrix may be pre-computed on a pre-specified grid for compilation during mapping map generation. In some embodiments, the calculation of the non-contact mapping map can be regularized using spatial methods, such as Laplace regularization and / or graphical Fourier decomposition of the spatial frequency components, and truncated at a user-selected spatial frequency.

[0232] The above embodiments should be understood as illustrative examples only; other embodiments are also contemplated. Any feature described herein with respect to any embodiment may be used alone or in combination with other described features, or in combination with one or more features of any other embodiment, or in any combination of any other embodiment. Furthermore, equivalents and modifications not described above may be employed without departing from the scope of the inventive concept, as defined by the appended claims.

Claims

1. A dynamic display system of cardiac information, comprising: one or more electrodes configured to record a set of electrical potential data representative of cardiac activity at a plurality of time intervals; and a cardiac information console comprising: a signal processor configured to: calculate a set of cardiac activity data at the plurality of time intervals using the recorded set of electrical potential data, wherein the cardiac activity data is associated with surface locations of one or more heart chambers; and a user interface module configured to display information related to the cardiac activity data presented relative to a graphical representation of the surface of the one or more heart chambers.

2. The system of claim 1 and / or any one or more of the other claims herein, further comprising: a dynamic reference routine, wherein the dynamic reference routine is configured to mitigate the effects of impedance artifacts and / or motion artifacts.

3. The system of claim 1 and / or any one or more of the other claims herein, further comprising: one or more catheters, each catheter comprising at least one electrode of the one or more electrodes, wherein the system is configured to position the one or more catheters, and wherein the system is further configured to determine a best fit shape of at least one of the one or more catheters positioned by the system.

4. The system of claim 3 and / or any one or more other claims herein, wherein, the system implements B-splines to determine the best fit shape.

5. The system of claim 1 and / or any one or more other claims now pending, wherein, the system is configured to implement a virtual position reference, and wherein the virtual position reference comprises a position reference anchor and one or more data corrections.

6. The system of claim 1 and / or any one or more other claims now pending, wherein, the system is configured to perform positioning in a hybrid impedance-based positioning mode.

7. The system of claim 6 and / or any one or more other claims herein, wherein, the hybrid impedance-based positioning mode is configured to perform positioning from magnetic-based positioning data and impedance-based positioning data.

8. The system of claim 1 and / or any one or more other claims now pending, wherein, the graphical representation of the surface of the one or more heart chambers comprises an editable digital anatomic model, and wherein the system is configured to generate the editable digital anatomic model.

9. The system of claim 1 and / or any one or more other claims now pending and / or as attached hereto, wherein, the system is configured to record sets of electrical potential data from contact and non-contact sources.

10. The system of claim 1 and / or any one or more other claims now pending and / or as attached hereto, wherein, the information related to the cardiac activity data comprises one or more trigger locations.

11. The system of claim 10 and / or any one or more other claims herein, wherein, the one or more trigger locations comprise locations where conduction velocity is slower than healthy heart tissue.

12. The system of claim 1 and / or any one or more other claims herein, wherein, the system is further configured to determine various conduction properties.

13. The system of claim 12 and / or any one or more other claims herein, wherein, processing activation data to quantify different conduction properties.

14. The system of claim 1 and / or any one or more other claims herein, wherein, the system is further configured to perform spatiotemporal analysis.

15. The system of claim 14 and / or any one or more other claims herein, wherein, the system is configured to perform multiple forms of spatiotemporal analysis.

16. The system of claim 1 and / or any one or more other claims herein, wherein, the system is further configured to analyze spatiotemporal sequences of activation by visualizing activation regions of a temporal reference.

17. The system of claim 1 and / or any one or more other claims herein, wherein, the system is further configured to analyze spatiotemporal sequences of activation via activation region visualization of an amplitude reference.

18. The system of claim 1 and / or any one or more other claims herein, wherein, the system is further configured to perform cardiac information analysis comprising data aggregation and statistical analysis.

19. The system of claim 1 and / or any one or more other claims herein, wherein, the system is further configured to perform one or more cardiac activation analyses.

20. The system of claim 19 and / or any one or more other claims herein, wherein, the system is configured to perform the one or more cardiac activation analyses in conjunction with performing clinical measurements and / or computational modeling.

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