System for ablation zone prediction

Abstract

An ablation system includes a computing device and an ablation device configured to ablate a target. The computing device is configured to generate a three-dimensional model based on functional respiration imaging data of a patient and predict an ablation zone based on the functional respiration imaging data.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of the filing date of U.S. Provisional Patent Application No. 63 / 412,731, filed on October 3, 2022. Technical Field

[0003] The present disclosure relates to systems, methods, and apparatus for predicting ablation zones during microwave ablation therapy procedures. Background Art

[0004] When planning a therapeutic procedure, clinicians often rely on patient data, including X-ray data, computed tomography (CT) scan data, magnetic resonance imaging (MRI) data, or other imaging data that allows clinicians to view the patient's internal anatomy. Clinicians use patient data to identify targets of interest and develop strategies for approaching the targets of interest for therapeutic procedures.

[0005] Existing imaging modalities present unique challenges when monitoring the progression of the ablation zone during an ablation procedure. Ultrasound imaging shows "bubbles" or ground-glass opacities within the lung as energy is delivered, making it difficult to determine the edges of the actual ablation zone as energy application progresses. CT imaging provides "near-time" imaging that reflects the size of the ablation zone at the time of the last scan capture, but does not reflect the growth of the ablation occurring in real time.

[0006] Additionally, predicting the outcome of an ablation procedure (eg, final ablation zone characteristics) is difficult because one standard data set used to predict the outcome of an ablation procedure may not be applicable to multiple patients and / or multiple targets. Summary of the invention

[0007] Systems and methods are provided for predicting ablation zones during microwave ablation therapy procedures.

[0008] According to one aspect of the present disclosure, an ablation system includes a computing device and an ablation device configured as an ablation target. The computing device is configured to receive functional respiratory imaging data of a patient and generate a three-dimensional model of the patient's lungs based on the received functional respiratory imaging data of the patient. The computing device is further configured to predict lung movement based on the received functional respiratory imaging data of the patient and predict movement of the ablation device relative to the lungs and the target during a predetermined ablation duration. The computing device predicts an ablation zone position and boundary relative to the target based on the received functional respiratory imaging data of the patient and the predicted movement of the ablation device relative to the lungs and the target during a predetermined ablation duration.

[0009] In one aspect, the computing device is configured to calculate an optimal path for the ablation device to approach the target based on the received functional respiratory imaging data of the patient, the optimal path producing a maximum ablation margin. The calculated optimal path may include a path through the cavity network and a puncture point on the airway wall to place the ablation device outside the cavity network.

[0010] In one aspect, the computing device is configured to calculate an optimal position for final placement of the ablation device relative to the target based on the received functional respiratory imaging data of the patient, the optimal position producing a maximum ablation margin. The calculated optimal position may be outside the airway of the lung.

[0011] In one aspect, the computing device is configured to receive post-operative data corresponding to actual ablation zone locations and boundaries and compare the post-operative data corresponding to the actual ablation zone locations and boundaries with the predicted ablation zone locations and boundaries to calculate deviations between the actual ablation zone locations and boundaries and the predicted ablation zone locations and boundaries. The computing device can be configured to execute a learning algorithm to learn other predictions of ablation zone locations and boundaries from the calculated deviations.

[0012] In one aspect, the functional respiratory imaging data may include data corresponding to at least one of: vascular volume, airway volume, lung volume, airway resistance, internal airflow distribution, ventilation, perfusion, air trapping, aerosol deposition, fissure integrity, fibrosis, or emphysema.

[0013] According to another aspect of the present disclosure, an ablation system includes a computing device configured to receive functional respiratory imaging data of a patient's lungs and a target inside the lungs, predict lung movement based on the received functional respiratory imaging data of the patient, predict movement of an ablation device relative to the lungs and the target during a predetermined ablation duration, and predict an ablation zone position and boundary relative to the target based on the received functional respiratory imaging data of the patient and the predicted movement of the ablation device relative to the lungs and the target during the predetermined ablation duration.

[0014] In one aspect, the computing device is configured to generate a three-dimensional model of the patient's lungs based on received functional respiratory imaging data of the patient.

[0015] In one aspect, the computing device can be configured to calculate an optimal path for the ablation device to approach the target based on the received functional respiratory imaging data of the patient, the optimal path will produce a maximum ablation margin. The calculated optimal path can include a path through the cavity network and a puncture point on the airway wall to place the ablation device outside the cavity network.

[0016] In one aspect, the computing device is configured to calculate an optimal position for final placement of the ablation device relative to the target based on the received functional respiratory imaging data of the patient, the optimal position producing a maximum ablation margin. The calculated optimal position may be outside the airway of the lung.

[0017] In one aspect, the computing device is configured to receive post-operative data corresponding to actual ablation zone locations and boundaries and compare the post-operative data corresponding to the actual ablation zone locations and boundaries with the predicted ablation zone locations and boundaries to calculate deviations between the actual ablation zone locations and boundaries and the predicted ablation zone locations and boundaries. The computing device can be configured to execute a learning algorithm to learn other predictions of ablation zone locations and boundaries from the calculated deviations.

[0018] In one aspect, the functional respiratory imaging data may include data corresponding to at least one of: vascular volume, airway volume, lung volume, airway resistance, internal airflow distribution, ventilation, perfusion, air trapping, aerosol deposition, fissure integrity, fibrosis, or emphysema.

[0019] According to another aspect of the present disclosure, an ablation system includes a computing device. The computing device is configured to: receive functional respiratory imaging data of a patient's lungs and a target inside the lungs, predict an ablation zone position and boundary relative to the target based on the received functional respiratory imaging data of the patient, receive postoperative data corresponding to an actual ablation zone position and boundary, and compare the postoperative data corresponding to the actual ablation zone position and boundary with the predicted ablation zone position and boundary to calculate a deviation between the actual ablation zone position and boundary and the predicted ablation zone position and boundary.

[0020] In one aspect, the computing device is configured to execute a learning algorithm to learn other predictions of ablation zone locations and boundaries from the calculated deviations.

[0021] In one aspect, the functional respiratory imaging data may include data corresponding to at least one of: vascular volume, airway volume, lung volume, airway resistance, internal airflow distribution, ventilation, perfusion, air trapping, aerosol deposition, fissure integrity, fibrosis, or emphysema.

[0022] Any of the above-described aspects and embodiments of the present disclosure may be combined without departing from the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The objects and features of the disclosed systems and methods will become apparent to those of ordinary skill in the art when reading the description of the various embodiments of the disclosed systems and methods with reference to the accompanying drawings, in which:

[0024] Figure 1is a schematic diagram of a microwave ablation surgical system according to an illustrative aspect of the present disclosure;

[0025] Figure 2 According to one aspect of the present disclosure, Figure 1 A schematic diagram of a computing device as a part of a microwave ablation surgical system;

[0026] Figure 3 An example user interface showing a predicted ablation zone according to an aspect of the present disclosure is shown; and

[0027] Figure 4 is a flow chart illustrating a method for predicting an ablation zone according to an aspect of the present disclosure. DETAILED DESCRIPTION

[0028] The present disclosure provides a system and method for predicting the ablation zone during a microwave ablation therapy procedure using functional respiratory imaging data. Existing lung ablation systems rely on fixed lookup tables to determine the power and time settings required to produce ablation. This can lead to variability because the lungs have a vasculature and alveoli filled with air and fluid. The presence of tumors further increases variability, depending on the morphology. When confirmed during surgery or after surgery through confirmation scans, the ablation zone may be much smaller than expected, causing the physician to change the plan during surgery, increase the time and frustration of the surgery, or cause the physician to determine that another surgery is needed to completely ablate the target.

[0029] The present disclosure describes a system and method for understanding the movement of the lungs in 3D using multiple data sets (e.g., multiple lung volume scans during different breathing phases), combined with other parameters obtained through functional respiratory imaging. Functional respiratory imaging can provide insights into data factors such as vascular volume, airway volume, lung volume, airway resistance, internal airflow distribution, ventilation / perfusion, air trapping, aerosol deposition, fissure integrity, fibrosis and / or emphysema. Using this information, the ablation system takes into account the movement of the lungs, and the parameters obtainable through functional respiratory imaging are input into a predictive model to determine how and where to place an ablation device (e.g., a flexible ablation catheter) to produce a maximum ablation boundary. The model also takes into account predictions of the movement of the ablation catheter relative to the target and other lung structures that may affect the ablation zone during the ablation procedure.

[0030] Although the present disclosure will be described in terms of specific illustrative embodiments, it will be apparent to those skilled in the art that various modifications, rearrangements and substitutions may be made without departing from the spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

[0031] Figure 1An electromagnetic navigation (EMN) system 10 is depicted that is configured to examine CT image data to identify one or more targets, plan a path to the identified targets (planning phase), navigate an extended working channel (EWC) 12 of a catheter guide assembly 40 to the targets (navigation phase) via a user interface, and confirm placement of the EWC 12 relative to the targets. One such EMN system is the ELECTROMAGNETIC NAVIGATION BRONCHOSCOPY® system currently sold by Medtronic. The targets may be tissue of interest identified by examining the CT image data during the planning phase. After navigation, a medical instrument (such as a biopsy tool or other tool) may be inserted into the EWC 12 to obtain a tissue sample (or perform treatment) from tissue located at or near the target.

[0032] like Figure 1 As shown, the EWC 12 is part of a catheter guide assembly 40. In practice, the EWC 12 is inserted into a bronchoscope 30 for accessing the luminal network of a patient "P". Specifically, the EWC 12 of the catheter guide assembly 40 can be inserted into a working channel of the bronchoscope 30 to navigate through the luminal network of the patient. The distal portion of the EWC 12 includes a sensor 44. The position and orientation of the sensor 44 relative to a reference coordinate system and, therefore, the position and orientation of the distal portion of the EWC 12 within an electromagnetic field can be derived. The catheter guide assembly 40 is currently marketed by Medtronic under the trade names SUPERDIMENSION® Surgical Kit or EDGE TM Surgical kits are marketed and sold and are believed to be usable with the present disclosure.The system 10 and its components are described in more detail below.

[0033] The EMN system 10 generally includes an operating table 20 configured to support a patient "P"; a bronchoscope 30 configured to be inserted into the airway of the patient "P" through the mouth of the patient "P"; a monitoring device 120 coupled to the bronchoscope 30 (e.g., a video display for displaying video images received from a video imaging system of the bronchoscope 30); a tracking system 50 including a tracking module 52, a plurality of reference sensors 54, and a transmitter pad 56; and a computing device 100 including software and / or hardware for facilitating identification of a target, path planning to a target, navigation of a medical device to a target, and placement of the EWC 12 and / or confirmation of placement of an appropriate device through the EWC 12 and relative to the target.

[0034] This particular aspect of the system 10 also includes a fluoroscopic imaging device 110 capable of acquiring fluoroscopic images or x-ray images or videos of the patient "P". Fluoroscopic data (e.g., images, image series, or videos) captured by the fluoroscopic imaging device 110 can be stored within the fluoroscopic imaging device 110 or transmitted to the computing device 100 for storage, processing, and display. In addition, the fluoroscopic imaging device 110 can be moved relative to the patient "P" so that images can be acquired from different angles or perspectives relative to the patient "P" to create a fluoroscopic video through a fluoroscopic scan. The fluoroscopic imaging device 110 can include a single imaging device or more than one imaging device. In embodiments including multiple imaging devices, each imaging device can be a different type of imaging device or the same type of imaging device.

[0035] The computing device 100 may be any suitable computing device including a processor and a storage medium, wherein the processor is capable of executing instructions stored on the storage medium. The computing device 100 may further include a database configured to store patient data, a CT data set including a CT image, a fluoroscopic data set including fluoroscopic images and videos, a navigation plan, and any other such data. Although not explicitly shown, the computing device 100 may include an input terminal or may be configured to receive the CT data set, the fluoroscopic images / videos, and other data described herein. In addition, the computing device 100 includes a display configured to display a graphical user interface.

[0036] With respect to the planning phase, the computing device 100 utilizes previously acquired CT image data to generate and view a three-dimensional model of the airway of the patient "P", enabling identification of targets on the three-dimensional model (automatically, semi-automatically, or manually), and allowing determination of a path through the airway of the patient "P" to tissue located at and around the target. More specifically, the CT images acquired from the previous CT scans are processed and assembled into a three-dimensional CT volume, which is then used to generate a three-dimensional model of the airway of the patient "P". The three-dimensional model may be displayed on a display associated with the computing device 100, or in any other suitable manner. Using the computing device 100, various views of the three-dimensional model or enhanced two-dimensional images generated by the three-dimensional model are presented. The enhanced two-dimensional images may have some three-dimensional capabilities because they are generated from three-dimensional data. The three-dimensional model may be manipulated to facilitate identification of targets on the three-dimensional model or two-dimensional image, and a suitable path (e.g., a route to be followed during navigation) may be selected through the airway of the patient "P" to approach tissue located at the target. Once selected, the path plan, the three-dimensional model, and the resulting images may be saved and exported to the navigation system for use during the navigation phase(s).

[0037] With respect to the navigation phase, registration of the image and navigation path may be performed using a six-degree-of-freedom electromagnetic tracking system 50, although other configurations are contemplated. The tracking system 50 includes a tracking module 52, a plurality of reference sensors 54, and a transmitter pad 56. The tracking system 50 is configured to be used with the sensor 44 of the catheter guide assembly 40 to track its electromagnetic position within an electromagnetic coordinate system.

[0038] The transmitter pad 56 is positioned beneath the patient "P". The transmitter pad 56 generates an electromagnetic field around at least a portion of the patient "P" within which the locations of the plurality of reference sensors 54 and sensor elements 44 can be determined using the tracking module 52. One or more of the reference sensors 54 are attached to the chest of the patient "P". The six degree-of-freedom coordinates of the reference sensors 54 are sent to the computing device 100 (which includes appropriate software) where they are used to calculate a patient reference coordinate system. Registration, as described in detail below, is typically performed to coordinate the location of the three-dimensional model and two-dimensional images from the planning phase with the airway of the patient "P" as viewed through the bronchoscope 30, and to allow the navigation phase to proceed with precise knowledge of the location of the sensors 44, even in portions of the airway that the bronchoscope 30 cannot reach.

[0039] Registration of the position of the patient "P" on the transmitter pad 56 is performed by moving the sensor 44 through the airway of the patient "P". More specifically, as the EWC 12 moves through the airway, data related to the position of the sensor 44 is recorded using the transmitter pad 56, the reference sensor 54, and the tracking module 52. The shape generated by the position data is compared with the internal geometry of the passage of the three-dimensional model generated in the planning stage, and the positional correlation between the shape based on the comparison and the three-dimensional model is determined, for example, using software on the computing device 100. In addition, the software also identifies non-tissue space (e.g., air-filled cavities) in the three-dimensional model. The software aligns or registers the image representing the position of the sensor 44 with the three-dimensional model and the two-dimensional image generated from the three-dimensional model, which images are based on the recorded position data and the assumption that the sensor 44 remains located in the non-tissue space in the airway of the patient "P". Alternatively, a manual registration technique can be utilized by navigating the bronchoscope 30 with the sensor 44 to a pre-specified position in the lungs of the patient "P" and manually associating the image from the bronchoscope with the model data of the three-dimensional model.

[0040] Once the patient “P” is registered to the image data and the pathway is planned, a user interface is displayed in the navigation software that lists the path the clinician needs to follow to reach the target.

[0041] Once the EWC 12 has successfully navigated close to the target depicted on the user interface, the EWC 12 is in place as a guide channel for guiding medical instruments to the target, including but not limited to optical systems, ultrasound probes, marker placement tools, biopsy tools, ablation tools (i.e., microwave ablation devices), laser probes, cryoprobes, sensor probes, and aspiration needles. In one aspect, the ablation device 130 is guided through the EWC 12 to be placed relative to the target and to ablate the target.

[0042] The ablation device 130 is a surgical instrument having a microwave ablation antenna, which is used to ablate tissue, such as a lesion or tumor (hereinafter referred to as a "target"), by heating the tissue using electromagnetic radiation or microwave energy to denature or kill cancer cells. The ablation device 130 can be a rigid surgical instrument configured to be inserted percutaneously and navigated to the target, or a flexible catheter configured to be navigated to the target via a patient's cavity network.

[0043] In addition to the EM tracking system, the surgical instrument at the target can also be visualized by using another imaging system (e.g., ultrasound imaging, fluoroscopic imaging, computed tomography, etc.). For example, an ultrasound imaging device (such as an ultrasound wand) can be used to image the patient's body during a microwave ablation procedure to visualize the location of the surgical instrument (such as ablation device 130) within the patient's body. In one aspect, the target is imaged using fluoroscopic imaging device 110 after or during the navigation of ablation device 130 to the target.

[0044] The location of ablation device 130 within the patient can be tracked during the surgical procedure. An example method of tracking the location of ablation device 130 is by using an EM tracking system that tracks the location of ablation device 130 by tracking sensors attached to or incorporated into ablation device 130 or other devices used to help ablation device 130 navigate to a target.

[0045] Figure 2A system diagram of a computing device 100 is shown. The computing device 100 may include a memory 202, a processor 204, a display 206, a network interface 208, an input device 210, and / or an output module 212. The memory 202 includes any non-transitory computer-readable storage medium for storing data and / or software that can be executed by the processor 204 and controls the operation of the computing device 100. In an embodiment, the memory 202 may include one or more solid-state storage devices, such as flash memory chips. As an alternative or supplement to one or more solid-state storage devices, the memory 202 may include one or more mass storage devices connected to the processor 204 through a mass storage controller (not shown) and a communication bus (not shown). Although the description of the computer-readable medium contained herein refers to solid-state storage, it should be understood by those skilled in the art that the computer-readable storage medium can be any available medium that can be accessed by the processor 204. That is, the computer-readable storage medium includes non-transitory, volatile and non-volatile, removable and non-removable media implemented in any method or technology to store information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technology, CD-ROM, DVD, Blu-ray or other optical storage, cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by the computing device 100.

[0046] The memory 202 can store application programs 216 and / or functional respiratory imaging data 214 of one or more patients. The application programs 216, when executed by the processor 204, can cause the display 206 to present a user interface 218. The processor 204 can be a general-purpose processor, a dedicated graphics processing unit (GPU) configured to perform specific graphics processing tasks while releasing the general-purpose processor to perform other tasks, and / or any number of such processors or combinations thereof. The display 206 can be touch-sensitive and / or voice-activated, so that the display 206 can act as an input and output device. Alternatively, a keyboard (not shown), a mouse (not shown) or other data input devices can be used. The network interface 208 can be configured to connect to a network such as a local area network (LAN), a wide area network (WAN), a wireless mobile network, a Bluetooth network and / or the Internet, etc., composed of a wired network and / or a wireless network. For example, the computing device 100 may receive functional respiratory imaging data, DICOM imaging data, computed tomography (CT) image data, or other imaging data of a patient from an imaging workstation 150 and / or a server (e.g., a hospital server, an Internet server, or other similar server) for use during surgical ablation planning. The patient functional respiratory imaging data may also be provided to the computing device 100 via the removable memory 202. The computing device 100 may receive updates to its software (e.g., application 216) via the network interface 208. The computing device 100 may also display a notification on the display 206 that a software update is available.

[0047] The input device 210 may be any device that a user may use to interact with the computing device 100, such as a mouse, keyboard, foot pedal, touch screen, and / or voice interface. The output module 212 may include any connection port or bus, such as a parallel port, a serial port, a universal serial bus (USB), or any other similar connection port known to those skilled in the art.

[0048] Application 216 may be one or more software programs stored in memory 202 and executed by processor 204 of computing device 100. During the planning phase, application 216 guides the clinician through a series of steps of identifying the target, determining the size of the target, determining the size of the treatment zone, and / or determining an access route to the target for later use during the surgical phase. In some embodiments, application 216 is loaded onto a computing device in an operating room or other facility where the surgical procedure is performed and is used as a floor plan or map to guide the clinician in performing the surgical procedure without any feedback from an ablation device 130 used in the procedure to indicate where ablation device 130 is located relative to the floor plan. In other embodiments, system 10 provides computing device 100 with data regarding the location of ablation device 130 within the patient, such as through EM tracking, which application 216 may then use to indicate on the floor plan where ablation device 130 is located.

[0049] The application 216 may be installed directly on the computing device 100, or may be installed on another computer (e.g., a central server) and opened on the computing device 100 via the network interface 208. The application 216 may be run locally on the computing device 100 as a web-based application or any other format known to those skilled in the art. In some embodiments, the application 216 will be a single software program having all the features and functions described in the present disclosure. In other embodiments, the application 216 may be two or more different software programs that provide various parts of these features and functions. For example, the application 216 may include one software program used during the planning phase, and a second software program used during the surgical phase of the microwave ablation treatment. In this case, the various software programs that form part of the application 216 may be made to communicate with each other and / or import and export various settings and parameters related to the microwave ablation treatment and / or the patient to share information. For example, a treatment plan generated by one software program during the planning phase and any of its components may be stored and exported for use by a second software program during the surgical phase.

[0050] The application 216 communicates with the user interface 218 to generate a user interface for presenting visual interaction features to the clinician, for example on the display 206, and for receiving clinician input, for example via a user input device. For example, the user interface 218 can generate a graphical user interface (GUI) and output the GUI to the display 206 for viewing by the clinician.

[0051] Computing device 100 is linked to display 110, such that computing device 100 can control output on display 110 and output on display 206. Computing device 100 can control display 110 to display output that is the same as or similar to output displayed on display 206. For example, output on display 206 can be mirrored on display 110. Alternatively, computing device 100 can control display 110 to display output that is different from output displayed on display 206. For example, display 110 can be controlled to display guidance images and information during a microwave ablation procedure, while display 206 can be controlled to display other output, such as configuration or status information.

[0052] As used herein, the term "clinician" refers to any medical professional (i.e., physician, surgeon, nurse, etc.) or other user of the treatment planning system 10 who is involved in planning, performing, monitoring and / or supervising a medical procedure involving use of the embodiments described herein.

[0053] Figure 3 An example user interface 300 is shown that may be displayed on one or both of the display 110 and the display 206 during an ablation procedure. During an ablation procedure, a clinician navigates an ablation device 130 (displayed as ablation device 314 on the user interface 300) along a path to a target (e.g., using an imaging device, such as fluoroscopy, CT, or hybrid three-dimensional fluorescence CT imaging device to visualize the placement of the ablation device 130 relative to the target). As the ablation device 130 is navigated, the application 216 tracks the position of the ablation device 130 or the position of a catheter through which the ablation device is placed within the patient's body and displays the tracked position of the ablation device 130 or catheter superimposed on the patient's imaging data (e.g., patient CT data, ultrasound imaging data, fluoroscopy imaging data, hybrid three-dimensional fluorescence CT imaging data, etc.) on the user interface. In addition, the application 216 may project a vector (not shown) extending from the end of the ablation device 130 that may be displayed on the user interface 300 to give the clinician an indication of tissue intersected along the trajectory of the ablation device 130. In this way, clinicians can alter the treatment of a lesion or tumor to minimize trauma, optimize placement, or follow a planned path to the target.

[0054] The user interface 300 includes a navigation view 312 that includes a patient image 313 captured during or preoperatively. For example, the navigation view 312 may include a three-dimensional rendering of a patient's lungs generated from DICOM data. The user interface 300 also includes a view for displaying status messages related to the ablation procedure, such as a power setting 316 of the ablation device 130, the duration of ablation and / or the remaining time until the ablation procedure is completed, the progress of ablation, and / or temperature feedback 308 from a temperature sensor. The navigation view 312 includes a representation 314 of the ablation device 130 and a shaded indication 314a representing the portion of the ablation device 130 that is located below the displayed two-dimensional or three-dimensional imaging plane, an ablation simulation growth 318 showing the progress of the ablation zone during energy application superimposed on a patient image 313 of the surgical site, and a total predicted ablation zone 320 showing the area that will be ablated if the ablation procedure is allowed to run to completion. The navigation view 312 may include a three-dimensional rendering generated from DICOM image data, for example, a three-dimensional rendering generated from a CT scan of the patient.

[0055] The size of the ablation simulated growth 318 and the total predicted ablation zone 320 can be based on the expected ablation zone size at different energy application durations, which is calculated based on functional respiratory imaging data (e.g., of the patient and historical functional respiratory imaging data sets). The ablation simulated growth 318 may include a solid outer edge that increases in size based on the duration of energy application, a jagged line outer edge that moves and increases in size based on the duration of energy application, and / or a pulsed line outer edge that gradually changes between appearing and disappearing and increases in size based on the duration of energy application and the functional respiratory imaging data. The appearing and disappearing pulsed lines of the ablation simulated growth 318 prevent the ablation simulated growth 318 from blocking the visibility of objects in the displayed patient image 313.

[0056] In some aspects, additionally or alternatively, the size of the ablation simulated growth 318 and / or the total predicted ablation zone 320 can be based on one or more other data sets, such as previously acquired data samples, MRI telemetry data, and / or extrapolation from contrast-enhanced ultrasound. In some aspects, the functional respiratory imaging data used by the computing device 100 to calculate the predicted ablation zone 320 includes data corresponding to at least one of the following: vascular volume, airway volume, lung volume, airway resistance, internal airflow distribution, ventilation, perfusion, air trapping, aerosol deposition, fissure integrity, fibrosis, or emphysema.

[0057] Steering Figure 4, a method for predicting an ablation zone is shown and described as method 400. Method 400 is described as being performed by computing device 100, but some or all of the steps of method 400 may be implemented, alone or in combination, by one or more other components of system 10. In addition, while method 400 is shown and described as including specific steps and as being performed in a specific order, it should be understood that method 400 may include some or all of the described steps and may be performed in any order not specifically described.

[0058] Method 400 begins at step 401, in which computing device 100 acquires preoperative CT data of a patient's lungs to identify one or more targets that require ablation. In step 403, computing device 100 acquires or otherwise receives functional respiratory imaging data of the patient, which includes lung imaging at different respiratory points and provides additional insights into the patient's lungs. For example, the functional respiratory imaging data may include data corresponding to at least one of the following: vascular volume, airway volume, lung volume, airway resistance, internal airflow distribution, ventilation, perfusion, air trapping, aerosol deposition, fissure integrity, fibrosis, or emphysema.

[0059] In step 405, computing device 100 generates a 3D model of the patient's lungs based on the functional respiratory imaging data, which may include segmentation of the structure and airways inside the lungs. In step 407, computing device 100 generates a 3D model of lung movement based on the functional respiratory imaging data. The 3D model generated in step 407 may be displayed on a display for analysis.

[0060] In step 409, computing device 100 generates a prediction of movement of the lung and a prediction of movement of ablation device 130 relative to the lung structure and the target during a predetermined duration of the ablation procedure (e.g., a 10-minute ablation procedure). In step 411, computing device 100 predicts the location and boundary of the ablation zone relative to the target based on the received functional respiratory imaging data of the patient and the predicted movement of ablation device 130 relative to the lung and the target during the predetermined duration of the ablation.

[0061] Method 400 may additionally include step 413, in which computing device 100 performs an optimization step to calculate an optimal path for ablation device 130 to approach the target based on the received functional respiratory imaging data of the patient, which optimal path will produce a maximum ablation boundary. For example, in step 413, computing device 100 may determine that the optimal path for ablation device 130 to approach the target includes a path through the cavity network and a puncture point on the airway wall to place ablation device 130 outside the cavity network. Additionally or alternatively, step 413 may include calculating an optimal position for the final placement of ablation device 130 relative to the target based on the received functional respiratory imaging data of the patient, which optimal position will produce a maximum ablation boundary. For example, computing device 100 may determine, based on the functional respiratory imaging data, that the optimal position for the final placement of ablation device 130 is a position outside the airway wall for ablating a target inside or outside the airway wall.

[0062] In step 415, the computing device 100 receives post-operative data corresponding to the actual ablation zone location and boundary, and compares the post-operative data corresponding to the actual ablation zone location and boundary with the predicted ablation zone location and boundary to calculate the deviation between the actual ablation zone location and boundary and the predicted ablation zone location and boundary. The post-operative data may include an image data set depicting the results of the ablation procedure performed on the target, such as CT data. In step 417, the computing device 100 executes a learning algorithm to learn other predictions of the ablation zone location and boundary for the same patient or other patients from the calculated deviation. The learning algorithm may utilize artificial intelligence, data models, or machine learning techniques, which may include but are not limited to neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), Bayesian regression, naive Bayes, nearest neighbors, least squares, mean methods, and support vector regression, as well as other data science and artificial science techniques that utilize historical results and deviations of predicted ablation zone characteristics from actual ablation zone characteristics to generate and output predicted ablation zones. In one aspect, the algorithm segments the actual ablation zone from the post-operative scan and compares the actual ablation zone to the predicted ablation zone predicted in the pre-operative scan.The 3D volume is used to find deviations in 3D space between the plan and the post-operative confirmation.

[0063] Although the embodiments have been described in detail with reference to the accompanying drawings for the purpose of illustration and description, it should be understood that the process and apparatus of the present invention should not be interpreted as being limited thereto. It will be apparent to those skilled in the art that various modifications may be made to the aforementioned embodiments without departing from the scope of the present disclosure.

Claims

1. An ablation system, comprising: an ablation device configured to navigate to and ablate a target within a lung of a patient; as well as A computing device, the computing device being configured to: receiving functional respiratory imaging data of the patient; generating a three-dimensional model of the patient's lungs based on the received functional respiratory imaging data of the patient; predicting lung movement based on the received functional respiratory imaging data of the patient; predicting movement of the ablation device relative to the lung and the target during a predetermined ablation duration; and An ablation zone location and boundary relative to the target is predicted based on received functional respiratory imaging data of the patient and predicted movement of the ablation device relative to the lung and the target during the duration of the predetermined ablation.

2. The ablation system according to claim 1, wherein: The computing device is configured to compute a path for the ablation device to approach the target based on the received functional respiratory imaging data of the patient, the path resulting in a maximum ablation margin.

3. The ablation system according to claim 2, wherein: The calculated path may include a path through the luminal network and puncture points on the airway wall to place the ablation device outside the luminal network.

4. The ablation system according to any one of claims 1 to 3, wherein: The computing device is configured to compute a position for final placement of the ablation device relative to the target based on the received functional respiratory imaging data of the patient, the position resulting in a maximum ablation margin.

5. The ablation system according to claim 4, wherein: The calculated location is outside the airway of the lung.

6. The ablation system according to any one of claims 1 to 3, wherein: The computing device is configured to: receiving post-procedural data corresponding to actual ablation zone locations and boundaries; and The post-operative data corresponding to the actual ablation zone position and boundary are compared with the predicted ablation zone position and boundary to calculate the deviation between the actual ablation zone position and boundary and the predicted ablation zone position and boundary.

7. The ablation system of claim 6, wherein: The computing device is configured to execute a learning algorithm to learn other predictions of ablation zone locations and boundaries from the calculated deviations.

8. The ablation system according to any one of claims 1 to 3, wherein: The functional respiratory imaging data includes data corresponding to at least one of: vascular volume, airway volume, lung volume, airway resistance, internal airflow distribution, ventilation, perfusion, air trapping, aerosol deposition, fissure integrity, fibrosis, or emphysema.

9. An ablation system, comprising: A computing device, the computing device being configured to: receiving functional respiratory imaging data of a patient's lungs and an object within the lungs; predicting lung movement based on the received functional respiratory imaging data of the patient; predicting movement of an ablation device relative to the lung and the target during a predetermined ablation duration; and An ablation zone location and boundary relative to the target is predicted based on received functional respiratory imaging data of the patient and predicted movement of the ablation device relative to the lung and the target during the duration of the predetermined ablation.

10. The ablation system of claim 9, wherein: The computing device is configured to generate a three-dimensional model of the patient's lungs based on the received functional respiratory imaging data of the patient.

11. The ablation system of claim 9 or claim 10, wherein: The computing device is configured to compute a path for the ablation device to approach the target based on the received functional respiratory imaging data of the patient, the path resulting in a maximum ablation margin.

12. The ablation system of claim 11, wherein: The calculated path may include a path through the luminal network and puncture points on the airway wall to place the ablation device outside the luminal network.

13. The ablation system according to any one of claims 9 or 10, wherein: The computing device is configured to compute a position for final placement of the ablation device relative to the target based on the received functional respiratory imaging data of the patient, the position resulting in a maximum ablation margin.

14. The ablation system of claim 13, wherein: The calculated optimal position is outside the airways of the lungs.

15. The ablation system according to any one of claims 9 or 10, wherein: The computing device is configured to: receiving post-procedural data corresponding to actual ablation zone locations and boundaries; and The post-operative data corresponding to the actual ablation zone position and boundary are compared with the predicted ablation zone position and boundary to calculate the deviation between the actual ablation zone position and boundary and the predicted ablation zone position and boundary.

16. The ablation system of claim 15, wherein: The computing device is configured to execute a learning algorithm to learn other predictions of ablation zone locations and boundaries from the calculated deviations.

17. The ablation system according to any one of claims 9 or 10, wherein: The functional respiratory imaging data includes data corresponding to at least one of: vascular volume, airway volume, lung volume, airway resistance, internal airflow distribution, ventilation, perfusion, air trapping, aerosol deposition, fissure integrity, fibrosis, or emphysema.

18. An ablation system, comprising: A computing device, the computing device being configured to: receiving functional respiratory imaging data of a patient's lungs and an object within the lungs; predicting ablation zone locations and boundaries relative to the target based on the received functional respiratory imaging data of the patient; receiving post-procedural data corresponding to actual ablation zone locations and boundaries; and The post-operative data corresponding to the actual ablation zone position and boundary are compared with the predicted ablation zone position and boundary to calculate the deviation between the actual ablation zone position and boundary and the predicted ablation zone position and boundary.

19. The ablation system of claim 18, wherein: The computing device is configured to execute a learning algorithm to learn other predictions of ablation zone locations and boundaries from the calculated deviations.

20. The ablation system of claim 18 or claim 19, wherein: The functional respiratory imaging data includes data corresponding to at least one of: vascular volume, airway volume, lung volume, airway resistance, internal airflow distribution, ventilation, perfusion, air trapping, aerosol deposition, fissure integrity, fibrosis, or emphysema.

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