Imaging system for computational fluid dynamics

Abstract

In the present disclosure, there is provided an imaging system for a patient, comprising an imaging probe and an imaging assembly. The imaging probe comprises an elongate shaft and a rotatable optical core disposed within a lumen of the elongate shaft. The imaging probe further comprises an optical assembly for directing light to a tissue to be imaged and collecting reflected light from the tissue to be imaged. The system further comprises the imaging assembly optically coupled to the imaging probe. The system further comprises a processing unit having a processor and a memory coupled to the processor, the memory storing instructions for the processor to execute an algorithm. The system records image data based on the reflected light collected by the optical assembly, the image data including data collected from a portion of a blood vessel during a pullback procedure. The algorithm is capable of analyzing the image data.

Description

[Technical field]

[0001] [Related Applications] This application claims the benefit of U.S. Provisional Application No. 63 / 298,086 (Docket No. GTY-022-PR1), filed January 10, 2022, entitled “Imaging System for Computational Fluid Dynamics,” the entire contents of which are incorporated herein by reference.

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 416,170 (Docket No. GTY-023-PR1), filed October 14, 2022, entitled "Imaging System," the entire contents of which are incorporated herein by reference.

[0003] This application is related to U.S. Provisional Application No. 62 / 148,355 (Docket No. GTY-001-PR1), filed April 16, 2015, entitled “Micro-Optical Probe for Neurology,” the entire contents of which are incorporated herein by reference.

[0004] This application is related to U.S. Provisional Application No. 62 / 322,182 (Docket No. GTY-001-PR2), filed April 13, 2016, entitled “Micro-Optical Probe for Neurology,” the entire contents of which are incorporated herein by reference.

[0005] This application is related to International Patent Application No. PCT / US2016 / 027764 (Docket No. GTY-001-PCT), filed April 15, 2016, entitled "Micro-Optical Probe for Neurology," and International Publication No. WO 2016 / 168605, published October 20, 2016, the entire contents of which are incorporated herein by reference.

[0006] This application is related to U.S. patent application Ser. No. 15 / 566,041 (Docket No. GTY-001-US), filed Oct. 12, 2017, entitled "Micro-Optical Probe for Neurology," and U.S. Patent No. 1,127,206, issued Mar. 22, 2022, the entire contents of which are incorporated herein by reference.

[0007] This application is related to U.S. Patent Application No. 17 / 668,757 (Docket No. GTY-001-US-CON1), filed February 10, 2022, entitled "Micro-Optical Probe for Neurology," and U.S. Patent Application Publication No. 2022-0218206, published July 14, 2022, the entire contents of which are incorporated herein by reference.

[0008] This application is related to U.S. Provisional Application No. 62 / 212,173 (Attorney Docket No. GTY-002-PR1), filed on August 31, 2015, entitled “IMAGING SYSTEM WITH IMAGING PROBE AND DELIVERY DEVICE,” the entire contents of which are incorporated herein by reference.

[0009] This application is related to U.S. Provisional Application No. 62 / 368,387 (Attorney Docket No. GTY-002-PR2), filed July 29, 2016, entitled “IMAGING SYSTEM WITH IMAGING PROBE AND DELIVERY DEVICE,” the entire contents of which are incorporated herein by reference.

[0010] This application is related to International Patent Application No. PCT / US2016 / 049415 (Docket No. GTY-002-PCT), filed August 30, 2016, entitled "IMAGING SYSTEM WITH IMAGING PROBE AND DELIVERY DEVICE," and International Publication No. WO 2017 / 040484, published March 9, 2017, the entire contents of which are incorporated herein by reference.

[0011] This application is related to U.S. patent application Ser. No. 15 / 751,570 (Docket No. GTY-002-US), filed Feb. 9, 2018, entitled “IMAGING SYSTEM WITH IMAGING PROBE AND DELIVERY DEVICE,” and U.S. Patent No. 10,631,718, issued Apr. 28, 2020, the entire contents of which are incorporated herein by reference.

[0012] This application is related to U.S. patent application Ser. No. 16 / 802,991 (Docket No. GTY-002-US-CON1), filed March 17, 2020, entitled "IMAGING SYSTEM WITH IMAGING PROBE AND DELIVERY DEVICE," and U.S. Patent No. 1,106,4873, issued July 20, 2021, the entire contents of which are incorporated herein by reference.

[0013] This application is related to U.S. Patent Application No. 17 / 350021 (Docket No. GTY-002-US-CON2), filed June 17, 2021, entitled "IMAGING SYSTEM WITH IMAGING PROBE AND DELIVERY DEVICE," and U.S. Patent Application Publication No. 2022-0142464, published May 12, 2022, the entire contents of which are incorporated herein by reference.

[0014] This application is related to U.S. Provisional Application No. 62 / 591,403 (Docket No. GTY-003-PR1), entitled “Imaging System,” filed November 28, 2017, the entire contents of which are incorporated herein by reference.

[0015] This application is related to U.S. Provisional Application No. 62 / 671,142 (Docket No. GTY-003-PR2), filed May 14, 2018, entitled “Imaging System,” the entire contents of which are incorporated herein by reference.

[0016] This application is related to International Patent Application No. PCT / US2018 / 062766 (Docket No. GTY-003-PCT), filed November 28, 2018, entitled "IMAGING SYSTEM," and International Publication No. WO 2019 / 108598, published June 6, 2019, the entire contents of which are incorporated herein by reference.

[0017] This application is related to U.S. Patent Application No. 16 / 764,087 (Docket No. GTY-003-US), filed May 14, 2020, entitled “IMAGING SYSTEM,” and U.S. Patent Application Publication No. 2020-0288950, published September 17, 2020, the entire contents of which are incorporated herein by reference.

[0018] This application is related to U.S. Provisional Application No. 62 / 732,114 (Docket No. GTY-004-PR1), filed September 17, 2018, entitled “IMAGING SYSTEM WITH OPTICAL PATH,” the entire contents of which are incorporated herein by reference.

[0019] This application is related to International Patent Application No. PCT / US2019 / 051447 (Docket No. GTY-004-PCT), filed September 17, 2019, entitled "IMAGING SYSTEM WITH OPTICAL PATH," and International Publication No. WO 2020 / 061001, published March 26, 2020, the entire contents of which are incorporated herein by reference.

[0020] This application is related to U.S. Patent Application No. 17 / 276,500 (Docket No. GTY-004-US), filed March 16, 2021, entitled "IMAGING SYSTEM WITH OPTICAL PATH," and U.S. Patent Application Publication No. 2021-0267442, published September 2, 2021, the entire contents of which are incorporated herein by reference.

[0021] This application is related to U.S. Provisional Application No. 63 / 017258 (Docket No. GTY-005-PR1), filed April 29, 2020, entitled “Imaging System,” the entire contents of which are incorporated herein by reference.

[0022] This application is related to International Patent Application No. PCT / US2021 / 29836 (Docket No. GTY-005-PCT), filed April 29, 2021, entitled "Imaging System," and International Publication No. WO 2021 / 222530, published November 4, 2021, the entire contents of which are incorporated herein by reference.

[0023] This application is related to U.S. patent application Ser. No. 17 / 919,809 (Attorney Docket: GTY-005-US), filed on October 19, 2022, entitled "Imaging System," the entire contents of which are incorporated herein by reference.

[0024] This application is related to U.S. Provisional Application No. 62 / 840,450 (Docket No. GTY-011-PR1), filed April 30, 2019, entitled “IMAGING PROBE WITH FLUID PRESSURE ELECTRONICS,” the entire contents of which are incorporated herein by reference.

[0025] This application is related to International Patent Application No. PCT / US2020 / 030616 (Docket No. GTY-011-PCT), filed April 30, 2020, entitled "IMAGING PROBE WITH FLUID PRESSURE ELECTRONICS ELEMENT," and International Publication No. WO 2020 / 223433, published November 5, 2020, the entire contents of which are incorporated herein by reference.

[0026] This application is related to U.S. Patent Application No. 17 / 600,212 (Docket No. GTY-011-US), filed September 30, 2021, entitled “Imaging Probe with Fluid Pressurizing Element,” and U.S. Patent Application Publication No. 2022-0142462, published May 12, 2022, the entire contents of which are incorporated herein by reference.

[0027] This application is related to U.S. Provisional Application No. 62 / 850,945 (Docket No. GTY-013-PR1), filed May 21, 2019, entitled “OCT-GUIDED TREATMENT OF A PATIENT,” the entire contents of which are incorporated herein by reference.

[0028] This application is related to U.S. Provisional Application No. 62 / 906,353 (GTY-013-PR2), filed September 26, 2019, entitled “OCT-Guided Patient Treatment,” the entire contents of which are incorporated herein by reference.

[0029] This application is related to International Patent Application No. PCT / US2020 / 033953 (Docket No. GTY-013-PCT), filed May 21, 2020, entitled "SYSTEMS AND METHODS FOR OCT-GUIDED PATIENT TREATMENT," and International Publication No. WO 2020 / 237024, published November 26, 2020, the entire contents of which are incorporated herein by reference.

[0030] This application is related to U.S. Patent Application No. 17 / 603,689 (Docket No. GTY-013-US), filed on October 14, 2021, entitled "Systems and Methods for OCT-Guided Patient Treatment," and U.S. Patent Application Publication No. 2022-0061670, published on March 3, 2022, the entire contents of which are incorporated herein by reference.

[0031] This application is related to U.S. Provisional Application No. 63 / 154,934 (Docket No. GTY-021-PR1), filed March 1, 2021, entitled “Optical Imaging System,” the entire contents of which are incorporated herein by reference.

[0032] This application is related to U.S. Patent Application No. 17 / 682,197 (Docket No. GTY-021-US), filed February 28, 2022, entitled "Optical Imaging System," and U.S. Patent Application Publication No. 2023-0000321, published January 5, 2023, the entire contents of which are incorporated herein by reference.

[0033] [Field of inventive concepts] The present invention relates generally to imaging systems, and more particularly to an intravascular imaging system that includes an imaging probe and a delivery device. [Background technology]

[0034] Imaging probes have been commercialized for imaging various internal locations of a patient, such as, for example, intravascular probes for imaging a patient's heart. Current imaging probes are limited in their ability to reach certain anatomical locations due to their size and stiffness. Current imaging probes are inserted through a guidewire, which can make positioning of the imaging probe difficult and limit the use of one or more delivery catheters through which the imaging probe is inserted. Thus, there is a need for an imaging system that includes a small diameter, highly flexible probe, as well as one or more delivery devices that can accommodate such improved imaging probes. Summary of the Invention

[0035] According to one aspect of the inventive concept, an imaging system for a patient includes an imaging probe having an elongated shaft, the shaft having a proximal end, a distal portion, and a lumen extending between the proximal end and the distal portion. The imaging probe further includes a rotatable optical core having a proximal end and a distal end. At least a portion of the rotatable optical core is disposed within the lumen of the elongated shaft. The imaging probe further includes an optical assembly disposed near the distal end of the optical core. The optical assembly is configured to direct light to and collect reflected light from a tissue to be imaged. The system further includes an imaging assembly constructed and arranged to optically couple to the imaging probe. The imaging assembly is configured to illuminate light into the imaging probe and to receive the reflected light collected by the optical assembly. The system further includes a processing unit including a processor and a memory coupled to the processor. The memory is configured to store instructions for the processor to execute an algorithm. The system may be configured to record image data based on the reflected light collected by the optical assembly, the image data including data collected from a portion (segment) of a blood vessel during a pullback procedure, and the algorithm may be configured to analyze the image data.

[0036] In some embodiments, the image data includes OCT image data.

[0037] In some embodiments, the algorithm is configured to calculate the computational fluid dynamics of the portion (segment) of the blood vessel.

[0038] In some embodiments, the algorithm is configured to perform a segmentation of the image data, which may be selected from the group consisting of a processing device segmentation, a guide catheter segmentation, a guidewire segmentation, an implant segmentation, an intravascular implant segmentation, a flow diverter segmentation, a lumen segmentation, and a side branch segmentation, and combinations thereof. The algorithm may comprise a neural network tuned to perform the segmentation.

[0039] In some embodiments, the algorithm is configured to generate a reliability metric configured to represent a quality of the result of the image processing step.

[0040] In some embodiments, the algorithm comprises an artificial intelligence algorithm. The artificial intelligence algorithm may comprise a machine learning algorithm, a deep learning algorithm, or a neural network. The algorithm may comprise a neural network and may be configured to skip one or more layers in the neural network. The algorithm may comprise a single neural network trained to perform two or more image segmentation processes. The artificial intelligence algorithm may be trained to perform side branch segmentation, and the algorithm achieves an average weighted Dice score of at least 0.81.

[0041] In some embodiments, the algorithm is configured to receive image data in a single image domain, and the algorithm is further configured to transform the image data to one or more additional image domains.

[0042] In some embodiments, the algorithm is configured to process the image data in one or more image domains selected from the group consisting of polar domain, Cartesian domain, longitudinal domain, frontal image domain, and domains generated by computing image features, and combinations thereof, such as primary and / or secondary features, image texture, image entropy, homogeneity, correlation, contrast, energy, and / or any other image feature.

[0043] In some embodiments, the system further comprises a graphical user interface configured to be displayed to a user. The graphical user interface may be configured to provide an indicator of image data quality. The indicator of image data quality may be displayed in association with a cross-sectional OCT image. The graphical user interface may be configured to allow a user to review a result of an image processing step. The graphical user interface may be further configured to allow a user to approve the result of the image processing step. The graphical user interface may be further configured to allow a user to edit the result of the image processing step. The algorithm may comprise an artificial intelligence algorithm, and the image processing step may be performed by the artificial intelligence algorithm. The graphical user interface may comprise a plurality of workspaces, and data displayed in each of the workspaces may be synchronized. The data may be synchronized by a time index. The data may be synchronized by a location index.

[0044] In some embodiments, the system is configured to collect image data before and after an interventional procedure. The algorithm may be configured to compare the image data before the interventional procedure with the image data after the interventional procedure to quantify the effect of the interventional procedure. The algorithm may comprise an artificial intelligence algorithm.

[0045] In some embodiments, the algorithm comprises a bias.The system may comprise a user interface, and the bias may be entered and / or modified via the user interface.

[0046] The technology described herein, together with its features and attendant advantages, will be best understood from the following detailed description taken in conjunction with the accompanying drawings, in which exemplary embodiments are set forth by way of example.

[0047] [Incorporated by reference] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. The contents of all publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entireties for all purposes. [Brief description of the drawings]

[0048] [Figure 1] 1 shows a schematic diagram of a diagnostic system including an imaging probe and one or more algorithms for processing image data in accordance with the concepts of the present invention. [Diagram 2] 1 shows a graphical representation of a neural network in accordance with the concepts of the present invention. [Diagram 3] 1 illustrates one embodiment of a graphical user interface for displaying image data to guide a vascular intervention, in accordance with the concepts of the present invention. [Figure 3A]1 illustrates another embodiment of a graphical user interface for displaying image data and guiding a vascular intervention in accordance with the concepts of the present invention. [Figure 3B] 1 illustrates another embodiment of a graphical user interface for displaying image data and guiding a vascular intervention in accordance with the concepts of the present invention. [Figure 3C] 1 illustrates another embodiment of a graphical user interface for displaying image data and guiding a vascular intervention in accordance with the concepts of the present invention. [Figure 4A] 1 shows vascular anatomy illustrating various stages of atherosclerosis in accordance with the concepts of the present invention. [Figure 4B] 1 shows vascular anatomy illustrating various stages of atherosclerosis in accordance with the concepts of the present invention. [Figure 4C] 1 shows vascular anatomy illustrating various stages of atherosclerosis in accordance with the concepts of the present invention. [Figure 4D] 1 shows vascular anatomy illustrating various stages of atherosclerosis in accordance with the concepts of the present invention. [Diagram 5] 1 illustrates a method of treatment planning based on data collected and / or analyzed by the system in accordance with the concepts of the present invention. [Figure 6A] 1 shows various OCT images of a blood vessel and a guide catheter in accordance with the concepts of the present invention. [Figure 6B] 1 shows various OCT images of a blood vessel and a guide catheter in accordance with the concepts of the present invention. [Figure 6C] 1 shows various OCT images of a blood vessel and a guide catheter in accordance with the concepts of the present invention. [Figure 7A] 1 shows an image displayed to a user to represent OCT image data and image quality in accordance with the concepts of the present invention. [Figure 7B] 1 shows an image displayed to a user to represent OCT image data and image quality in accordance with the concepts of the present invention. [Figure 7C] 1 shows an image displayed to a user to represent OCT image data and image quality in accordance with the concepts of the present invention. [Figure 7D] 1 shows an image displayed to a user to represent OCT image data and image quality in accordance with the concepts of the present invention. [Figure 8] 1 illustrates one embodiment of a graphical user interface for displaying image data and allowing a user to review information determined by the system based on the image data, in accordance with the concepts of the present invention. [Figure 8A] 1 illustrates one embodiment of a graphical user interface for displaying image data and allowing a user to review information determined by the system based on the image data, in accordance with the concepts of the present invention. [Figure 9] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 10] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 11A] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 11B] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 12A] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 12B] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 13] 13A-13C show OCT image data illustrating the results of poor and good catheter purging in accordance with concepts of the present invention. [Figure 14] 13 illustrates another embodiment of a graphical user interface for displaying image data and guiding a vascular intervention in accordance with the concepts of the present invention. [Figure 15] 1 illustrates a method for treating a patient, including treatment plan development and evaluation, in accordance with the concepts of the present invention. [Figure 16A] 1A-1C illustrate examples of various types of image data in accordance with the concepts of the present invention. [Figure 16B]1A-1C illustrate examples of various types of image data in accordance with the concepts of the present invention. [Figure 16C] 1A-1C illustrate examples of various types of image data in accordance with the concepts of the present invention. [Figure 16D] 1A-1C illustrate examples of various types of image data in accordance with the concepts of the present invention. [Figure 16E] 1A-1C illustrate examples of various types of image data in accordance with the concepts of the present invention. [Figure 17] 1 illustrates one embodiment of a graphical user interface for displaying image features automatically identified by an image processing algorithm in accordance with the concepts of the present invention. [Figure 18A] 10A-10C show examples of pre-processed image data with different blood levels in each image in accordance with the concepts of the present invention. [Figure 18B] 10A-10C show examples of pre-processed image data with different blood levels in each image in accordance with the concepts of the present invention. [Figure 18C] 10A-10C show examples of pre-processed image data with different blood levels in each image in accordance with the concepts of the present invention. [Figure 19A] 13 shows another OCT image in accordance with the concepts of the present invention. [Figure 19B] 13 shows another OCT image in accordance with the concepts of the present invention. [Figure 19C] 13 shows another OCT image in accordance with the concepts of the present invention. [Figure 20] 1 shows the results of tests performed by the applicant according to the concepts of the present invention. [Figure 21] 1 shows a graphical representation of a neural network in accordance with the concepts of the present invention. [Figure 21A] 1 illustrates an image frame in accordance with the concepts of the present invention. [Figure 21B] 1 illustrates longitudinal image data in accordance with the concepts of the present invention. [Figure 22A] 1 shows a representation of a segmentation by a combined method according to the inventive concept. [Figure 22B] 1 illustrates an example of segmented image data in accordance with the concepts of the present invention. [Figure 23-1]1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 23-2] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 24] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 25A] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 25B] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 26A] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 26B] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 27A] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 27B] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 28] 1 shows various representations of data collected by the applicant in accordance with the concepts of the present invention; [Figure 29] A method is shown in accordance with concepts of the present invention for capturing image data and applying AI algorithms to the image data to develop improved therapeutics and obtain regulatory approval for the therapeutics. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0049] Next, embodiments of the present technology will be described in detail, examples of which are illustrated in the accompanying drawings. Like reference numerals may be used to refer to like components. However, the following description should not be construed as limiting the disclosure to the particular embodiments, but as including various modifications, equivalents, and / or alternatives to the embodiments described herein.

[0050] It will be understood that the terms "comprising" (and all related expressions), "having" (and all related expressions), "including" (and all related expressions) or "including" (and all related expressions) as used herein specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups (combinations) thereof.

[0051] Furthermore, although terms such as "first", "second", "third", etc. may be used herein to describe various limits, elements, components, regions, layers, and / or sections, it will be understood that these limits, elements, components, regions, layers, and / or sections should not be limited by these terms. These terms are used only to distinguish one limit, element, component, region, layer, or section from another limit, element, component, region, layer, or section. Thus, a "first" limit, element, component, region, layer, or section described below may also be referred to as a "second" limit, element, component, region, layer, or section without departing from the teachings of the present application.

[0052] Additionally, when an element is described as being "on," "attached," "connected," or "coupled" to another element, it is understood that the element may be directly on or above the other element, connected or coupled to the other element, or there may be one or more intervening elements. In contrast, when an element is described as being "directly on," "directly attached," "directly connected," or "directly coupled" to another element, there are no intervening elements present. Other terms used to describe relationships between elements (e.g., "between," "adjacent," etc.) should be interpreted similarly.

[0053] As used herein, the terms "operably attached," "operably connected," "operably coupled," and similar terms relating to attachment of components, refer to the attachment of two or more components that provides one, two, or more of an electrical, fluid, magnetic, mechanical, optical, acoustic, and / or other operable attachment arrangement. Two or more components may be operably attached to facilitate the transmission of power, signals, electrical energy, fluid or other flowable material, magnetic, mechanical coupling, light, sound (e.g., ultrasound), and / or other material (components) between the two or more components.

[0054] Furthermore, when a first element is described as being "in," "on," and / or "within" a second element, it is understood that the first element can be disposed within an interior space of the second element, within a portion of the second element (e.g., within a wall of the second element), on an exterior and / or interior surface of the second element, and any combination or combinations thereof.

[0055] As used herein, the term "proximate" to describe a first component or location being close to a second component or location is to be interpreted to include one or more locations near the second component or location, as well as locations in, on, and / or within the second component or location. For example, a component located near an anatomical location (e.g., a location of a target tissue) includes not only a component located near the anatomical location, but also a component located in, on, and / or within the anatomical location.

[0056] Spatially relative terms such as "lower," "bottom," "lower," "lower side," "upper," "top," "upper," and "above" may be used to describe the relationship of an element and / or feature to another element and / or feature, for example, as shown in the figures. Furthermore, it will be understood that the spatially relative terms are intended to encompass various orientations of the device in use and / or operation in addition to the orientation depicted. For example, if a device in the figures is turned over, an element described as "below" and / or "below" another element or feature would then be positioned "above" the other element or feature. The device may be otherwise oriented (e.g., rotated 90 degrees or oriented in other directions) and the spatially relative descriptions used herein interpreted accordingly.

[0057] As used herein, the terms "reduce," "reduce," "mitigate," and the like are intended to include a reduction in amount (including a reduction to zero). A "reduce" in the likelihood of occurrence also includes prevention of occurrence. Similarly, the terms "prevent," "prevent," "suppress," and the like include the actions of "reduce," "reduce," "mitigate," and the like.

[0058] As used herein, the term "and / or" is to be construed as meaning that each of the two specified features or components is specifically disclosed without regard to the presence or absence of the other. For example, "A and / or B" is to be construed as meaning that (i) A, (ii) B, and (iii) both A and B are specifically disclosed as if each were individually set forth herein.

[0059] As used herein, the term "one or more" means one, two, three, four, five, six, seven, eight, nine, ten, or any number more.

[0060] As used herein, the term "and combinations thereof" may be used after a list of items that are included singly or collectively. For example, components, processes, and / or other items selected from the group consisting of A, B, C, and combinations thereof, includes a set of one or more components that includes one, two, three, or more items A, one, two, three, or more items B, and / or one, two, three, or more items C.

[0061] As used herein, "and" can mean "or," and vice versa, unless expressly stated otherwise. For example, if a feature is described as having A, B, or C, the feature can have A, B, C, or any combination of A, B, and C. Similarly, if a feature is described as having A, B, and C, the feature can have only one or only two of A, B, or C.

[0062] As used herein, when a quantifiable parameter is described as having a value "between" a first value X and a second value Y, it is intended to include parameters having values ​​greater than or equal to X (at least X), values ​​less than or equal to Y, and / or values ​​greater than or equal to X (at least X) and less than or equal to Y. For example, a length "between" 1 and 10 includes lengths greater than or equal to 1 (at least 1) (including values ​​greater than 10), lengths less than or equal to 10 (including values ​​less than 1), and / or values ​​greater than 1 and less than or equal to 10.

[0063] As used in this disclosure, the phrase "configured to" may be used interchangeably with, for example, "suitable to," "capable of," "designed to," "adapted to," "made to," and "capable of," depending on the context. "Configured" does not mean only "specially designed" in hardware. Alternatively, depending on the context, a "device configured to" may mean that the device is "capable" of operating in conjunction with another device or component.

[0064] As used herein, the term "about" or "approximately" refers to ±5% of the stated value.

[0065] The term "threshold" as used herein refers to a maximum level, a minimum level, and / or a range of values ​​that correlate with a desired or undesirable condition. In some embodiments, a system parameter is maintained above a minimum threshold, below a maximum threshold, at a value within a threshold range, and / or at a value outside a threshold range, for example, to cause a desired effect (e.g., effective treatment) and / or to prevent or reduce (hereinafter simply "prevent") an undesirable event (e.g., adverse device and / or clinical event). In some embodiments, a system parameter is maintained at or above a first threshold (e.g., a first temperature threshold to produce a desired therapeutic effect on tissue) and below a second threshold (e.g., a second temperature threshold to prevent undesirable tissue damage). In some embodiments, the thresholds are determined to include a safety margin, taking into account patient variability, system variability, tolerances, etc. As used herein, "above a threshold" refers to a parameter being above a maximum threshold, below a minimum threshold, within a threshold range, and / or outside a threshold range.

[0066] "Room pressure" as described herein is intended to mean the pressure of the environment surrounding the systems and devices of the inventive concept. Positive pressure includes pressure above room pressure or simply pressure greater than another pressure (e.g., a positive pressure differential across a fluid path component such as a valve). Negative pressure includes pressure below room pressure or pressure less than another pressure (e.g., a negative pressure differential across a fluid component path such as a valve). Negative pressure also includes vacuum and does not necessarily mean pressure below vacuum. As used herein, the term "vacuum" may be used to refer to a complete vacuum, a partial vacuum, or any negative pressure as described above.

[0067] The term "diameter" as used herein to describe a non-circular shape is to be interpreted as the diameter of an imaginary circle that approximates the shape in question. For example, when describing a cross-section (e.g., a cross-section of a component), the term "diameter" is to be interpreted as representing the diameter of an imaginary circle that has the same cross-sectional area as the cross-section of the component being described.

[0068] As used herein, the term "major axis" of a component refers to the length of an imaginary cylinder of smallest volume that could completely enclose the component, and the term "minor axis" refers to the diameter of that imaginary cylinder.

[0069] The term "functional element" as used herein is to be interpreted to include one or more elements configured (constructed and arranged) to perform a function. Functional elements may include sensors and / or transducers. In some embodiments, a functional element (e.g., a functional element configured as a therapeutic element) is configured to deliver energy and / or treat tissue. Alternatively or additionally, a functional element (e.g., a functional element including a sensor) may be configured to record one or more parameters (e.g., patient physiological parameters, patient anatomical parameters (e.g., tissue shape parameters), patient environmental parameters, and / or system parameters, etc.). In some embodiments, a sensor or other functional element is configured to perform a diagnostic function (e.g., collect data used to perform a diagnosis). In some embodiments, a functional element is configured to perform a therapeutic function (e.g., deliver therapeutic energy and / or therapeutic agent). In some embodiments, a functional element comprises one or more elements configured (constructed and arranged) to perform a function selected from the group consisting of: delivering energy, extracting energy (e.g., for cooling a component), delivering a medicine or other agent, manipulating a system component or a patient's tissue, recording or sensing a parameter (e.g., a patient physiological parameter or a system parameter, etc.), and a combination of one or more of the foregoing. A functional element may comprise a fluid and / or a fluid delivery system. A functional element may comprise a reservoir (e.g., an expandable balloon or other fluid-maintaining reservoir, etc.). A "functional assembly" may comprise an assembly configured (constructed and arranged) to perform a function, such as a diagnostic function and / or a therapeutic function. A functional assembly may comprise an expandable assembly. A functional assembly may comprise one or more functional elements.

[0070] The term "transducer" as used herein is construed to include any component or combination of components that receives energy or any input and generates an output. For example, a transducer may comprise an electrode that receives electrical energy and distributes the electrical energy to tissue (e.g., based on the size of the electrode). In some configurations, a transducer converts an electrical signal into any output. The output may be, for example, light (e.g., in the case of a transducer comprising a light emitting diode or a light bulb), sound (e.g., in the case of a transducer comprising a piezoelectric crystal configured to transmit ultrasonic energy), pressure (e.g., applied pressure or force), thermal energy, cryogenic energy, chemical energy, mechanical energy (e.g., in the case of a transducer comprising a motor or a solenoid) mechanical energy, magnetic energy, and / or a different electrical signal (e.g., a signal different from the input signal to 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 comprise any component that delivers energy and / or agents to tissue. For example, the transducer may be configured to deliver electrical energy (e.g., in the case of a transducer comprising one or more electrodes), optical energy (e.g., in the case of a transducer comprising a laser, a light emitting diode, and / or an optical component such as a lens or a prism), mechanical energy (e.g., in the case of a transducer comprising a tissue manipulation element), acoustic energy (e.g., in the case of a transducer comprising a piezoelectric crystal), chemical energy, electromagnetic energy, magnetic energy, and combinations of one or more of these to the tissue.

[0071] As used herein, the term "fluid" refers to a liquid, gas, gel, or any flowable material (eg, a material that can be propelled through a lumen and / or opening).

[0072] As used herein, the term "material" refers to a single material or a combination of two, three, four or more materials.

[0073] It will be understood that certain features of the inventive concepts that are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the inventive concepts that are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination. For example, it will be understood that all features set forth in any claim (whether independent or dependent) may be combined in any manner.

[0074] It should be understood that at least some of the diagrams and descriptions of the inventive concepts have been simplified to focus on elements relevant for a clear understanding of the inventive concepts, and that for purposes of clarity, other elements that a person skilled in the art would recognize may form part of the inventive concepts have been omitted, although such elements are well known in the art and will not necessarily be helpful in furthering an understanding of the inventive concepts, and therefore a description of such elements will not be provided herein.

[0075] The terms defined in this disclosure are used only to describe certain embodiments of the disclosure and are not intended to limit the scope of the disclosure. Terms provided in the singular form shall include the plural form unless otherwise clearly indicated by the context. All terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the relevant art, unless otherwise defined herein. Terms defined in commonly used dictionaries should be interpreted as having the same or similar meaning as the contextual meaning of the relevant art, and should not be interpreted as having an ideal or exaggerated meaning unless expressly defined herein. In some cases, the terms defined in this disclosure should not be interpreted as excluding embodiments of the disclosure.

[0076] Provided herein is a system for diagnosing and / or treating a patient, such as for use in a medical procedure (including a diagnostic procedure, a therapeutic procedure, or both). The system of the inventive concept comprises an imaging probe and an imaging assembly. The imaging probe may comprise an elongate shaft, a rotatable optical core, and an optical assembly. The shaft may comprise a proximal end, a distal portion, and a lumen extending between the proximal end and the distal portion. The rotatable optical core may comprise a proximal end and a distal end, and at least a portion of the rotatable optical core may be disposed within the lumen of the elongate shaft. The optical assembly may be disposed near the distal end of the rotatable optical core and may be configured to direct light to tissue and collect reflected light from the tissue. The imaging system may comprise one or more algorithms configured to improve performance of the system.

[0077] Imaging systems according to the concepts of the present invention provide image data representative of arteries, veins, and / or other conduits in the body and may be used to image one or more devices inserted into these conduits. The imaging system may be used to image tissue and / or other structures outside of blood vessels and / or other lumens into which an imaging probe is inserted. The imaging system may provide image data related to healthy tissue as well as image data related to diseased tissue (e.g., blood vessels containing stenosis, myocardial bridges, and / or other vascular narrowings (herein "lesions" or "stenoses") and / or blood vessels containing aneurysms, etc.). The imaging system may be configured to provide treatment information (e.g., recommended treatment procedures to be performed, etc.), such as when the treatment information is used by an operator (e.g., the patient's clinician) to plan treatment or predict treatment outcomes.

[0078] Referring to FIG. 1, a schematic diagram of a diagnostic system according to the concepts of the present invention is shown, the system 10 comprising an imaging probe and one or more algorithms for processing image data. The system 10 may be configured as a diagnostic system configured to record image data from a patient and generate one or more images based on the recorded data. Furthermore, the system 10 may be configured to analyze the recorded data and / or the generated images (either or both of which may be referred to as "image data"). The system 10 may be configured to provide, for example, diagnostic data relating to a disease or condition of the patient, planning data relating to a plan of a therapeutic procedure to be performed on the patient, and / or outcome data relating to the effectiveness and / or technical outcome of the therapeutic procedure. The diagnostic data may include image data.

[0079] The system 10 may be configured (constructed and arranged) to record optical coherence tomography (OCT) data from an imaging location (e.g., OCT data recorded from a section of a blood vessel during a pullback procedure, as described herein). In some embodiments, the OCT data recorded by the system 10 includes high frequency OCT (HF-OCT) data. The system 10 may include an imaging probe 100, which is a catheter-based probe, and a probe interface unit, a PIU 200. The PIU 200 is configured to be operably attached to the imaging probe 100. The PIU 200 may include a rotation assembly 210 and / or a retraction assembly 220. The rotation assembly 210 may be operably attached to the imaging probe 100 to rotate at least a portion of the imaging probe 100. The retraction assembly 220 may be operably attached to the imaging probe 100 to retract at least a portion of the imaging probe 100. The system 10 may include a console 300 that is operably attached to the imaging probe 100, such as via the PIU 200. The imaging probe 100 may be introduced into a patient's vessel (e.g., a patient's blood vessel or other vessel) using (e.g., through) one or more delivery catheters (e.g., delivery catheter 80 as shown). Additionally or alternatively, the imaging probe 100 may be introduced through an introduction device (e.g., an endoscope, arthroscope, balloon dilator, etc.). In some embodiments, the imaging probe 100 is configured to be introduced into a patient's vessel and / or other body site, where the patient's vessel and / or other body site is selected from the group consisting of an artery, a vein, an artery in or near the heart, a vein in or near the heart, an artery in or near the brain, a vein in or near the brain, a peripheral artery, a peripheral vein, a body site accessed through a natural body orifice (e.g., the esophagus), a body site accessed through a surgically created opening (e.g., a vessel or other site in the abdomen), and any combination of one or more of the foregoing.

[0080] In some embodiments, the imaging probe 100 and / or other components in the system 10 may be similarly configured (constructed and arranged) to similar components described in commonly owned co-pending U.S. patent application Ser. No. 17 / 668,757, filed Feb. 10, 2022, entitled "Micro-Optical Probe for Neurology" (Attorney Docket No. GTY-001-US-CON1). The imaging probe 100 may be configured (constructed and arranged) to collect image data from a site on a patient (e.g., a cardiac site within a blood vessel, an intracranial site, or other site accessible via the patient's vasculature). In some embodiments, the system 10 may be similarly configured (constructed and arranged) to similar systems and methods of use thereof described in commonly owned co-pending U.S. patent application Ser. No. 17 / 350,021, filed Jun. 17, 2021, entitled "Imaging System with Imaging Probe and Delivery Device" (Attorney Docket No. GTY-002-US-CON2).

[0081] The imaging probe 100 may comprise an elongate body, which comprises one or more elongate shafts and / or tubes (herein referred to as shaft 120). The shaft 120 comprises a proximal end 1201, a distal end 1209, and a lumen 1205 extending therebetween. In some embodiments, the lumen 1205 may comprise multiple coaxial lumens within the one or more elongate shafts of the shaft 120, e.g., one or more lumens (e.g., axially aligned lumens) that abut one another to form a single lumen 1205. In some embodiments, at least a portion of the shaft 120 comprises a torque shaft. In some embodiments, a portion of the shaft 120 comprises a braided structure. In some embodiments, a portion of the shaft 120 comprises a spiral cut tube (e.g., the shaft 120 comprises a spiral cut metal tube). In some embodiments, the pitch of the spiral cut may vary along the length of the cut, which may cause, for example, the stiffness of the shaft 120 to vary along the length of the shaft 120. A portion of the shaft 120 may be comprised of a tube made of a nickel-titanium alloy. The shaft 120 operatively surrounds an optical core 110 (e.g., a rotatable optical fiber) (e.g., disposed within the lumen 1205), where the optical core 110 comprises a proximal end 1101 and a distal end 1109. The optical core 110 may comprise a dispersion-shifted optical fiber. The dispersion-shifted optical fiber may be, for example, a suppressed cladding dispersion-shifted fiber (e.g., a non-zero dispersion shifted (NZDS) fiber). The shaft 120 further comprises a distal portion 1208. The distal portion 1208 includes a transparent portion, a window 130 (e.g., a window that is relatively transparent to one or more frequencies of light passing through the optical core 110). An optical assembly (optical assembly 115) is operably attached to the distal end 1109 of the optical core 110. The optical assembly 115 is disposed within the window 130 of the shaft 120. The optical assembly 115 may comprise a GRIN lens optically coupled to the distal end 1109 of the optical core 110.The optical assembly 115 may be of a similar construction and arrangement to the optical assemblies described in commonly-owned co-pending U.S. patent application Ser. No. 16 / 764,087, filed May 14, 2020, entitled "IMAGING SYSTEM" (Docket No. GTY-003-US), and commonly-owned co-pending U.S. patent application Ser. No. 17 / 276,500, filed March 16, 2021, entitled "IMAGING SYSTEM WITH OPTICAL PATH" (Docket No. GTY-004-US). In some embodiments, the optical core 110 is comprised of a single continuous length of optical fiber that is seamless along its entire length. In some embodiments, the imaging probe 100 includes a single optical joint. For example, the optical joint is a joint (seam) between the optical assembly 115 and the distal end 1109 of the optical core 110 (e.g., in the case where the optical core 110 is seamless along its entire length).

[0082] A connector assembly (connector assembly 150) is disposed at the proximal end of the shaft 120. The connector assembly 150 operably connects the imaging probe 100 to the rotation assembly 210. In some embodiments, the connector assembly 150 comprises an optical connector fixedly attached to the proximal end of the optical core 110. The imaging probe 100 may comprise a second connector (connector 180) that may be disposed on the shaft 120. The connector 180 may be detachably attached and / or adjustably positioned along the length of the shaft 120. The connector 180 may be positioned along the shaft 120 near the proximal end of the delivery catheter 80 by, for example, a clinician, technician, and / or other user of the system 10 (referred to herein as a "user" or "operator") after the imaging probe 100 has been inserted into a patient via the delivery catheter 80. The shaft 120 may include a portion between the connector assembly 150 and the location of the connector 180 that is configured to provide and / or include slack in the shaft 120 (service loop 185).

[0083] In some embodiments, the shaft 120 comprises a multi-part structure, e.g., an assembly of two or more tubes that may be connected in various ways. In some embodiments, one or more tubes of the shaft 120 may be comprised of polyethylene terephthalate (PET) tubing. In this case, the PET tubing may surround the junction between two axially aligned tubes (e.g., two parts of the shaft 120), for example, to form a bond between the two tubes. In some embodiments, tension is applied to one or more PET tubes after assembly (e.g., the tubes are stretched longitudinally when the shaft 120 is assembled), e.g., to prevent or at least reduce the tendency of the PET tube to wrinkle while the shaft 120 is advanced through a tortuous path. In some embodiments, one or more parts of the shaft 120 include a coating. The coating comprises one, two, or more materials and / or surface modification processes (e.g., to provide a hydrophilic coating or a lubricious coating). In some embodiments, one or more metal portions (e.g., nickel titanium portions) of shaft 120 are surrounded by a tube (e.g., a polymer tube), which, for example, improves adhesion of a coating to that portion of shaft 120.

[0084] The imaging probe 100 may include one or more visualized markers (e.g., marker 131 as shown) along its length (e.g., along the shaft 120). The marker 131 may be one or more markers selected from the group consisting of radiopaque markers, ultrasound reflective markers, magnetic markers, ferrous materials, and combinations of one or more of the foregoing. In some embodiments, the marker 131 is positioned at a location along the imaging probe 100 selected to assist an operator of the system 10 in performing a pullback procedure (referred to herein as a "pullback procedure" or "pullback"). For example, the marker 131 may be positioned approximately one pullback length from the distal end 1209 of the shaft 120, such that after the pullback, the distal end 1209 is no closer to the proximal end than the initial location of the marker 131. In some embodiments, the operator may position the marker 131 at a location distal to the proximal end of the implant prior to the pullback. This maintains access to the implant after the pullback is completed (eg, the imaging probe 100 may be safely advanced through the implant after the pullback).

[0085] In some embodiments, the imaging probe 100 includes a gel 118 that is a viscous damping material (e.g., a gel injected or otherwise provided during the manufacturing process). The gel 118 is disposed within the shaft 120 and surrounds the optical assembly 115 and the distal portion of the optical core 110. The gel 118 may comprise a non-Newtonian fluid (e.g., a shear thinning fluid). In some embodiments, the gel 118 has a static viscosity of 500 centipoise or greater and a shear viscosity that is less than the static viscosity. In this case, the ratio of the static viscosity to the shear viscosity of the gel 118 may be between 1.2:1 and 100:1. In some embodiments, the gel 118 is injected (e.g., during the manufacturing process) from the distal end of the window 130. In some embodiments, the gel 118 includes a gel that is visible (e.g., a gel that is visible under ultraviolet light, such as when the gel 118 includes one or more materials that fluoresce under ultraviolet light). In some embodiments, during the manufacturing process in which gel 118 is injected into shaft 120 through window 130, shaft 120 is monitored while gel 118 is visualized (e.g., illuminated with ultraviolet light) so that the injection process can be controlled (e.g., injection is stopped once gel 118 has advanced sufficiently into shaft 120). Gel 118 may be a gel as described in commonly owned co-pending U.S. patent application Ser. No. 17 / 668,757, entitled "Micro-Optical Probe for Neurology," filed Oct. 12, 2017 (Attorney Docket No. GTY-001-US-CON1), and commonly owned co-pending U.S. patent application Ser. No. 16 / 764,087, entitled "Imaging System," filed May 14, 2020 (Attorney Docket No. GTY-003-US).

[0086] The imaging probe 100 may comprise a distal tip (distal tip 119). In some embodiments, the distal tip 119 may comprise a spring tip. The spring tip may be configured to improve the "navigability" of the imaging probe 100 (e.g., improve the "trackability" and / or "steerability" of the imaging probe 100), such as when the probe 100 is moved through a tortuous path (e.g., through the blood vessels of the brain or heart, which have tortuous paths). In some embodiments, the distal tip 119 has a length of 5 mm to 100 mm (e.g., the distal tip 119 may comprise a spring having a length of 5 mm to 100 mm). In some embodiments, the distal tip 119 may comprise a user-shapeable spring tip (e.g., at least a portion of the distal tip 119 is malleable). The imaging probe 100 may be rotated (e.g., via connector 180) to adjust the orientation of the nonlinear shaping of the distal tip 119 (e.g., to adjust the trajectory of the distal tip 119 in the patient's vasculature). Alternatively or additionally, the distal tip 119 may comprise a cap, plug, and / or other element configured to seal the distal opening of the window 130. In some embodiments, the distal tip 119 may comprise a radiopaque marker configured to enhance visibility of the imaging probe 100 under a fluoroscope or other x-ray device. In some embodiments, the distal tip 119 may comprise a relatively short lumen guidewire pathway, which allows for "rapid exchange" movement of the imaging probe 100 over a guidewire (not shown) of the system 10.

[0087] In some embodiments, the outer diameter of at least the distal portion of the imaging probe 100 (e.g., the distal portion of the shaft 120 that surrounds the optical assembly 115) is 0.030 inches or less (e.g., 0.025 inches or less, 0.020 inches or less, and / or 0.016 inches or less).

[0088] In some embodiments, the imaging probe 100 may be configured (constructed and arranged) for use in an intravascular neurological procedure (e.g., a procedure to visualize blood, blood vessels, and other tissues near the brain and / or visualize devices placed temporarily or permanently near the brain). The overall length of the imaging probe 100 configured for use in an intravascular neurological procedure (also referred to herein as a "neurological procedure") may be 150 cm or more (e.g., about 300 cm). Alternatively or additionally, the imaging probe 100 may be configured (constructed and arranged) for use in an intravascular cardiac procedure (e.g., a procedure to visualize blood, blood vessels, and other tissues near the heart and / or visualize devices placed temporarily or permanently near the heart). The overall length of the imaging probe 100 configured for use in an intravascular cardiac procedure (e.g., also referred to herein as a "cardiac procedure" or "cardiovascular procedure") may be 120 cm or more (e.g., about 280 cm) (e.g., to allow the proximal end of the imaging probe 100 to be placed outside of a sterile field). In some embodiments, for example, when the proximal end of the probe 100 is positioned outside the sterile field, the length of the imaging probe 100 may be 220 cm or more (eg, 220 cm or more and less than 320 cm).

[0089] In some embodiments, the imaging probe 100 includes an element (as shown, FPE 1500) that may be configured as a fluid propulsion element and / or a fluid pressurizing element (herein, "fluid pressurizing element"). The FPE 1500 may be configured to prevent and / or reduce the presence of air bubbles in the gel 118 in the vicinity of the optical assembly 115. The FPE 1500 may be fixedly attached to the optical core 110, where rotation of the optical core 110 also rotates the FPE 1500 to generate a pressure increase in the gel 118 configured to reduce the presence of air bubbles from locations proximate the optical assembly 115, for example. Such one or more fluid pressurizing elements (FPE 1500) may be configured (constructed and arranged) to reduce the likelihood of air bubble formation in the gel 118, to reduce the size of air bubbles in the gel 118, and / or to move air bubbles formed in the gel 118 away from locations that may adversely affect the collection of image data by the optical assembly 115 (e.g., to move air bubbles away from the optical assembly 115). In some embodiments, the fluid propulsion element (FPE1500) of the imaging probe 100 has a configuration (structure and arrangement) similar to the fluid propulsion element described in co-pending U.S. patent application Ser. No. 17 / 600,212 (Docket No. GTY-011-US), entitled “Imaging Probe with Fluid Pressurizing Element,” filed by the applicant on Sep. 30, 2021.

[0090] In some embodiments, the delivery catheter 80 comprises an elongate shaft (shaft 81 as shown). The shaft 81 has a lumen 84 extending therethrough and a connector 82 disposed at a proximal end of the shaft. The connector 82 may comprise a Touhy valve or other valved connector (e.g., a valved connector configured to prevent the exit of fluid from the associated delivery catheter 80) (with or without a separate shaft disposed within the connector 82). The connector 82 may comprise a port 83 (e.g., one or more ports configured (constructed and arranged) to allow for the introduction of fluid into the delivery catheter 80 and / or the removal of fluid from the delivery catheter 80). In some embodiments, a flushing fluid as described herein may be introduced via the one or more ports 83 to, for example, remove blood or other undesirable material from a location proximate the optical assembly 115 (e.g., from a location proximal to the optical assembly 115 to a location distal to the optical assembly 115). Port 83 may be located on a side of connector 82 and may include a luer fitting and a cap and / or a valve. Shaft 81, connector 82, and port 83 may each be constructed of standard materials and may comprise structures similar to commercially available introducers, guide catheters, diagnostic catheters, mid-catheters, and microcatheters used in interventional procedures today. Delivery catheter 80 may comprise a catheter configured to deliver imaging probe 100 to an intracerebral, intracardiac, and / or other location in a patient.

[0091] The delivery catheter 80 may comprise two or more delivery catheters (e.g., three or more delivery catheters). The delivery catheter 80 may comprise at least a vascular introducer and another delivery catheter insertable into the patient (e.g., through the vascular introducer after the vascular introducer has been placed through the patient's skin). The delivery catheter 80 may comprise a set of delivery catheters collectively having a set of different inner diameters (ID) and different outer diameters (OD), such that a first delivery catheter 80 is slidably capable of receiving a second delivery catheter 80 (e.g., the outer diameter of the second delivery catheter is equal to or smaller than the inner diameter of the first delivery catheter), and the second delivery catheter 80 is slidably capable of receiving a third delivery catheter 80 (e.g., the outer diameter of the third delivery catheter is equal to or smaller than the inner diameter of the second delivery catheter). In such a configuration, a first delivery catheter 80 (e.g., its distal end) can be advanced to a first anatomical location, and a second delivery catheter 80 (e.g., its distal end) can be advanced through the first delivery catheter to a second anatomical location that is distal or remote (hereinafter "distal") from the first anatomical location, and similar advancement can be repeated, if necessary, using successively smaller diameter delivery catheters. In some embodiments, the delivery catheter 80 can be configured (constructed and arranged) similarly to similar components described in co-pending U.S. patent application Ser. No. 17 / 350021 (Docket No. GTY-002-US-CON2), filed June 17, 2021 by the applicant and entitled "IMAGING SYSTEM WITH IMAGING PROBE AND DELIVERY DEVICE."

[0092] In some embodiments, the delivery catheter 80 comprises a guide extension catheter (e.g., a catheter having a hollow shaft reinforced with a coil) and a push wire attached to the proximal end of the shaft. The shaft may have a skived (partially circumferential) (arcuate) proximal portion to facilitate insertion of a separate device (e.g., a therapeutic device and / or probe 100) into the shaft.

[0093] The rotating assembly 210 is operably attached to the connector assembly 150 of the imaging probe 100. The rotating assembly 210 may comprise one or more rotational couplings (rotary joints, optical connectors, rotational actuators (e.g., motors), and / or linkages) configured to be operably attached to the optical core 110 to enable and / or cause rotation of the optical core 110. The connector assembly 150 may be configured (constructed and arranged) to be removably attached to the rotating assembly 210 and to enable a rotary connection between the proximal end 1101 and a rotary fiber optic joint (e.g., a fiber optic rotary joint (FORJ)). The rotating assembly 210 may be configured (constructed and arranged) similar to similar components described in commonly-owned co-pending U.S. patent application Ser. No. 16 / 764,087, filed May 14, 2020, entitled "IMAGING SYSTEM" (Attorney Docket No. GTY-003-US), and commonly-owned co-pending U.S. patent application Ser. No. 17 / 276,500, filed March 16, 2021, entitled "IMAGING SYSTEM WITH OPTICAL PATH" (Attorney Docket No. GTY-004-US). The rotating assembly 210 may be configured to rotate the optical core 110 at a speed of 100 revolutions per second or more (e.g., 200 revolutions per second or more, or 250 revolutions per second or more), or at a speed of 20 to 1000 revolutions per second. The rotating assembly 210 may comprise a rotational actuator selected from the group consisting of a motor, a servo, a stepper motor (e.g., a stepper motor with a gear box), an actuator, a hollow core motor, and combinations thereof. In some embodiments, the rotation assembly 210 is configured to rotate the optical assembly 115 and the optical core 110 together.

[0094] The retraction assembly 220 is operably attached to the imaging probe 100, for example to retract the imaging probe 100 relative to an access site on a patient. The retraction element 2210 may be operably attached to the retraction assembly 220 and the imaging probe 100, for example to transmit a retraction force from the retraction assembly 220 to the imaging probe 100. The retraction element 2210 may include a conduit 2211 surrounding a link 2212. The link 2212 is slidably received within the conduit 2211. The retraction element 2210 may include a connector 2213 operably connected to the retraction assembly 220, such that the retraction assembly 220 can retract the link 2212 relative to the conduit 2211. In some embodiments, the conduit 2211 includes a connector 2214 that is operably attached to a reference point near the patient access site (e.g., connector 82 of delivery catheter 80), e.g., to establish a reference point for retraction of the imaging probe 100 relative to the patient. The connector 2214 can be attached to a reference point, e.g., by attachment to a patient introduction device, a surgical table, and / or another fixed or semi-fixed reference point. The coupling 2212 is removably attached to the connector 180 of the imaging probe 100. The retraction assembly 220 retracts at least a portion of the imaging probe 100 (e.g., a portion of the imaging probe 100 distal to the attached connector 180) relative to the established reference by retracting the coupling 2212 relative to the conduit 2211 (e.g., by retracting a portion of the coupling 2212 that exits a portion of the conduit 2211, as shown). In some embodiments, the retraction assembly 220 is configured to retract at least a portion of the imaging probe 100 (e.g., at least a portion of the optical assembly 115 and the shaft 120) at a speed between 5 mm / sec and 200 mm / sec, or between 5 mm / sec and 100 mm / sec (e.g., about 60 mm / sec).Additionally or alternatively, a pullback procedure may be performed (e.g., over a distance of 100 mm at 5 mm / sec) for a period of 0.5 seconds to 25 seconds (e.g., about 20 seconds). The service loop 185 of the imaging probe 100 may be disposed between the connector 180 and the rotating assembly 210, such that the imaging probe 100 may be retracted relative to the patient while the rotating assembly 210 remains stationary (e.g., attached to a portion of the operating table and / or console 300).

[0095] The retraction assembly 220 further comprises a power element configured to retract the linkage 2212. In some embodiments, the power element comprises a linear actuator, a worm drive operably attached to a motor, a pulley system, and / or other linear power transmission mechanism. The linkage 2212 may be operably attached to the power element via one or more links and / or connectors. The retraction assembly 220 may be configured (constructed and arranged) similarly to like components described in co-pending U.S. patent application Ser. No. 16 / 764,087, filed May 14, 2020, entitled "IMAGING SYSTEM" by the applicant (Docket No. GTY-003-US).

[0096] In some embodiments, the PIU 200 may comprise a single individual component (e.g., a single housing) capable of housing both the rotating assembly 210 and the retracting assembly 220. Alternatively or additionally, the PIU 200 may comprise two or more individual components (e.g., two or more housings), e.g., provided as individual components for each of the rotating assembly 210 and the retracting assembly 220. In some embodiments, the connector assembly 150, the service loop 185, the retraction element 2210, and the connector 2213 are housed within a single individual component (e.g., a single housing) and configured to be operably connected to both the rotating assembly 210 and the retracting assembly 220 (e.g., when the rotating assembly 210 and the retracting assembly 220 are housed within a single housing or single individual component).

[0097] In some embodiments, the system 10 includes a second imaging device 15 that is an ancillary imaging device (e.g., in addition to the imaging probe 100). The second imaging device 15 may be comprised of one or more imaging devices selected from the group consisting of, for example, an x-ray, a fluoroscopy device (e.g., a single-plane or biplane fluoroscopy device), a CT scanner, an MRI, a PET scanner, an ultrasound imaging device, and combinations of one or more of the foregoing. In some embodiments, the second imaging device 15 includes a device configured to perform rotational angiography.

[0098] In some embodiments, system 10 includes a treatment device 16, which is a device configured to treat a patient (e.g., provide one or more therapies to a patient). Treatment device 16 may include an occlusion treatment device and / or other treatment device. The occlusion treatment device and / or other treatment device is selected from the group consisting of a balloon catheter configured to dilate a narrowing in a blood vessel, a drug eluting balloon, an aspiration catheter, an ultrasonic disintegrator, an atherectomy device, a thrombectomy device (e.g., a stent retriever), a Trevo™ stentriever, a Solitaire™ stentriever, a Revive™ stentriever, an Eric™ stentriever, a Lazarus™ stentriever, a stent delivery catheter, a microblade implant, an embolization system, a WEB™ embolization system, a Luna™ embolization system, a Medina™ embolization system, and combinations of one or more of the foregoing. In some embodiments, the imaging probe 100 and / or other components of the system 10 are configured to collect data related to the treatment device 16 (e.g., position, orientation, and / or other configuration data of the treatment device 16) after the treatment device 16 is inserted into the patient.

[0099] System 10 may further include one or more devices (e.g., the illustrated patient monitor 17) configured to monitor one, two, or more physiological and / or other parameters of the patient. Patient monitor 17 may include one or more monitoring devices selected from the group consisting of an ECG monitor, an EEG monitor, a blood pressure monitor, a blood flow monitor, a respiratory monitor, a patient motion monitor, a T-wave trigger monitor, and combinations thereof.

[0100] The system 10 may further include one or more fluid injectors (e.g., the illustrated injector 20). Each injector 20 may be configured to inject one or more fluids (e.g., the illustrated injectate 21) (e.g., flushing fluid, imaging contrast agent (e.g., radiopaque contrast agent), and / or other fluids). The injectors 20 may include a power injector, a syringe pump, a peristaltic pump, or other fluid delivery device configured to inject imaging contrast agent (e.g., radiopaque contrast agent) and / or other fluids. In some embodiments, the injectors 20 are configured to deliver imaging contrast agent and / or other fluids (e.g., contrast agent, saline, and / or dextran). In some embodiments, the injectors 20 deliver fluids in a flushing procedure (cleansing procedure) as described herein. In some embodiments, the injector 20 delivers the contrast agent or other fluid through a delivery catheter 80 having an inner diameter of 5 Fr to 9 Fr, a delivery catheter 80 having an inner diameter of 0.53 inches to 0.70 inches, or a delivery catheter 80 having an inner diameter of 0.0165 inches to 0.027 inches. In some embodiments, the contrast agent or other fluid is delivered through a delivery catheter as small as 4 Fr (e.g., for distal injection). In some embodiments, the injector 20 delivers the contrast agent and / or other fluid through the lumen of the delivery catheter 80, within which there are also one or more smaller delivery catheters 80. In some embodiments, the injector 20 is configured to deliver two different fluids simultaneously and / or sequentially (e.g., a first fluid delivered from a first reservoir and containing a first concentration of contrast agent, and a second fluid delivered from a second reservoir and containing less or no contrast agent).

[0101] The injectate 21 may include a fluid selected from the group consisting of an optically transparent material, saline, a visualizable material, a contrast agent, dextran, an ultrasound reflective material, a magnetic material, and combinations thereof. The injectate 21 may include a contrast agent and saline. The injectate 21 may include 20% or more of a contrast agent. During collection of image data (e.g., during pullback), a flushing procedure may be performed to remove blood or other slightly opaque material (hereinafter opaque material) in the vicinity of the optical assembly 115 (e.g., to remove opaque material between the optical assembly 115 and the delivery catheter and / or between the optical assembly 115 and a blood vessel wall), such as by delivering one or more fluids (e.g., the injectate 21 propelled by the injector 20 or other fluid delivery device). This allows, for example, light dispensed from the optical assembly 115 to reach and reflect back to all tissues and other objects being imaged. In such flushing embodiments, injectate 21 may include an optically transparent material (e.g., saline). Injectate 21 may also include one or more visualizeable materials, as described herein.

[0102] Alternatively, or in addition to use in a flushing procedure, the injectate 21 may include a material configured to be viewed by a second imaging device 15. For example, the injectate 21 may include a contrast agent configured to be viewed by a second imaging device 15 including a fluoroscope and / or other x-ray device, an ultrasound reflective material configured to be viewed by a second imaging device 15 including an ultrasound imager, and / or a magnetic material configured to be viewed by a second imaging device 15 including an MRI.

[0103] The system 10 may further include an implant (e.g., implant 31). The implant 31 may be implanted into a patient via a delivery device (e.g., implant delivery device 30 and / or delivery catheter 80). The implant 31 may include an implant (e.g., a temporary or chronic implant), for example, to treat a vascular occlusion and / or an aneurysm. In some embodiments, the implant 31 includes one or more implants selected from the group consisting of a flow diverter, a Pipeline™ flow diverter, a Surpass™ flow diverter, an embolic coil, a stent, a Wingspan™ stent, a covered stent, an aneurysm treatment implant, and combinations of one or more of the foregoing.

[0104] The implant delivery device 30 may include a catheter and / or other tools used to deliver the implant 31, such as, for example, if the implant 31 includes a self-expanding or balloon-expandable portion. In some embodiments, the system 10 includes an imaging probe 100, one or more implants 31, and / or one or more implant delivery devices 30. In some embodiments, the imaging probe 100 is configured to collect data related to the implant 31 and / or the implant delivery device 30 (e.g., the anatomical position, orientation, and / or other configuration data of the implant 31 and / or the implant delivery device 30) after the implant 31 and / or the implant delivery device 30 are inserted into a patient.

[0105] In some embodiments, one or more of the system components (e.g., the second imaging device 15, the treatment device 16, the patient monitor 17, the injector 20, the implant delivery device 30, the delivery catheter 80, the imaging probe 100, the PIU 200, the rotation assembly 210, the retraction assembly 220, and / or the console 300) further comprises one or more functional elements (herein "functional elements") (e.g., functional elements 99a, 99b, 99c, 99d, 99e, 89, 199, 299, 219, 229, 399 as shown). Each functional element may include at least two functional elements. Each functional element may include one or more elements selected from the group consisting of a sensor, a transducer, and combinations thereof. The functional element may include a sensor configured to generate a signal. The functional elements may include sensors selected from the group consisting of physiological sensors, pressure sensors, strain gauges, position sensors, GPS sensors, accelerometers, temperature sensors, magnetic sensors, chemical sensors, biochemical sensors, protein sensors, flow sensors (e.g., ultrasonic flow sensors), gas detection sensors (e.g., ultrasonic bubble detectors), sound sensors (e.g., ultrasonic sensors), and combinations thereof. The sensors may include physiological sensors selected from the group consisting of pressure sensors (e.g., blood pressure sensors), blood gas sensors, flow sensors (e.g., blood flow sensors), temperature sensors (e.g., blood or other tissue temperature sensors), and combinations thereof. The sensors may include position sensors configured to generate signals related to a vascular pathway geometry (e.g., a 2-dimensional or 3-dimensional vascular pathway geometry). The sensors may include magnetic sensors. The sensors may include flow sensors. The system may further include algorithms configured to process signals generated by the sensor-based functional elements. Each functional element may include one or more transducers.Each functional element may include one or more transducers selected from the group consisting of a heating element (e.g., a heating element configured to provide sufficient heat for tissue ablation), a cooling element (e.g., a cooling element configured to provide cryogenic energy for tissue ablation), an acoustic transducer (e.g., an ultrasonic transducer), a vibration transducer, and combinations thereof.

[0106] In some embodiments, the overall length of the imaging probe 100 is 120 cm or more (e.g., 160 cm or more) (e.g., about 280 cm). In some embodiments, the overall length of the imaging probe 100 is 350 cm or less. In some embodiments, the imaging probe 100 has a length configured to be inserted into a patient (herein, an "insertable length") of 90 cm or more (e.g., 100 cm or more) (e.g., about 145 cm). In some embodiments, the imaging probe 100 has an insertable length of 250 cm or less (e.g., 200 cm or less). In some embodiments, the distal tip 119 comprises a spring tip (elastic tip) having a length of 5 mm or more (e.g., 25 mm or more) (e.g., about 15 mm). In some embodiments, the distal tip 119 comprises a spring tip (elastic tip) having a length of 75 mm or less (e.g., 30 mm or less). In some embodiments, the distal portion of shaft 120 (e.g., window 130) has an outer diameter of less than 2 Fr (e.g., less than 1.4 Fr) (e.g., about 1.1 Fr). In some embodiments, the distal portion of shaft 120 (e.g., window 130) has an outer diameter of 0.5 Fr or more (e.g., 0.9 Fr or more). In some embodiments, shaft 120 may comprise one or more materials selected from the group consisting of polyetheretherketone (PEEK), nylon, polyether block amide, nickel titanium alloy, and combinations thereof.

[0107] In some embodiments, at least a portion (e.g., the most flexible portion) of the imaging probe 100 is configured to be safely and effectively positioned with a small radius of curvature (e.g., 5 mm, 4 mm, 3 mm, 2 mm, and / or 1 mm). In some embodiments, the optical core 110 comprises an optical fiber. The optical fiber has a diameter of less than 120 μm (e.g., less than 100 μm, less than 80 μm, less than 60 μm) (e.g., about 40 μm). In some embodiments, the numerical aperture of the optical core 110 is one or more of 0.11, 0.14, 0.16, 0.17, 0.18, 0.20, and / or 0.25. In some embodiments, the optical assembly 115 comprises a lens selected from the group consisting of a GRIN lens, a molded lens (e.g., a fused lens and a polished lens), a lens with an axicon structure (e.g., an axicon nanostructure), and combinations thereof. In some embodiments, the optical assembly 115 comprises a lens having an outer diameter of less than 200 μm (e.g., less than 170 μm, less than 150 μm, or less than 100 μm) (e.g., about 80 μm). In some embodiments, the optical assembly 115 comprises a lens having a length of less than 3 mm (e.g., less than 1.5 mm). In some embodiments, the optical assembly 115 comprises a lens having a length of 0.5 mm or more (e.g., 1 mm or more). In some embodiments, the optical assembly 115 comprises a lens having a focal length of 0.5 mm or more and / or 5.0 mm or less (e.g., 1.0 mm or more and / or 3.0 mm or less) (e.g., about 0.5 mm). In some embodiments, the optical assembly 115 may have a longer focal length, for example to be able to observe structures outside of a blood vessel into which the optical assembly 115 is inserted. In some embodiments, the optical assembly 115 has a working distance (also referred to as depth of field, confocal length, or Rayleigh range) of, for example, 1 mm or less, 5 mm or less, or 10 mm or less (e.g., 1 mm or more and / or 5 mm or less).In some embodiments, the optical assembly 115 has an outer diameter of 80 μm or more and / or 200 μm or less (e.g., 150 μm or more and / or 170 μm or less) (e.g., about 150 μm). In some embodiments, the system 10 (e.g., the retraction assembly 220) is configured to perform a pullback of the imaging probe 100 at a speed of 10 mm / sec or more and / or 300 mm / sec or less (e.g., 50 mm / sec or more and / or 200 mm / sec or less) (e.g., a pullback speed of about 100 mm / sec). In some embodiments, the system 10 (e.g., the retraction assembly 220) is configured to perform a pullback over a distance of 25 mm or more and / or 200 mm or less (e.g., 25 mm or more and / or 150 mm or less) (e.g., a distance of about 50 mm). In some embodiments, the system 10 (e.g., retraction assembly 220) is configured to perform the pullback over a period of 0.2 seconds or more and / or 5.0 seconds or less (e.g., a period of 0.5 seconds or more and / or 2.0 seconds or less) (e.g., a period of about 1.0 seconds). In some embodiments, the system 10 (e.g., rotation assembly 210) is configured to rotate the optical core 110 at an angular velocity of 20 revolutions per second or more and / or 1000 revolutions per second or less (e.g., an angular velocity of 100 revolutions per second or more and / or 500 revolutions per second or less) (e.g., an angular velocity of about 250 revolutions per second). In some embodiments, the delivery catheter 80 has an inner diameter of 0.016 inches or more and / or 0.050 inches or less (e.g., an inner diameter of 0.016 inches or more and / or 0.027 inches or less) (e.g., an inner diameter of about 0.021 inches).

[0108] In some embodiments, the console 300 includes an imaging assembly 320. The imaging assembly 320 may be configured to provide light to the optical assembly 115 (e.g., via the optical core 110) and collect light from the optical assembly 115 (e.g., via the optical core 110). The imaging assembly 320 may include a light source 325. The light source 325 may be comprised of one or more light sources (e.g., one or more light sources configured to provide light of one or more wavelengths to the optical assembly 115 via the optical core 110). The light source 325 is configured to provide light to the optical assembly 115 (e.g., via the optical core 110) so that image data can be collected that includes cross-sectional, longitudinal, and / or volumetric information related to the patient region or implanted device being imaged. The light source 325 may be configured to provide light such that the collected image data includes characteristics of tissue within the patient region being imaged. This may allow for quantifying, qualifying, or otherwise providing information related to a patient disease or disorder present within the patient region being imaged. The light source 325 may be configured to emit broadband light, with the center wavelength of the light source 325 being in the range of 350 nm to 2500 nm, in the range of 800 nm to 1700 nm, or in the range of 1280 nm to 1310 nm, or about 1300 nm (e.g., light is emitted in a sweep range of 1250 nm to 1350 nm). The light source 325 may have a sweep rate of 20 kHz or more. In some embodiments, the light source 325 has a sweep rate of 100 kHz or more (e.g., 200 kHz or more, 300 kHz or more, 400 kHz or more, and / or 500 kHz or more) (e.g., about 200 kHz). Such a fast sweep rate provides a number of advantages (over a similar system having a slower sweep rate), such as, for example, a faster frame rate and the ability to accommodate faster pullback and rotation speeds. For example, a fast sweep speed allows a desired sampling density (eg, the amount of lumen surface area swept by the rotating beam) to be achieved in a shorter time.This is advantageous in most situations, especially when there is relative motion between the probe and the surface (tissue) being imaged (e.g., the arteries of a beating heart). The bandwidth of the light source 325 may be selected to achieve a desired resolution (which varies depending on the application of the system 10). In some embodiments, the bandwidth is about 5%-15% of the central wavelength, which allows for a resolution of 20 μm-5 μm. The light source 325 may be configured to emit light at a power level that meets ANSI (American National Standards) Class 1 ("eye safety") limits, although higher power levels may be used. In some embodiments, the light source 325 emits light in the 1.3 μm band at a power level of about 20 mW. As the central wavelength of the emitted light increases, tissue light scattering decreases, but water absorption increases. The light source 325 may emit light at a wavelength of about 1300 nm to balance these two effects. The light source 325 may be configured to illuminate a shorter wavelength of light (e.g., about 800 nm light) across the region of the patient being imaged (including a large volume of fluid). Alternatively or additionally, the light source 325 may be configured to illuminate a longer wavelength of light (e.g., about 1700 nm light), for example, to reduce high levels of scattering within the region of the patient being imaged. In some embodiments, the light source 325 includes a tunable light source (e.g., a light source that emits a single wavelength that is repeatedly changed over time) and / or a broadband light source. The light source 325 may include a single spatial mode light source or a multimode light source (e.g., a multimode light source with spatial filtering).

[0109] The light source 325 may have a relatively long effective coherence length (e.g., greater than 10 nm) (e.g., 50 mm or longer) at all frequencies within the bandwidth of the light source. Such coherence length performance allows a longer effective scan range to be realized by the system 10 because the light returning from the distant object (e.g., tissue) being imaged must maintain phase coherence with the returning reference light to produce detectable interference fringes. For swept-source lasers, the instantaneous linewidth is very narrow (i.e., when the laser is sweeping, it is outputting a very narrow band of frequencies that changes with the sweep rate). Similarly, for broadband sources, the detector configuration must be able to select a very narrow linewidth from the spectrum of the light source. The coherence length is inversely proportional to the linewidth. Longer scan ranges allow imaging of larger or more distant objects (e.g., imaging of more distal tissues). Current systems have a small coherence length, which correlates with reduced image capture range and artifacts (ghosting) resulting from objects outside the effective scanning range.

[0110] In some embodiments, light source 325 has a sweep bandwidth of 30 nm or more and / or 250 nm or less (e.g., a sweep bandwidth of 50 nm or more and / or 150 nm or less) (e.g., a sweep bandwidth of about 100 nm). In some embodiments, light source 325 has a central wavelength of 800 nm or more and / or 1800 nm or less (e.g., a central wavelength of 1200 nm or more and / or 1350 nm or less) (e.g., a central wavelength of about 1300 nm). In some embodiments, light source 325 has an optical output of 5 mW or more and / or 500 mW or less (e.g., an optical output of 10 mW or more and / or 50 mW or less) (e.g., an optical output of about 20 mW).

[0111] The system 10 may include one or more operatively connecting cables or other conduits (bus 58 as shown). The bus 58 may operatively connect the PIU 200 to the console 300, the rotating assembly 210 to the console 300 (as shown), the retracting assembly 220 to the console 300, and / or the rotating assembly 210 to the retracting assembly 220. The bus 58 may include one or more optically transmitting fibers, wires, traces, and / or other electrical transmitting cables, fluid conduits, and combinations of one or more of the above. In some embodiments, the bus 58 includes at least an optically transmitting fiber that optically couples the rotating assembly 210 to the imaging assembly 320 of the console 300. Additionally or alternatively, the bus 58 includes at least a power transmission cable and / or a data transmission cable that transfers power and / or drive signals to one or more power elements of the rotating assembly 210 and / or the retracting assembly 220.

[0112] The console 300 may include a processing unit 310. The processing unit 310 may be configured to perform and / or facilitate one or more functions of the system 10 (e.g., one or more processes, energy supply (e.g., light energy supply), data collection, data analysis, data transfer, signal processing, and / or other functions). The processing unit 310 may include a processor 312, a memory 313, and / or algorithms 315, as shown. The memory 313 may store instructions for executing the algorithms 315 and may be coupled to the processor 312. The system 10 may include an interface (user interface 350) for providing information to and / or receiving information from an operator of the system 10. The user interface 350 may be integrated into the console 300, as shown. In some embodiments, the user interface 350 may comprise a component separate from the console 300 (e.g., a display separate from but operably coupled to the console 300). User interface 350 may include one, two, or more user input components and / or user output components. For example, user interface 350 may include a joystick, a keyboard, a mouse, a touch screen, and / or another human interface device (user input device 351 shown). In some embodiments, user interface 350 includes a display (display 352 shown) (e.g., a touch screen display). In some embodiments, processor 312 may provide a graphical user interface (GUI 353). GUI 353 may be displayed on and / or provided by display 352. User interface 350 may include input devices and / or output devices.The input and / or output devices may be selected from the group consisting of speakers, indicator lights (e.g., LED indicators), tactile feedback devices, foot pedals, switches (e.g., momentary switches), microphones, cameras (e.g., where the processor 312 enables eye-tracking and / or other input via image processing), and combinations thereof.

[0113] In some embodiments, the system 10 includes a data storage and processing device (server 400). The server 400 stores (e.g., patient image data) Alternatively or additionally, server 400 may comprise a cloud-based server. Server 400 may include a processing unit 410 as shown. Processing unit 410 may be configured to perform one or more functions of system 10 (e.g., one or more functions described herein). Processing unit 410 may include one or more algorithms (algorithms 415). Processing unit 410 may include a memory (not shown) that stores instructions for executing algorithms 415. Server 400 may be configured to receive and store various types of data 420 (e.g., image data, diagnostic data, planning data, and / or outcome data as described herein). In some embodiments, the data 420 may include data collected from multiple patients (e.g., multiple patients treated with the system 10) (e.g., data collected during and / or after a clinical procedure in which image data is collected from the patients via the system 10). For example, image data may be collected via the imaging probe 100, recorded by the processing unit 310 of the console 300, and transmitted to the server 400 for analysis. In some embodiments, the console 300 and the server 400 may be in communication over a network (e.g., a wide area network such as the Internet). Alternatively or additionally, the system 10 may include a virtual private network (VPN) through which various devices of the system 10 transfer data.

[0114] As described herein, one or more functions of system 10 performed by processing unit 310 and / or processing unit 410 may be performed by either or both processing units. For example, in some embodiments, image data is collected and pre-processed by processing unit 310 of console 300. The pre-processed image data is then transferred to server 400 where the image data is further processed. The processed image data is then transferred to console 300 and displayed to an operator (e.g., via GUI 353). In some embodiments, one or more images in a first set based on the first set of image data (e.g., images processed locally via processing unit 310) are displayed to an operator after collection of the image data (e.g., in near real-time), and then a second image based on the first set of image data (e.g., images processed remotely via processing unit 410) is displayed to the operator (e.g., the first image is displayed while the second image is being processed).

[0115] In some embodiments, the algorithm 315 is configured to adjust (e.g., automatically and / or semi-automatically adjust) one or more operating parameters of the system 10 (e.g., operating parameters of the console 300, the imaging probe 100, and / or the delivery catheter 80). Additionally or alternatively, the algorithm 315 may be configured to adjust operating parameters of a separate device (e.g., the injector 20 and / or the implant delivery device 30 described herein). In some embodiments, the algorithm 315 is configured to adjust the operating parameters based on one or more sensor signals (e.g., sensor signals provided by a sensor-based functional element of the inventive concepts described herein). The algorithm 315 may be configured to adjust (e.g., automatically adjust or recommend adjustment) the operating parameters. The operating parameters are selected from the group consisting of rotational parameters (e.g., rotational speed of the optical core 110 and / or the optical assembly 115), retraction parameters of the shaft 120 and / or the optical assembly 115 (e.g., retraction speed, distance, start position, end position, and / or retraction start timing (e.g., when retraction is initiated)), position parameters (e.g., position of the optical assembly 115), row spacing parameters (e.g., number of rows per frame), image display parameters (e.g., scaling of display size relative to vessel diameter, configuration parameters of the imaging probe 100), parameters of the injectate 21 (e.g., a ratio of saline to contrast configured to determine an appropriate refractive index), parameters of the light source 325 (e.g., a power delivered and / or a frequency of light delivered), and combinations of one or more of these. In some embodiments, the algorithm 315 is configured to adjust (e.g., automatically adjust or recommend adjustment of) the retraction parameters (e.g., a parameter that triggers the initiation of a pullback (e.g., a pullback initiated based on a parameter)).The retraction parameters are selected from the group consisting of flushing of the lumen (the lumen adjacent the optical assembly 115 being sufficiently cleared of blood or other material that would interfere with imaging), receiving an indicator signal (e.g., a signal indicating sufficient flushing fluid has been delivered) from the injector 20, a change in collected image data (e.g., detection of a change in the image based on collected image data that correlates to adequate evacuation of blood from around the optical assembly 115), and a combination of one or more of the above. In some embodiments, the algorithm 315 is configured to adjust system 10 configuration parameters associated with the imaging probe 100. For example, the algorithm 315 identifies the attached imaging probe 100 (e.g., automatically identifies via RFID or other embedded ID) and adjusts the system 10 parameters. The system 10 parameters may be, for example, optical path length parameters, dispersion parameters, catheter type parameters, “enabled function” parameters (e.g., parameters that lock and / or unlock the use of system 10 functions), calibration parameters (e.g., optical length to physical length conversion parameters), and / or other parameters listed above. In some embodiments, the console 300 is configured to record one or more metrics (measurements) related to the performance of the imaging probe 100 (e.g., brightness score). The metrics may be encoded into the probe 100 during use (e.g., encoded into an on-board memory of the probe 100, such as a writeable RFID tag). Additionally or alternatively, fault information may be encoded into the probe 100 (e.g., written to an RFID tag), for example, when a fault occurs and / or is detected by the system 10. For example, the fault information may include the date and time of image loss and / or other diagnostic information (e.g., calibration failure, etc.).

[0116] In some embodiments, the algorithm 315 is configured to trigger the initiation of the pullback based on a time gate parameter. In some embodiments, a T-wave trigger (e.g., provided by another device) may be provided to the console 300 to initiate the pullback when a low motion portion of the cardiac cycle is detected. As an alternative or in addition to a T-wave trigger, a motion pattern (e.g., a relative motion pattern) may be tracked (e.g., using angiography) between one or more portions (e.g., components or other features) of the imaging probe 100 and relatively stable (e.g., non-moving) portions of the patient's anatomy (e.g., the ribs, sternum, and / or spine).

[0117] When the console 300 of the system 10 is first installed at a clinical site (e.g., a catheter lab), a calibration routine (e.g., a calibration routine used to establish delays between the clinical site's angiography system (e.g., second imaging device 15) and other components in the system 10) may be performed. Essentially, the imaging probe 100 is provided, the clinical site's angiography system is activated, and an angiography image feed is provided to the console 300 (e.g., using any standard video connection, analog or digital). The video frames provided by the angiography system are registered according to the clock of the console 300 and used as a reference time frame. A pullback of the imaging probe 100 (e.g., in a patient or non-patient simulation mode) is initiated (coordinated by the clock of the console 300) and an angiogram (e.g., device 15) is captured. A trained operator (e.g., a clinician and / or technician) may review the angiography image frames and designate the first frame where motion was detected. This process establishes the associated delays according to the clock of the console 300. Motion detection may be automated, for example, using a neural network or other algorithm (e.g., algorithms 315 and / or 415) trained to recognize motion of the imaging probe 100 under angiography (e.g., motion of a marker band on the imaging probe 100).

[0118] In some embodiments, a calibration process for establishing delays between the angiography system (e.g., the second imaging device 15) and other components in the system 10, and imaging processes performed during the relatively low motion of the cardiac cycle, includes the following steps: In a first step, an angiogram is started once the probe 100 is inserted into the patient and positioned within the target anatomy. In a second step, the system 10 analyzes the relative motion (e.g., the motion of a marker band or other part of the imaging probe 100 that follows the patient's heartbeat) between one or more parts of the imaging probe 100 and more stable features in the image (e.g., an image of the sternum or spine). Once the cardiac rhythm is established and low motion parts are identified (typically 5-10 cardiac cycles are used for this analysis, using velocity vector analysis, neural network analysis, etc.), an indicator is provided and the "metronome" of the system 10 is started. The system 10 may refer to the output of the metronome, for example, when a radiopaque flushing material is injected to clear blood from the target area being imaged, because during such flushing, one or more portions of the imaging probe 100 (e.g., one or more marker bands) may become radiopaque (e.g., the radiopaque portions of the probe 100 are indistinguishable from the flushing material). In another embodiment, a flushing material that is not radiopaque (e.g., dextran) may be used. In a third step, flushing is initiated, for example, by an operator or by a method controlled automatically by the system 10. The flushing is continued for multiple cardiac cycles (e.g., 3-5 cardiac cycles). In a fourth step, the system 10 analyzes one or more images generated by the system 10 to detect the clearance of the blood vessel being imaged.In a fifth step, pullback is initiated during a low motion portion of the metronome (e.g., an expected low motion portion of the cardiac cycle), taking into account previously established delays between the components of the system 10 and the angiography system. In some embodiments, for example, the pullback ends about halfway through the cardiac cycle or less, such that the capture of all or part of the image data remains within the low motion portion of the cardiac cycle. The system 10 may be configured to provide a pullback speed of 50 mm / sec or more (e.g., 100 mm / sec or more, or 200 mm / sec or more). In a sixth step, a pullback sequence of images with minimal motion artifacts is provided to the operator and / or used for CFD calculations (as described herein), implant (e.g., stent) length measurements, etc. As described herein, using image capture during low motion avoids or at least reduces errors associated with motion artifacts (e.g., longitudinal motion artifacts in particular).

[0119] In some embodiments, the algorithm 315 and / or algorithm 415 (also referred to as "algorithm 315, 415") are configured to perform various image processing of image data generated by the system 10. The algorithm 315, 415 may include one, two, or more artificial intelligence algorithms configured to perform various image processing and / or other calculations, as described herein. For example, the algorithm 315, 415 may comprise a neural network implemented using functionality of the DDNet and / or UNet techniques (e.g., functionality tuned for processing and segmentation of intravascular image data). In some embodiments, the algorithm 315, 415 may include one or more algorithms similar to those described herein with reference to FIG. 2.

[0120] The system 10 may be configured to allow an operator to modify one or more of the algorithms 315, 415. In some embodiments, the algorithms 315, 415 may include one or more biases (e.g., biases toward false positives or false negatives). In some embodiments, the algorithms 315, 415 include a bias to more accurately identify larger side branches at the expense of falsely identifying smaller side branches, as described herein. In some embodiments, the system 10 is configured to allow an operator to create and / or modify (e.g., via the user interface 350) the biases of one or more of the algorithms 315, 415.

[0121] The algorithms 315, 415 may include one or more algorithms configured to perform one or more image processing applications, e.g., selected from the group consisting of image quality assessment, procedural device segmentation (e.g., guide catheter and / or guidewire segmentation), implant segmentation (e.g., intravascular implant segmentation such as stents and / or flow diverters), lumen segmentation (e.g., vessel lumen segmentation), side branch segmentation, tissue characterization (e.g., atherosclerosis vs. normal characterization), thrombus detection, and combinations thereof.

[0122] In some embodiments, the algorithms 315, 415 include various signal and / or image processing algorithms configured to process and / or analyze image data collected by the system 10. Using these algorithms, the system 10 may be configured to perform an automated quantification of one or more parameters, which may be, for example, one or more patient parameters (e.g., parameters related to the patient's health), one or more image parameters (e.g., parameters related to the quality of the image data), one or more treatment parameters (e.g., parameters related to the clinical effect and / or technical proficiency of the treatment performed), and combinations thereof. For example, the system 10 may include metrics (e.g., data metrics 525 shown). The data metrics 525 may include results calculated using and / or based on an analysis (e.g., mathematical analysis) of the various parameters described above.

[0123] The data metrics 525 may represent a quantification of the quality of the image data (e.g., a quantification determined by an automated process of the system 10). In some embodiments, the data metrics 525 may include a "confidence metric" that represents the quality of the results of an image processing step (e.g., a segmentation process). The data metrics 525, including the confidence metric, may represent a calculated level of accuracy of the image data determined by the system 10 (i.e., the level of "confidence" that an operator of the system 10 may have in the data being presented). In some embodiments, if the data metrics 525 comprise a confidence metric that falls below a first threshold (e.g., a value indicative of low confidence), the system 10 alerts the operator, e.g., via an indicator displayed to the operator via the GUI 353. Additionally or alternatively, the system 10 may be configured to not display any image data if the confidence metric associated with that image data falls below a second threshold (e.g., a value indicative of less confidence than the first threshold). In some embodiments, the system 10 may be configured to display a warning to the operator (e.g., a warning of unreliable data) and / or to prompt the operator to allow the display of unreliable image data.

[0124] In some embodiments, the data metrics 525 include a quantification of one or more characteristics (e.g., attachment level or protrusion amount) representative of an interaction between a patient's anatomy and a therapeutic device (e.g., implant 31) implanted in the patient. For example, the system 10 may be configured to analyze image data collected before, during, and / or after implantation of the implant and to determine one or more values ​​of the data metrics 525 representative of (e.g., corresponding to) an interaction between the implant and a patient's tissue (e.g., a vessel wall, an ostium of one or more side branches, and / or aneurysm necks).

[0125] In some embodiments, the data metrics 525 include metrics related to healing near the implantation site, for example, when the system 10 is used to collect image data from the implantation site during a follow-up procedure (e.g., a procedure performed at least one month, at least six months, or at least one year after the implantation procedure).

[0126] In some embodiments, data metrics 525 include metrics related to predicted outcomes of an intervention procedure (e.g., metrics whose values ​​are calculated and / or updated during, after, or both). For example, data metrics 525 may be used to provide guidance to an operator by indicating predicted outcomes (e.g., based on an analysis of the potential effectiveness of an intervention) of an intended (e.g., future) and / or already performed intervention (e.g., an intervention configured to treat a cerebral aneurysm and / or ischemic stroke). For example, the mesh density of a flow diverter covering the neck of an aneurysm may be estimated by system 10 (e.g., based on automated image processing as described herein). The mesh density may be used to predict the outcome of the intervention (e.g., long-term dissolution of the aneurysm). Additionally or alternatively, the shape of the mesh may be used to estimate the angle of optical assembly 115 relative to the surface of the mesh and modify the mesh density accordingly. For example, at a curved portion, light exiting the optical assembly 115 (e.g., light rays emanating from the optical assembly 115) may be along an oblique angle relative to the normal to the mesh surface. In this case, the mesh pattern is elongated in the plane of incidence (e.g., the plane defined by the surface normal and the light rays) according to the angle of the light rays. By modifying the elongation of the mesh pattern to achieve a symmetrical pattern, the angle of the light rays is obtained, and this angle information can be used by the system 10 to modify the calculated mesh density.

[0127] In some embodiments, the data metrics 525 include metrics (e.g., values ​​used for recommendations or other notifications) that inform the patient's clinician of the possibility of performing an additional (e.g., second) therapeutic procedure on the patient, which may, for example, optimize or at least improve a therapeutic procedure where at least a first procedure (e.g., an interventional procedure) has already been performed. The additional therapeutic procedure may include an interventional procedure selected from the following group: adjustment (e.g., repositioning, expanding, contracting, and / or other adjustment of an implant) of a device (e.g., the therapeutic device 16) implanted in the patient in a previous procedure, implantation of a device (e.g., the device 16) in the patient (regardless of whether the patient had a previous device implanted), a vasodilatation procedure, an atherectomy procedure and / or other procedure in which occluding material is removed, coil embolization or other procedures to occlude undesirable spaces in the vasculature, a drug delivery procedure, and combinations thereof.

[0128] In some embodiments, the system 10 may identify whether a myocardial bridge is present in a portion of the imaged vessel. For example, the system 10 may automatically detect the presence of a myocardial bridge (e.g., via algorithms 315, 415) and / or data presented to an operator of the system 10 may indicate the presence of a myocardial bridge (e.g., allowing the operator to draw a conclusion based on the presented data). In some embodiments, image data may be collected by the system 10 during a pullback procedure. During the pullback procedure, the imaging probe 100 is pulled back at a speed such that multiple cardiac cycles are captured so that strains of the imaged vessel (e.g., strains caused by cardiac motion) may be analyzed throughout the cardiac cycle. In some embodiments, the system 10 is configured to identify the myocardial bridge by analyzing the image data to detect artifacts in the image data indicative of the presence of a myocardial bridge (e.g., a characteristic artifact similar to an echolucent "halo" seen when imaging a myocardial bridge using intravascular ultrasound).

[0129] In some embodiments, the system 10 is configured to quantify the quality of the image data (e.g., quantification determined by an automated process of the system 10 as described herein). In some embodiments, if the quality of the image data falls below a threshold, one or more analysis processes of the system 10 (e.g., image analysis as described herein) are disabled such that the process is not performed on the low quality image data. For example, if the image data is analyzed and it is determined (e.g., by the system 10 and / or an operator of the system 10) that the optical assembly 115 started and / or ended within the stent during the pullback procedure, the system 10 may be configured to disable subsequent CFD or other calculations as described herein based on the insufficient image data. In some embodiments, the system 10 may assess the quality of a purging procedure based on the quality of the image data. For example, the system 10 may assess the image quality to identify intrusion of blood into the delivery catheter 80 to indicate the need for purging. This analysis may be used to provide feedback to the user in real time during imaging, for example, by displaying a warning message (e.g., "Please Purge Catheter"). Similarly, after image acquisition is complete, the system 10 may analyze the image data to display a warning to the user if catheter purging was incomplete. In some embodiments, the system 10 may analyze the image data to identify blood residue within the lumen and display a warning to the user, as well as indicate to the user areas of incomplete blood clearance. If blood clearance is incomplete in areas critical to the CFD calculations (e.g., a dim reference frame or a stenosis), a warning may be provided that the image quality is not sufficient for the CFD calculations.

[0130] In some embodiments, the system 10 is configured to use high-resolution image data (e.g., OCT image data) to perform various computational fluid dynamics (CFD) and / or optical flow ratio (OFR) calculations to accurately simulate blood flow through a stenosed artery (e.g., a coronary artery) and estimate pressure drop through one or more lesions as described herein. These methods provide a user (e.g., an interventional clinician) with a combined, simultaneous measurement of arterial anatomy and vascular hemodynamic status (e.g., "physiologic anatomy") at high resolution, which may be used to better characterize and diagnose the significance of stenosed coronary arteries pre- and post-intervention. This information may be used to provide informed guidance and / or optimize interventional procedures as detailed herein.

[0131] Conventional clinical practice only allows for the use of either intravascular imaging (e.g., OCT or IVUS imaging) or physiological measurements (e.g., FFR, iFR, RFR, etc.) at a time because imaging and physiological measurements can only be obtained using separate instruments (e.g., dedicated catheters). In contrast, the system 10 may be configured to capture both vascular anatomy and physiology (e.g., in a single "pullback acquisition"). Such a combined solution has the important advantage of providing natively co-registered anatomical and physiological data (e.g., captured with a single device) that can be used to better plan and optimize coronary interventions than either of these tools alone.

[0132] In some embodiments, the CFD simulations performed by system 10 are designed to closely simulate hyperemic conditions, such as when pressure wires are used to obtain fractional flow reserve data. Alternatively or additionally, CFD methods may be used to simulate non-hypemic conditions, similar to how, for example, iFR or RFR catheters are used to collect vascular hemodynamic data. FFR devices typically perform a single FFR measurement from a single location distal to all lesions. Using the CFD methods of system 10 described herein, blood flow and pressure drop across an OCT-imaged coronary artery segment may be more easily assessed.

[0133] Conventional FFR techniques suffer from a significant limitation due to lesion crosstalk. For example, in the case of contiguous lesions, the FFR acquisition cannot distinguish the individual contribution of each lesion. The CFD technique of the system 10 described herein makes it possible to determine the contribution of each lesion and indicate which of the imaged lesions is more critical and should be treated.

[0134] The system 10 may be configured to perform CFD simulation and pressure drop assessment of an entire arterial segment (e.g., 100 mm or more) in a few seconds (e.g., less than 20 seconds) using simplified quasi-2D and / or 2D solvers. Compared to a "full" 3D solver (e.g., a solver configured to implement the Navier-Stokes equations), the quasi-2D and / or 2D solvers may reduce computation time by an order of magnitude or more while maintaining sufficient accuracy for coronary pressure drop assessment.

[0135] In some embodiments, the CFD simulation relies heavily on segmentation of image data (e.g., OCT image data). The segmentation can be obtained through traditional image processing algorithms and / or AI techniques (e.g., machine learning, deep learning, neural networks, and / or other artificial intelligence techniques). In some embodiments, these techniques comprise various steps of the method 1000 described with reference to FIG. 5 for analyzing an image dataset (e.g., an OCT image dataset) to quantify blood flow and / or pressure drop.

[0136] In some embodiments, the system 10 includes a graphical user interface (e.g., GUI 353), as described herein with reference to, for example, FIGS. 3A-3C. In some embodiments, the GUI 353 is configured to acquire and use OCT images and / or simulated physiologic data to provide a user with an easy and instant way to diagnose coronary stenosis and plan and optimize coronary interventions. In some embodiments, the OCT-FFR "physio-anatomical" data is registered to the coronary angiography data, which may provide a comprehensive tool for interventional clinicians to accurately plan and guide coronary procedures. Additionally or alternatively, the OCT-FFR simulation may be used to create a virtual stent tool that allows a user of the system 10 (e.g., interventional clinician) to simulate the effect of stents of various lengths and diameters at various vascular locations to optimize stent size and selection and devise an optimal intervention strategy.

[0137] In some embodiments, the physiological-anatomical data may be quantified (e.g., by the system 10) by a number of metrics. For example, these metrics may be used for quantification of the pre- and post-interventional effects of a treatment (e.g., "gain" quantification).

[0138] In some embodiments, the system 10 is configured to ensure the quality and suitability of the data for CFD calculations. For example, the system 10 may be configured to ensure the reliability of the segmentation results (e.g., side branch and / or lumen segmentation) by determining a “confidence metric” as described herein. The purpose of the confidence metric is to inform the user of potentially degraded images when the segmentation results are unclear (unstable), allowing for quick visual confirmation and (if necessary) correction. In some embodiments, the system 10 may be configured to verify that a complete pullback has been ensured from a location distal to the lesion to the tip of the guide catheter. In some embodiments, a complete pullback may be defined as a pullback that captured the entire disease, as a pullback that did not start and / or end on a diseased vessel segment, and as a pullback where the entire stent, if present, was imaged. If the pullback starts and ends on a diseased vessel segment, the system 10 may be configured to recover from this situation to provide accurate CFD measurements. For example, in this case, system 10 may be configured to identify healthy vessel segments (e.g., via one or more techniques described herein) and use branching laws to estimate vessel diameter and / or area in proximal and / or distal reference frames, e.g., as described herein with reference to Figures 4A-4D.

[0139] In some embodiments, the system 10 is configured to perform an assessment of image quality, including assessing whether there is a significant amount of blood remaining in the lumen of the vessel during pullback (e.g., whether there is blood obscuring one or more portions of the vessel). The system 10 may be configured to perform the assessment as described with reference to FIGS. 7A-7D. In some embodiments, the system 10 is configured to assess blood trapped in a portion of the catheter being imaged (e.g., a portion of the catheter configured to be purged with saline before and / or during pullback), blood that reduces image quality. An example of an incomplete catheter purge and its effects is shown in FIG. 13 and described herein. One or more algorithms of the system 10 may be configured to automatically detect the degradation of image quality and the extent of image quality loss, and alert the user to the poor image quality and the potential need to repeat the acquisition (e.g., repeat the pullback). In some embodiments, the system 10 is configured to capture one or more angiographic images. Analysis of the angiography data performed by system 10 may reveal the presence of significant collateral vessels (e.g., one or more "donor" vessels that receive blood into the imaged vessel and / or one or more "recipient" vessels that receive blood from the imaged vessel) that may affect blood flow in the imaged vessel. The presence of significant collateral vessels may result in inaccurate FFR and CFD calculations (e.g., an erroneously low FFR if the imaged vessel is a donor vessel with one or more recipient collateral vessels and / or an erroneously high FFR if the imaged vessel is a recipient vessel with one or more donor collateral vessels). In some embodiments, a warning message may be displayed to inform the user of the presence of collateral vessels before the CFD calculations are performed by system 10 and / or before the results are displayed to the user by system 10.

[0140] In some embodiments, the system 10 is configured to use various image processing techniques (e.g., as described herein) to prevent incomplete and / or poor quality image data that may reduce the accuracy of the CFD simulation of the pressure drop calculation. An automatic determination of image data quality may alert a user of the system 10 to potential problems, allow the user to correct some issues if possible (e.g., allow the user to correct an inaccurate segmentation result), or notify the user when new image data acquisition may be required. An automatic evaluation of data quality may warn the user about images of moderate quality, provide guidance, or facilitate correction. Alternatively or additionally, significant losses of image data quality that cannot be recovered may be displayed to the user, and the system 10 may provide guidance on how to improve image quality (e.g., by instructing the user to better purge the catheter or to better engage the coronary ostium with the guide catheter) and perform additional image acquisition.

[0141] In some embodiments, the system 10 may determine reference diameters (e.g., proximal and distal reference diameters) and side branch size (e.g., as described herein with reference to FIGS. 4A-4D) and may use this information to calculate an "ideal" and / or "reference" vessel profile (vessel contour, vessel contour) to better guide the intervention and / or to quantify "stent expansion." The ideal vessel profile is a metric that may inform more accurate stent sizing. Stent expansion is a metric that may inform additional procedures to optimize the stent implantation procedure.

[0142] Information collected and / or analyzed by system 10 may be used to provide various functions in a clinical environment. For example, system 10 may be used as a tool to provide training (e.g., training for clinicians or other users of system 10) and / or provide equipment diagnostic information in a clinical environment (e.g., self-diagnostic information and / or diagnostic information related to equipment in the clinical environment that is not part of system 10). When used in training, system 10 may be configured to perform initial and / or periodic evaluations of a user of system 10, for example, by comparing a judgment made by the user (e.g., based on image data collected by system 10 and input to system 10) with a judgment made by system 10 (e.g., algorithms 315, 415) based on similar data (e.g., the same data). For example, system 10 may perform automated image evaluations (e.g., determining whether blood is present during imaging, whether a guide catheter is properly positioned during imaging, and / or whether a catheter lumen has been sufficiently purged during imaging). Based on such automated evaluation, system 10 may provide feedback to the user based on the user's operation of system 10 and / or the user's interpretation of the data. For example, system 10 may suggest IQ improvements, provide considerations based on the image quality evaluation, or provide an overall pullback review.

[0143] When used for diagnostics, the system 10 may perform image quality assessments and infer from the image quality (e.g., by algorithms 315, 415) whether components of the system 10 may be responsible for image quality degradation. For example, the system 10 may detect practical issues (e.g., imaging assembly 320 failure (e.g., detected from blurry image data), loose and / or broken connectors, and / or lack of (bad) image registration (e.g., due to NURD or other physical conditions of the catheter). In some embodiments, the system 10 is configured to track usage of various components of the system (e.g., the number of pullbacks that the imaging probe 100 and / or imaging assembly 320 have been used). In some embodiments, system 10 is configured to analyze a first set of image data collected by system 10 and a second set of image data from another imaging device (e.g., a second imaging device 15), analyze (e.g., by algorithms 315, 415) the image quality of the second set of image data, and provide a diagnostic report for the second imaging device (e.g., a diagnostic report to determine whether the second imaging device is operating properly or requires service or calibration).

[0144] In some embodiments, the system 10 is configured to perform an automated review of the image data collected by the system 10 to ensure that the image quality is sufficient to perform subsequent calculations based on the image data (e.g., FFR calculations as described herein). The system 10 may be configured to identify various issues from the image data, such as those selected from the following group: blood in the image (e.g., due to insufficient blood removal), poor lumen wall integrity, image distortion (e.g., distortion due to NURD), lack of visualization of the guide catheter, insufficient pullback distance (e.g., less than 40 mm), improper start and / or end of the image data (e.g., start and / or end within the stent), and combinations thereof.

[0145] In some embodiments, the system 10 is configured to analyze the image data to determine whether the patient meets any exclusion criteria (e.g., whether the patient is excluded from further treatment and / or diagnosis by the system 10). The exclusion criteria identified by the system 10 include the presence of a chronic total occlusion (CTO) in the target vessel, severe diffuse disease in the target vessel (e.g., disease defined as the presence of diffuse, continuous, macroscopic luminal irregularities present in a large portion of the coronary artery tree), the presence of a myocardial bridge (MB), a target lesion involving the left main (e.g., greater than 50% stenosis), artifacts observed in pre-PCI OCT imaging for the target lesion or if there are multiple target lesions, artifacts observed in pre-PCI OCT imaging for all target lesions, any preparation (e.g., balloon dilation, atherectomy, etc.) prior to pre-PCI OCT imaging and physiological measurements, etc. Presence of a target lesion requiring pre-PCI OCT imaging and physiologic measurements (including, but not limited to, balloon dilation, atherectomy, etc.) or, in the case of multiple target lesions, all target lesions requiring some preparation prior to pre-PCI OCT imaging and physiologic measurements (including, but not limited to, balloon dilation, atherectomy, etc.), target lesions more than 60 mm from the coronary ostium and / or significant coronary artery disease (CAD) (e.g., inability to image the lesion by OCT with a single pullback), inaccurate or unsuccessful catheter purging and / or contrast flush, presence of plaque rupture and / or intravascular hematoma in the target vessel (stenosis of 40% or more of visible diameter), and combinations thereof.

[0146] In some embodiments, system 10 is configured to analyze the angiographic image data to identify a vessel in which imaging probe 100 is positioned (e.g., a vessel represented by vascular image data collected by system 10). In some embodiments, one or more algorithms (e.g., algorithms 315, 415) of system 10 are modified based on the vessel being imaged (e.g., automatically modified based on identification of the vessel being imaged from the angiographic image data). In some embodiments, system 10 is configured to perform motion correction of the OCT image data by analyzing velocity vectors of angiographic image data collected simultaneously with the OCT image data.

[0147] In some embodiments, the image processing method of the system 10 described herein is configured to automatically perform a process selected from the group consisting of: identifying normal and diseased portions of an imaged vessel, identifying an ideal reference frame for vessel sizing (e.g., avoiding placing reference portions in diseased regions), optimizing the scaling law by avoiding the use of diseased portions as reference diameters, optimizing vessel size estimation, and combinations thereof.

[0148] Referring now to FIG. 2, a graphical representation of a neural network in accordance with the concepts of the present invention is shown. FIG. 2 shows an algorithm (algorithm 1015) configured as a neural network. Each of the algorithms 315, 415 described herein may include an algorithm configured similarly to algorithm 1015 (e.g., where algorithm 1015 is processed by processing unit 310 of console 300 and / or processing unit 410 of server 400). In some embodiments, algorithm 315 includes algorithm 1015 and / or algorithm 415 includes algorithm 1015. In some embodiments, algorithm 1015 includes machine learning, deep learning, neural networks, and / or other artificial intelligence algorithms ("AI algorithms" herein). These machine learning and the like are trained by a first processing unit (e.g., processing unit 410) and configured to process data (e.g., image data) using a second processing unit (e.g., processing unit 310 of console 300). System 10 may be configured to allow an operator to modify one or more of the algorithms 1015. In some embodiments, algorithm 1015 includes a bias, such as a bias for a particular outcome (e.g., a bias for false positives, a bias for false negatives, or other bias). In these embodiments, system 10 may be configured to allow an operator to create and / or modify the bias of one or more of the algorithms 1015 (e.g., via user interface 350).

[0149] The algorithm 1015 may process image data in multiple domains (e.g., both polar and Cartesian image domains). In some embodiments, the algorithm 1015 processes data in two, three, or more image domains. The algorithm 1015 may be configured to process image data in multiple domains by performing image data transformations at each encoding step and / or each decoding step of the algorithm 1015. In some embodiments, the algorithm 1015 only needs to input image data in a single image domain, and the algorithm 1015 transforms the image data from the single domain to one or more additional domains. The algorithm 1015 may be configured to process image data in one or more image domains selected from the following group: polar domain, Cartesian domain, longitudinal domain, frontal image domain, domains generated by computing image features (e.g., primary and / or secondary features, image texture, image entropy, homogeneity, correlation, contrast, energy, and / or other image features), and combinations thereof.

[0150] In some embodiments, algorithm 1015 is configured to prevent degradation of training, including overfitting, and to improve generalization of the network.

[0151] In some embodiments, the algorithm 1015 is configured to learn detailed features (e.g., detailed features of image data). In these embodiments, the learning rate of the algorithm 1015 may be increased (e.g., by skipping layers). For example, the algorithm 1015 may include an algorithm that has been trained to perform at least one process, where the training is completed in less than a week (e.g., less than a day) (e.g., less than 12 hours).

[0152] In some embodiments, the algorithm 1015 includes multiple AI algorithms, each of which is configured (e.g., trained) to perform a single image processing application. For example, a first algorithm is trained to perform a lumen segmentation application, and a second algorithm is trained to perform a side branch segmentation application. Alternatively or additionally, the algorithm 1015 may include a single algorithm trained to perform two, three, or more image processing applications (e.g., a single algorithm including two or more modules, each module trained to perform an image segmentation process). For example, an algorithm 1015 including a neural network or other AI algorithm may include a module trained to perform both lumen segmentation and side branch segmentation. In some embodiments, the algorithm 1015 is configured to "skip" one or more layers of the neural network to perform one of the multiple trained image processing applications (e.g., each module of the algorithm 1015 uses only the layers of the neural network necessary to perform segmentation). In some embodiments, the algorithm 1015 comprises one or more modules configured to quantify key features of the image data (e.g., image data including high resolution three-dimensional image data). Key features quantified by the algorithm 1015 include features of the vessel anatomy and / or morphology, the vessel lumen, the ostia of one or more side branches, arteriosclerotic disease, an ideal lumen profile (e.g., as described herein), an ideal stent expansion (e.g., as described herein), and combinations thereof. The algorithm 1015 may include AI or other algorithms configured to calculate computational fluid dynamics (CFD) of the imaged vessel (e.g., to quantify blood flow and / or pressure drop along the length of the imaged vessel).

[0153] Referring now to FIG. 3, an embodiment of a graphical user interface for displaying image data and guiding a vascular intervention is shown in accordance with the concepts of the present invention. The system 10 may include a toolkit to assist a clinician and / or other operator in planning, performing, and / or evaluating a vascular intervention (e.g., a cardiac intervention or a neurovascular intervention). The GUI 353 may include various display regions (e.g., portions of a display) that present information to an operator in a layout configured to assist in various aspects of an interventional procedure as described herein. The display regions may be rendered in various layouts (also referred to as "workspaces") on the GUI 353. FIG. 3 illustrates a workspace (workspace 3531) configured to display pre-intervention image data side-by-side with post-intervention image data. The workspace 3531 has a pre-intervention display region (workspace region 3501) and a post-intervention display region (workspace region 3502). The GUI 353 may render one or more images (e.g., still and / or video images) of angiography data and / or other two-dimensional projection data (e.g., OCT image data) in one or more display areas (e.g., display areas 3511a, 3511b as shown). The display areas 3511a, 3511b may render in workspace areas 3501, 3502, respectively, to display pre-intervention and post-intervention data (e.g., angiography data or other two-dimensional data), respectively. Additionally or alternatively, the GUI 353 may render one or more images of lumen cross-section data (e.g., OCT data) in one or more display areas (e.g., display areas 3512a, 3512b). The display areas 3512a, 3512b may render in workspace areas 3501, 3502, respectively, to display pre-intervention and post-intervention data (e.g., OCT data), respectively.

[0154] In some embodiments, GUI 353 may render one or more images representing a lumen profile of a blood vessel in one or more display areas (e.g., display area 3513 as shown). Display area 3513 may be rendered on workspace 3531, for example between workspace areas 3501, 3502 as shown. The lumen profile data displayed in display area 3513 may represent pre-intervention and / or post-intervention data. In some embodiments, the lumen profile data includes data calculated by one or more algorithms of system 10 as described herein (e.g., calculations performed on image data (e.g., OCT image data and / or non-OCT image data) collected by probe 100 and / or another component of system 10 as described herein).

[0155] In some embodiments, the GUI 353 may render one or more images including data graphs in one or more display areas (e.g., the illustrated display area 3514). The display area 3514 may be rendered on the workspace 3531, for example, in a location below the workspace areas 3501, 3502 as shown. The graphs displayed in the display area 3514 may represent pre-intervention and / or post-intervention data. For example, a graph comparing pre-intervention fractional flow reserve (FFR) data and post-intervention FFR data along the length of a vessel may be displayed as shown in FIG.

[0156] In some embodiments, the GUI 353 may display numerical data in one or more display areas (e.g., display area 3515 as shown). The display area 3515 may be rendered on the workspace 3531, for example along one side of the workspace as shown. The data displayed in the display area 3515 may include data calculated from image data collected by the system 10 and may be selected from the following group: dimensions of the imaged vessel (e.g., vessel length and / or mean diameter), vessel taper, pre-intervention FFR, intra-intervention FFR, post-intervention FFR, delta FFR, FFR gain per unit length (e.g., mm) of the implanted stent, FFR gain of the imaged vessel, lumen area obtained after the intervention, minimum expansion index (MEI) of the implanted stent, values ​​corresponding to residual malposition of the stent, and combinations thereof.

[0157] The GUI 353 may display one or more overlays related to the data displayed within the display areas described herein. For example, the GUI 353 may display an overlay 3521 that visually represents a location along the length of the imaged lumen with respect to where FFR degradation was determined. The overlay 3521 may include a number of unit indicators (e.g., dots as shown), each indicator representing a delta of the calculated FFR, as shown in the display areas 3511a, 3511b, 3513. For example, each dot represented in the overlay 3521 may represent a delta of the FFR (e.g., the calculated FFR). In some embodiments, the GUI 353 displays an overlay 3522 that visually indicates a location through the vessel where the OCT image data was collected (e.g., in relation to the displayed angiography image data). The overlay 3522 may include a line (as shown) that is rendered relative to the two-dimensional image data, as shown in the display areas 3511a, 3511b. In some embodiments, characteristics (e.g., graphic characteristics) of the lines of the overlay 3522 may vary along the length of the line to represent additional data (e.g., to represent FFR data along the length of the line), such as changing color of the line (e.g., red to indicate unhealthy portions of the imaged vessel and green to indicate healthy portions), changing line thickness, or other line characteristics to represent data calculated by the system 10 based on the recorded OCT image data or other data.

[0158] In some embodiments, one or more overlays are displayed over the OCT image data displayed via GUI 353 (e.g., data displayed in display areas 3512a, 3512b). In some embodiments, these one or more overlays represent segmentation data determined by an algorithm of system 10 (e.g., algorithm 1015). For example, the one or more overlays represent lumen segmentation, side branch segmentation, and / or device segmentation.

[0159] Images and / or other data displayed to the operator via GUI 353 may be used to assist and / or guide the operator (e.g., clinician) in performing cardiac, neurological, and other interventional procedures, as described herein. System 10 may be configured to calculate (e.g., by algorithms 315, 415, 1015 described herein) various data metrics that may be displayed to assist the operator. For example, prior to an interventional procedure (e.g., a stent procedure), system 10 may calculate an “ideal lumen profile” (e.g., an approximation by system 10 of the lumen of a vessel segment in the absence of disease). Such calculations may be performed, for example, by identifying diseased portions of a vessel and extrapolating a healthy lumen profile from data collected proximal and / or distal to the diseased portions, and / or by measuring the degree of tapering of identified side branches (e.g., using a “bifurcation rule” such as Murray’s Law). In some embodiments, after stenting, the system 10 may calculate an “ideal stent expansion” (e.g., an optimized stent expansion relative to an ideal lumen profile calculated before stenting, or a desired stent expansion). In some embodiments, the ideal stent expansion may be determined using a process similar to that used in determining the ideal lumen profile. In some embodiments, the system 10 compares pre-intervention image data with post-intervention image data to identify and adjust for changes in the appearance, diameter, and / or other characteristics of the vessel (e.g., the ostium of a side branch of a vessel) that may have been caused by the intervention (e.g., angioplasty and / or stent implantation). Such changes in vessel characteristics may alter the pre- and / or post-intervention calculated ideal lumen profile. The system 10 may be configured to adjust for discrepancies between the pre-intervention ideal lumen profile and the post-intervention ideal lumen profile by adjusting the post-intervention data based on the side branch diameter calculated using the pre-intervention data.By adjusting the side branch diameters calculated from the post-intervention data to match the corresponding diameters calculated from the pre-intervention data, more accurate calculations and / or comparisons between the pre-intervention data and the post-intervention data may be performed by the system 10.

[0160] 3A-3C, another embodiment of a graphical user interface for displaying image data and guiding a vascular intervention is shown in accordance with the concepts of the present invention. The GUI 353 of the embodiment shown in FIG. 3A-3C may have similar workspaces and / or display areas as those described with reference to FIG. 3, with similar and / or different layouts on the GUI 353. The layouts of the various workspaces and / or display areas shown may be arranged to optimize workflow for a user when the GUI 353 of the various embodiments is displayed.

[0161] FIG. 3A illustrates an example of a GUI 353 that is displayed to a user to allow the user to review the original state (e.g., pre-intervention state) of the imaged vessel. In some embodiments, OCT image data of the vessel (e.g., coronary arteries and / or neurovascular vessels) is collected and analyzed by the system 10 to simulate blood flow and estimate pressure drop (e.g., FFR value) through one or more lesions present in the vessel. In some embodiments, simulations are performed using quasi-two-dimensional, two-dimensional, and / or three-dimensional models of the imaged vessel generated by the system 10. These models may be based on OCT image data alone or in combination with other image data (e.g., angiography image data). These models may include data related to the vessel lumen, side branches, vessel wall characteristics (e.g., presence of plaque along the vessel wall), and / or other characteristics of the imaged vessel.

[0162] As shown in FIG. 3A, FFR values ​​may be displayed on two-dimensional and / or three-dimensional representations of OCT image data and / or angiography image data. In some embodiments, pressure drop and / or blood flow values ​​are displayed. In some embodiments, FFR values ​​are displayed as a graph (e.g., a graph in which a graphical characteristic (e.g., color) of the graph is changed to highlight data of particular interest (e.g., areas of high pressure drop). FFR data may be displayed as a measurement and / or a slope. In some embodiments, data is displayed in a graph (e.g., a visual representation of numerical data). For example, as shown on the right side of FIG. 3A, FFR data may be displayed as dots against a two-dimensional representation of the lumen profile. In some embodiments, differential FFR values ​​(e.g., comparing pre-intervention FFR values ​​with post-intervention FFR values) may be calculated and displayed, for example, as a measurement and / or a slope.

[0163] FIG. 3B illustrates an example of a GUI 353 that may be displayed to a user to allow the user to simulate stenting (or other treatment) of the imaged vessel. The system 10 may be configured to simulate and predict the outcome of performing a treatment procedure on the imaged vessel. For example, a user may input desired parameters for an intervention (e.g., stenting the imaged vessel), and the system 10 may predict the outcome of the treatment based on the input parameters and display the predicted outcome to the user. In some embodiments, the system 10 is configured to analyze the image data (e.g., via algorithms 315, 415, 1015 described herein) and suggest parameters for the intervention (e.g., suggest a location to place a stent in the imaged vessel). The treatment parameters may be selected from the group consisting of treatment location (e.g., stent placement), device length, device diameter, number of devices implanted, other device characteristics, and combinations thereof. In some embodiments, the system 10 is configured to run multiple simulations to determine the “best” treatment strategy. For example, system 10 may automatically iterate through various treatment options (e.g., using AI algorithms) to identify an optimal option or perform a user-initiated simulation based on parameters modified by the user via GUI 353. GUI 353 may provide tools for the user to manipulate the placement and / or other parameters of a virtual stent, such that system 10 may predict the outcome of a treatment based on the user's placement of a virtual stent.

[0164] In some embodiments, the GUI 353 may display "virtual" measurements (e.g., CFD values, FFR values, or other flow measurements) based on the predicted outcome of the planned treatment. For example, the GUI 353 may display "pre-treatment" values ​​and predicted "post-treatment" values ​​and / or delta values ​​(e.g., differential FFR values ​​(ΔFFR values)). The system 10 may be configured to predict lumen gain and / or stent expansion based on a virtual stent. In some embodiments, the system 10 may provide suggestions for preparing the imaged vessel for a treatment procedure (e.g., in the case of calcified plaque).

[0165] FIG. 3C shows an example of a GUI 353 displayed to a user to allow the user to review post-intervention image data (e.g., image data collected by the system 10 after a stent has been implanted in a previously imaged vessel). The post-intervention image data may be collected during the intervention procedure (e.g., after one or more stents have been implanted, if more stents are planned to be implanted) and / or at the end of the intervention procedure (e.g., after all planned stents have been implanted). The system 10 may be configured to analyze the post-intervention image data to determine the effectiveness of the performed treatment. In some embodiments, the information displayed after the intervention may inform the user if additional treatment needs to be performed. The system 10 may be configured to calculate CFD and / or FFR values ​​based on the post-intervention image data. This information may help the user determine whether a pressure drop is occurring within the implanted segment of the vessel and / or due to a stenosis outside the implanted segment. In some embodiments, the system 10 may analyze and / or display information regarding the implantation of one or more stents (e.g., whether the stent was properly expanded). In some embodiments, the GUI 353 may display a virtual representation of the imaged stent relative to image data (e.g., angiography data, cross-sectional OCT data, and / or a representation of the lumen profile).

[0166] In some embodiments, pre-intervention image data and post-intervention image data may be displayed side-by-side to the user, as shown in FIG. 3. The GUI 353 may display various metrics calculated by the system 10 that quantify changes in the imaged vessel after treatment. For example, FFR gain may be quantified by comparing pre-intervention FRP values ​​to post-intervention FFR values ​​(e.g., ΔFFR values). Additionally or alternatively, FFR gain per unit length (mm) of the stent may be quantified by the system 10. In some embodiments, the amount of stent expansion and / or malposition may be quantified and displayed to the user.

[0167] 4A-4D, anatomical diagrams of blood vessels showing various levels of atherosclerosis are shown, in accordance with the concepts of the present invention. In some embodiments, system 10 is configured to analyze image data (e.g., intravascular (IV) image data collected by system 10 as described herein) to estimate pressure drop within the vessel using computational fluid dynamics (CFD) techniques. The boundary equation of the CFD model includes the diameter of the proximal vessel (diameter D shown), P ) may require identification of a proximal reference frame used by system 10 to quantify the diameter of the vessel. The proximal reference frame may typically be identified within the image dataset as a proximal frame without disease (e.g., a frame of image data in which the imaged vessel is free of disease). FIG. 4A shows an imaged vessel without disease. However, in many clinical cases, the main vessel exhibits disease (e.g., arteriosclerosis) and the identified diameter does not represent the "real vessel size" (e.g., diameter D of a healthy vessel shown in FIG. 4B). P is underestimated due to negative vascular remodeling).

[0168] In some embodiments, the system 10 analyzes the intravascular image data to determine the diameter D of the external elastic lamina (EEL). P-ELLHowever, due to the uncertainty of positive and / or negative remodeling in the presence of arteriosclerotic disease, the diameter D P-ELL can often overestimate the actual vessel size.

[0169] In some embodiments, the system 10 uses one or more vascular scaling laws to estimate the actual vessel size (e.g., diameter D P-ELL (e.g., determining a more accurate estimate than that provided by Hseg Image data (e.g., OCT images captured by system 10) may be used to find diseased vessel segments (segments marked with a marker) and use information associated with the segments to then estimate the actual vessel lumen of adjacent segments. An algorithm 1015 (e.g., a machine learning or other AI algorithm) of system 10 may be configured to automatically identify disease-free vessel segments. In some embodiments, algorithm 1015 may be biased to preferentially identify the presence of disease. Alternatively, algorithm 1015 may be biased to preferentially identify the absence of disease.

[0170] In some embodiments, the system 10 (e.g., via the algorithm 1015) determines the diameter D of a healthy segment of a blood vessel. Hseg and other data available to system 10 (e.g., data calculated by system 10 and / or data imported into system 10), the diameter D P For example, the system 10 may be configured to estimate the diameter D of the side branch #1 shown in the figure. SB1 and diameter D Hseg Using the diameter D P The system 10 may implement various scaling laws, such as a scaling law based on the use of the 7 / 3 power law.

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[0171] However, in certain scenarios, diffuse arteriosclerosis may affect not only the long portion of the imaged vessel, but also the side branches of the imaged vessel. For example, as shown in Figure 4C, the presence of plaque in these parts of the vessel may result in a large increase in diameter D seg1 and diameter D SB3 Both and do not represent their actual diameters. In vessels showing diffuse disease, the ostia of side branches are often also affected.

[0172] In some embodiments, the system 10 analyzes non-invasively recorded image data (e.g., x-ray and / or fluoroscopic image data) to determine one or more illustrated diameters (e.g., diameter D SB3 ) In some embodiments, image data collected from outside the main vessel (e.g., fluoroscopic image data) may allow better visualization of the side branches along their entire length (e.g., superior to the intravascular imaging techniques described herein). In some embodiments, system 10 is configured to combine and register the noninvasive image data with the intravascular image data and use the combined data to calculate one or more vessel diameters.

[0173] Alternatively, system 10 may be configured to calculate one or more vascular diameters using only intravascular image data (e.g., OCT image data collected by system 10). In some embodiments, one or more scaling laws may be applied by system 10 to the multiple diameters, thereby optimizing the final estimate of all diameters and reducing error.

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[0174] The intravascular image data may provide information about the plaque distribution that may be used by the system 10 to assign weights (e.g., confidence labels) for the optimization process. For example, D (the diameter of the distal reference frame shown in FIG. 4C) represents a vessel segment that does not exhibit arteriosclerotic disease. This allows (e.g., by an algorithm of system 10) D , D SB1 , and D SB2 can be labeled as “high confidence” in the optimization process, but D seg1 , D seg2 , and D SB3 may be labeled as "low confidence."

[0175] FIG. 4D illustrates diffuse disease in a vessel and its side branches. When diffuse disease affects nearly the entire intravascular image data, IV images alone may not provide a reliable estimate of the "real vessel size." In some embodiments, the system 10 may identify this situation based on automatic identification of plaque and alert the user that a particular image data set may not be usable for reliable CFD pressure drop calculations. Additionally or alternatively, one or more machine learning and / or other image processing methods may be used to automatically assess image quality, such that a similar warning may be displayed to the user in the event of acquisition of low-quality image data (e.g., OCT image data).

[0176] 5, a method of treatment planning based on data collected and / or analyzed by a system in accordance with the concepts of the present invention is shown. Method 1000 may be performed using various devices of system 10 described herein. In step 1010, image data (e.g., image data representative of a patient's blood vessels) is acquired. For example, OCT image data may be recorded by a pullback procedure described herein. Alternatively or additionally, raw and / or pre-processed data may be imported into system 10 for analysis by system 10, and the analyzed image data may be displayed to a user to assist in treatment planning.

[0177] In steps 1020-1040, image processing may be performed by system 10. System 10 may include one or more algorithms (e.g., algorithms 315, 415, 1015 described herein) for processing image data. In some embodiments, one or more algorithms may include a bias (e.g., a bias described herein).

[0178] In step 1020, the system 10 may evaluate the quality of the acquired image data. For example, the system 10 may include one or more algorithms configured to evaluate the presence of blood in the lumen and / or to perform segmentation of the catheter.

[0179] In step 1030, the system 10 may perform one or more image analyses. For example, the system 10 may include one or more algorithms configured to perform an analysis selected from the group consisting of lumen segmentation, side branch segmentation, vessel health analysis, and combinations thereof.

[0180] In step 1040, the system 10 may calculate one or more boundary conditions based on the image data. For example, the system 10 may include one or more algorithms configured to identify a frame of reference for the image data and / or to determine a diameter of one or more imaged side branches.

[0181] Following the image processing performed in steps 1020-1040, system 10 may generate one or more digital models of the imaged blood vessel in step 1050. For example, system 10 may generate a high resolution three-dimensional model of the imaged blood vessel (e.g., a model that includes at least a portion of one or more side branches of the imaged blood vessel).

[0182] In step 1060, system 10 may perform one or more CFD simulations to estimate various properties of the imaged blood vessels. For example, system 10 may perform CFD calculations as described herein.

[0183] In step 1070, various information collected and / or calculated by the system 10 may be displayed to the user (e.g., via the GUI 353 described herein). For example, a three-dimensional model of the imaged blood vessel may be displayed to the user along with CFD values ​​(e.g., blood flow and / or pressure drop values) calculated along the length of the vessel. The system 10 may be configured to display various image data and / or system-generated models simultaneously (e.g., side-by-side) and / or as a merged display, such as when one data type is overlaid on another. In some embodiments, the system 10 may display both angiography image data and OCT image data.

[0184] In step 1080, intervention planning may be performed. For example, system 10 may automatically and / or semi-automatically determine (e.g., by algorithms of system 10) one or more interventional actions that may be performed on the imaged vessel (e.g., if disease is detected in the previous steps of method 1000). Additionally or alternatively, system 10 may be configured to provide one or more tools (e.g., by GUI 353 described herein) for the user to virtually treat (e.g., virtually insert a stent) or plan interventional actions on the imaged vessel. Method 1000 may return to step 1060 to, for example, recalculate the characteristics of the imaged vessel while incorporating predicted results of the planned interventional actions. Method 1000 may repeat the sequence of steps 1060, 1070, and 1080 (e.g., at the discretion of the user) to, for example, simulate and evaluate various intervention options. After the intervention plan is determined, the user may perform the determined intervention. In some embodiments, after performing the intervention, the method 1000 may be repeated, for example, to evaluate the results of the intervention.

[0185] 6A-6C, various OCT images of a blood vessel and a guide catheter are shown in accordance with the concepts of the present invention. In some embodiments, the pullback imaging procedure ends with the optical assembly (e.g., optical assembly 115 described herein) within a proximal guide catheter (e.g., a neurovascular microcatheter, a distal access catheter, a neural sheath, a balloon catheter) (herein a "guide catheter"). In these embodiments, a portion of the OCT image data represents a portion of the guide catheter (e.g., a portion of the guide catheter where the optical assembly was pulled back during imaging). For example, in some embodiments, as shown in FIGS. 6A and 6B, a large portion (up to 30-40% or more) of the entire image data set may be recorded within the guide catheter. The guide catheter may partially obscure the vessel wall and / or device (e.g., a stent) from the image or may obscure the entire vessel from the image. For example, depending on the configuration of the guide catheter, an imaging catheter may or may not be able to image through the guide catheter. For example, various guide catheters may be constructed of opaque plastic, clear plastic, one or more metal braids, and / or other structures that may affect the ability of system 10 to image the blood vessels through the guide catheter. Guide catheters are used in various vascular interventions (e.g., coronary, neurovascular, and peripheral arterial interventions).

[0186] When processing an image dataset (e.g., an image dataset acquired by system 10), it is advantageous to distinguish between portions of image data representing vascular and / or implantable devices and portions of image data collected from inside a guide catheter. In some embodiments, the guide catheter is identified and manually selected by a user. Alternatively, automatic detection (e.g., via an algorithm of system 10) may be implemented. Automatic detection of the guide catheter may reduce the number of user actions required to analyze the image dataset.

[0187] In some embodiments, after a portion of the image dataset collected from inside the guide catheter has been identified (e.g., automatically identified), that portion is removed (i.e., excluded) from further analysis. In some embodiments, the identification of the guide catheter is used to provide additional information to the user. For example, if the guide catheter is not detected, the user is alerted that the image acquisition is incomplete. Also, if an implanted device (e.g., a stent) ends up within the guide catheter, the user is alerted that the guide catheter is incorrectly placed.

[0188] In some embodiments, identifying the portion of the image data that includes the guide catheter can help identify and further process regions of interest in the image dataset, and can improve the accuracy of image processing of the regions of interest, such as segmentation of intravascular devices (e.g., stents, flow diverters, coils, and / or other intravascular devices), segmentation of lumens, side branches, plaque, wall dissections, thrombi, and / or other lumen characteristics, computational fluid dynamics (CFD) calculations (e.g., calculations to identify pressure drop, flow characteristics, etc.), and combinations thereof. Removing the image data that includes the guide catheter can provide several advantages. For example, removing such data can optimize and reduce processing time of the entire image dataset (e.g., subsequent image processing algorithms are more efficient because there is less data to process). As another example, removing such data can reduce the number of false positives (or negatives) in automatically identifying vascular and / or device features. The vessel and / or device features may be selected from the group consisting of, for example, lumen area, plaque, stenosis, device features (e.g., stent struts, flow diverters), side branches, intraluminal thrombus, and combinations thereof. In some embodiments, if the guide catheter is excluded from the image data, the system 10 may more accurately reconstruct the lumen morphology in three dimensions (e.g., for fluid dynamics calculations). In some embodiments, if a large side branch is detected proximate to the distal end of the guide catheter and / or if blood clearance is determined to be suboptimal (e.g., by an algorithm of the system 10), a warning may be provided to the user that the guide catheter is misplaced and / or that blood clearance is suboptimal. In some embodiments, the algorithm includes a bias in favor of the presence of an incorrect placement (e.g., to allow exchange of the guide when not necessary or to avoid a situation in which a guide is improperly placed but left undetected in an undesirable location).

[0189] In some embodiments, the guide catheter is automatically identified using conventional signal and image processing algorithms. For example, the guide catheter may be identified by analyzing the intensity profile and / or pixel intensity of one-dimensional, two-dimensional, and three-dimensional images and A-scan lines within the vessel. In some embodiments, the image data may be analyzed using one or more pattern recognition algorithms and / or geometric transformations (e.g., Hough transform and / or image cross-correlation). Alternatively or additionally, the guide catheter may be identified using artificial intelligence (AI) techniques described herein (e.g., techniques selected from the group consisting of two-dimensional convolutional encoder networks, dual domain encoder networks, other types of neural networks (including various different types of convolutional networks), combinations of conventional signals and image processing algorithms with AI algorithms, and combinations thereof). In some embodiments, the image processing algorithms of system 10 (e.g., algorithms 315, 415, 1015 described herein) are used to pre-process and / or post-process the image data and / or results determined using various artificial intelligence algorithms.

[0190] In some embodiments, the algorithms of system 10 are configured to identify a guide catheter in one or more two-dimensional cross-sectional OCT images (e.g., B-mode images) in polar and / or Cartesian coordinate formats, and / or in one or more longitudinal images (e.g., I-mode images). The two-dimensional method returns a probability measure of whether a given slice (e.g., a two-dimensional cross-sectional OCT image) contains a guide catheter. The method may be applied iteratively and / or using a binary search pattern to find the start (e.g., first occurrence) and / or end (e.g., last occurrence) of the guide catheter in the image dataset. For example, the algorithms of system 10 may include a "DD2Net Full Fusion Classifier architecture" that utilizes both polar and Cartesian coordinate information to determine the probability of a guide catheter being present in a frame of the image dataset. The identified guide catheter may be displayed to the user using various techniques, such as a two-dimensional cross-sectional OCT image or a two-dimensional longitudinal view (I-view) as shown in FIG. 6C, and / or three-dimensional visualization techniques, such as those described herein.

[0191] 7A-7D, there are shown images displayed to a user to represent OCT image data and image quality in accordance with the concepts of the present invention. The images shown in FIGS. 7A-7D may be displayed to a user via a graphical user interface (e.g., GUI 353 as described herein). FIG. 7A shows OCT image data displayed as a longitudinal view and a representation of an imaged lumen profile. In some embodiments, an image data quality indicator (as shown, indicator 3523) may be displayed for the displayed OCT image data. FIGS. 7B-7D show OCT image data displayed as a cross-sectional view. The arrows shown indicate the relationship between FIGS. 7B-7D and the longitudinal data displayed in FIG. 7A. In some embodiments, an image data quality indicator (as shown, indicator 3524) may be displayed for the cross-sectional OCT image data. Indicators 3523, 3524 may indicate image data quality (e.g., image data quality as assessed by system 10) as described herein. In some embodiments, portions of the image determined to have poor image data quality are highlighted (e.g., highlighted in a color such as red) as shown to alert the user. Alternatively or additionally, portions of the image with good image data quality may be highlighted. In some embodiments, a scale (e.g., a scale configured to indicate the value of the information displayed by indicators 3523, 3524) may be displayed.

[0192] In some embodiments, the system 10 is configured to automatically assess the quality of image data collected by the system. The quality of the acquired data (e.g., OCT image data collected by the system 10) depends on many factors related to the image acquisition. For example, low quality image data may be observed when OCT data acquisition is performed with incomplete removal of blood. In some embodiments, the system 10 comprises an automatic machine learning based algorithm configured to automatically classify images based on the quality of the image and the likelihood of including blood. Based on this classification, the system 10 may be configured to adjust one or more image processing procedures described herein. This may, for example, prevent or at least limit inaccurate segmentation and / or automatic analysis of low quality frames or image data. Additionally or alternatively, the system 10 may provide instructions to the user regarding potential low quality acquisitions to improve the clinical workflow of OCT image data analysis.

[0193] Referring now to FIG. 8, a graphical user interface of one embodiment is shown that displays image data and allows a user to review information determined by the system based on the image data, in accordance with the concepts of the present invention. The GUI 353 of FIG. 8 may be similar to the GUI 353 described herein. The GUI 353 may be configured to allow a user to review, approve, and / or edit the results of one or more image processing steps performed by the system 10 (e.g., performed by the algorithms of the system 10 described herein). For example, the system 10 may be configured to identify side branches of an imaged vessel and / or determine one or more characteristics of the identified side branches (e.g., the diameter of the side branches). The GUI 353 may display calculated information for a user to review. For example, workspace B displays a line indicating the calculated side branch angle and cut plane relative to a longitudinal view of the OCT image data. Workspace A includes an indicator showing the projection of the ostium of the surrounding side branches relative to a cross-sectional view of the OCT image data. Workspace C displays the various detected side branches relative to another longitudinal view of the OCT image data.

[0194] In some embodiments, the GUI 353 allows the user to select each side branch from workspace C to review the various automatically identified side branches and review the data displayed in workspace A and / or workspace B. In some embodiments, if the user agrees with the displayed information associated with the automatically identified side branch, the user may approve (or confirm) the information. In some embodiments, the user confirms the displayed information associated with the selected side branch with a single input (e.g., a single "click"). The GUI 353 also allows the user to edit the displayed information (e.g., overwrite the automatically generated information). For example, in workspace A, the user may edit the presented image of the side branch ostium circumference previously estimated by the system 10. Additionally or alternatively, in workspace B, the user may change the angle and / or cut plane. In some embodiments, after one or more user changes to the data, the system 10 calculates and / or recalculates values ​​based on the edited data. For example, if the user adjusts the side branch ostium circumference, the system 10 may calculate the area based on the user-modified circumference. Additionally, if the user adjusts the angle and / or cut plane, the system 10 may recalculate the ostium circumference based on the user-modified angle. Using workspace C, the user can review all identified side branches and correct false positives and / or false negatives by removing and / or adding side branch identifications, respectively. In some embodiments, the system 10 is configured to display only side branches identified as having a diameter above a threshold (e.g., a diameter above 1 mm).

[0195] 8A, another embodiment of a graphical user interface for displaying image data and allowing a user to review information determined by the system based on the image data in accordance with the concepts of the present invention is shown. In FIG. 8A, angiographic image data is displayed side-by-side with workspaces A, B, and C shown in FIG. 8. The system 10 may be configured to register OCT image data to the angiographic image data, such that features identified by analysis of the OCT image data (e.g., side branches) may be identified in the angiographic image. In some embodiments, the system 10 may be configured to analyze both the OCT image data and the angiographic image data (e.g., via algorithms 315, 415, 1015 described herein) to identify features of the vessels imaged with the OCT. Analysis of different types of image data (e.g., manual analysis by a user and / or automated analysis performed by the system 10) may produce more accurate results compared to analysis of a single type of image data. For example, if the ostium of a side branch is severely diseased and it is difficult to determine the diameter via the OCT image data, the angiography image data may allow a more accurate estimation of the diameter of the side branch (e.g., by analyzing a portion of the side branch that is not visible in the OCT image data). In some embodiments, the information calculated, displayed, and / or reviewed by the user as described herein may be utilized by the system 10 to perform subsequent analyses (e.g., to perform CFD calculations as described herein (e.g., calculations based on the diameters of the identified vessels and / or side branches)).

[0196] 9-12B, various representations of data collected by the applicant according to the inventive concept are shown. In some embodiments, the system 10 comprises an AI algorithm (e.g., algorithm 1015 described herein). The algorithm 1015 can be trained to perform side branch segmentation (e.g., to identify one or more side branches of an imaged vessel by analyzing image data). In some embodiments, the algorithm 1015 comprises a "DD2Net Full Fusion Architecture." The applicant trained and tested such an algorithm using training data having image data collected from about 70 pullbacks containing about 24,000 images. The applicant evaluated the algorithm using weighted Dice scores across more than 1500 images containing side branches. A sample of the results is shown in FIG. 9. In the applicant's tests, the average Dice score was 0.81. FIG. 10 shows the correlation between Dice score and the average area of ​​detected side branches. As shown in FIG. 10, the larger the area, the higher the Dice score (e.g., the better the segmentation performed by the algorithm). FIG. 11A shows a segmentation of a relatively small side branch, and FIG. 11B shows a segmentation of a relatively large side branch. In some embodiments, the algorithm 1015 is biased to more accurately identify large side branches at the risk of falsely identifying small side branches, since large side branches have a large impact on the CFD or flow calculations based on the segmented data. In some embodiments, the algorithm 1015 comprises a threshold for identifying side branches. In this case, for example, side branches smaller than the threshold are ignored by the algorithm 1015. For example, the algorithm 1015 may be configured to ignore side branches with a diameter less than 2 mm (e.g., less than 1 mm) (e.g., less than 0.5 mm). The outliers in the data shown in FIG. 10 are generally caused by poor image quality.For example, the image shown in Figure 12A shows that side branches are not properly identified in a very poor quality image. Figure 12B shows a high quality image for reference. In some embodiments, an algorithm of system 10 (e.g., algorithm 1015), as described herein, is configured to identify poor image quality and warn a user that the results of further processing of the image (e.g., segmentation results) may be unreliable.

[0197] System 10 may be configured to generate a three-dimensional model of one or more imaged blood vessels, including one or more side branches of the blood vessels. In some embodiments, the model is generated based at least in part on a segmentation (e.g., side branch segmentation) performed by algorithm 1015 as described herein. System 10 may be configured to generate the model using various surface generation algorithms (e.g., a "marching cubes" algorithm). In some embodiments, system 10 may include one or more software toolkits for modeling tissue (e.g., the Vascular Modeling Toolkit (VMTK)).

[0198] Referring now to Figure 13, there is shown OCT image data illustrating the results of incomplete and good catheter purging in accordance with the concepts of the present invention. Figure 13 shows a side-by-side comparison of an image of poor quality due to incomplete catheter purging (left image in Figure 13) with an image of good quality due to a complete purge (right image in Figure 13). In the close-up of the catheter image on the left, speckles can be seen indicating the presence of blood between the two catheter sheaths, while the black space between the two sheaths in the image on the right indicates that the blood has been completely purged (i.e., flushing media is present between the sheaths).

[0199] Referring now to FIG. 14, another embodiment of a graphical user interface for displaying image data and guiding vascular interventions according to the concepts of the present invention is shown. The GUI 353 of FIG. 14 may be similar to the GUI 353 described herein. In clinical settings, assessment of a patient's anatomical and / or physiological parameters has been demonstrated to aid in improved physician decision making, often leading to better outcomes and reduced costs of care. Due to the cost and complexity of the tools currently available to provide these assessments (e.g., separate tools each configured to provide a unique portion of anatomical and / or physiological information), these tools are used inconsistently. Furthermore, when these tools are used together, they may increase the time, cost, complexity, and / or risk of a procedure and do not provide information in an integrated and easily understood manner. The system 10 of the concepts of the present invention is configured to provide a better understanding of both the patient's anatomy and physiology, resulting in better treatment planning, improved safety, and improved efficacy.

[0200] The GUI 353 may provide a single interface with multiple workspaces (e.g., workspace areas 3501, 3502 as described herein). In this case, a user may select a workspace of interest, and data displayed elsewhere (e.g., other workspaces) is automatically synchronized to the workspace of interest (e.g., a time index and / or a location index may be adjusted in the workspace of interest and updated to display the relevant data in the other workspace). In some embodiments, a pre-intervention lumen profile is displayed in an overlaid format against a post-intervention lumen profile. The GUI 353 may be configured to allow a user to switch workspaces between different types of image data (e.g., between OCT image data and angiography image data). In some embodiments, when data is switched between different types of image data, the two data sets are synchronized. The GUI 353 may be configured to allow a user to switch workspaces between similar types of image data collected at different times and / or different locations (e.g., pre-intervention and post-intervention). In some embodiments, a first set of information (e.g., pre-intervention side branch information) may be displayed against a second set of information (e.g., post-intervention lumen profile information). The GUI 353 may provide a treatment planning interface, in which case the clinician may perform virtual stent placement, as described herein. The GUI 353 may display a lumen profile determined by analyzing image data collected by the system 10, for example, as described herein, and an “ideal” lumen profile calculated by the system 10. The GUI 353 may display one or more pressure curves (e.g., a pre-intervention pressure curve calculated by the system 10 and / or a post-intervention predicted pressure curve calculated based on the virtual stent (e.g., based on the length and placement of the virtual stent).

[0201] Referring now to FIG. 15, a method of treating a patient including treatment planning and evaluation according to the concepts of the present invention is shown. Method 2000 may be performed using various devices of system 10 described herein. In step 1, image data of a patient site is collected by system 10 in an initial (e.g., pre-intervention) pullback procedure. In step 2, system 10 performs (e.g., automatically) an anatomical and / or physiological evaluation of the image data as described herein. In step 3, a clinician evaluates the evaluation data provided by system 10 via GUI 353 of system 10, plans an interventional treatment based on the provided data, and then performs the interventional treatment according to the plan. In step 4, additional image data is collected by system 10 in a second pullback procedure. The image data collected in the second pullback is analyzed by system 10 and / or the clinician. In some embodiments, steps 3 and 4 are repeated, for example, until a desired treatment result is achieved. In step 5, post-treatment results may be compared to pre-intervention data.

[0202] In step 1, the initial pullback may include an approximately 100 mm pullback acquired over approximately 2 seconds. The system 10 may be configured to perform an initial image quality assessment (e.g., assessment of lumen clearance, location of the guide catheter, and / or identification of healthy segments of the imaged vessel, each as described herein). In some embodiments, the system 10 is configured to identify side branches of the imaged vessel. The system 10 may be configured to allow a user to review and / or edit the identified side branches, as described herein.

[0203] In step 3, the system 10 may be configured to model the outcome of a virtual treatment (e.g., virtual stent insertion) to predict the outcome of the treatment. For example, the system 10 may model the FFR gain that would be obtained by optimally expanding and implanting a stent. The system 10 may provide a model based on the length and implantation location of the stent (e.g., as input into the system 10 by a clinician).

[0204] In step 4, the second pullback may include about a 100 mm pullback collected over about 2 seconds. The system 10 may be configured to perform an initial image quality assessment (e.g., imaging the stent, positioning the guide catheter, and / or identifying healthy segments of the imaged vessel, each as described herein). The system 10 may be configured to calculate an actual treatment outcome based on the image data and compare the outcome to the model outcome calculated in step 3. In some embodiments, the system 10 is configured to identify opportunities for improvement (e.g., modifications that may be made to the implanted stent (e.g., further expansion of the stent) and / or locations where additional stents or other treatments may be performed). In some embodiments, the user selects one or more reference frames within the image data, which may enable, for example, side-by-side comparison of various vessel positions before and after the intervention.

[0205] In step 5, relative metrics (eg, FFR gain) between the pre-intervention and post-intervention data may be displayed to the user.

[0206] 16A-16E, examples of various types of image data in accordance with the concepts of the present invention are shown. Fig. 16A shows an angiogram image including a relatively low resolution two-dimensional projection. Figs. 16B and 16C show slices of an OCT image recorded within the vessel shown in Fig. 16A. Figs. 16D and 16E show similar slices of an IVUS image recorded within the same vessel.

[0207] Referring now to FIG. 17, a graphical user interface of one embodiment for displaying image features automatically identified by an image processing algorithm in accordance with the concepts of the present invention is shown. In some embodiments, the algorithm 1015 is configured to analyze the image data and segment one or more features identified in the data. As shown in FIG. 17, the algorithm 1015 may be configured to segment one or more features selected from the group consisting of one or more side branches, a lumen wall, a strut of a stent, a profile of a stent, a portion of a catheter, a portion of a guidewire, a characteristic of a vessel wall, and combinations thereof. In some embodiments, the algorithm 315, 415 comprises a machine learning algorithm (e.g., a convolutional neural network (CNN)). The convolutional neural network (CNN) may comprise a deep neural network and / or a neural network that applies convolution operations. In some embodiments, the CNN algorithm is shift invariant, space invariant, and / or edge sensitive. In some embodiments, the system 10 includes a CNN algorithm or other machine learning algorithm that is trained using image data collected by the system 10. The training data may include an image dataset that is augmented to provide a balanced training set. For example, low quality images may be replicated to balance high quality images with low quality images. Images that include side branches may be replicated to balance images that include side branches with images that do not. Images that include stents and / or other devices may be replicated to balance images that include and do not include various devices. In some embodiments, one or more images (e.g., each image) of the training dataset are randomly shifted, zoomed, and / or rotated.

[0208] 18A-18C, various pre-processed examples of image data with different levels of blood in each image are shown in accordance with the concepts of the present invention. In FIG. 18A, there is blood in the image, but the image is generally clear. In FIG. 18B, there is a significant amount of blood in the image, but the data is clear enough that the lumen profile can be inferred from adjacent frames, etc. In FIG. 18C, the image is almost completely obstructed by blood. In some embodiments, the system 10 is configured to detect the presence of blood in the image, as described herein. In some embodiments, the system 10 classifies the image as having or not having blood (e.g., a binary classification against a blood amount threshold). Alternatively or additionally, the image may be classified by a percentage or other metric relative to the amount of blood in the image. In some embodiments, the algorithm 1015 comprises a CNN (convolutional neural network) configured to detect the presence of blood in a frame of image data. The algorithm 1015 may be configured to consider image data from adjacent frames. The output of the CNN may include a probability map (e.g., the probability of the presence of blood in each frame of image data).

[0209] 19A-19C, there is shown another OCT image in accordance with the concepts of the present invention. FIGs. 19A and 19B show OCT image slices where the calculated probability of blood in the image is displayed for each image. The probabilities shown in FIGs. 19A and 19B may be calculated using algorithm 1015 described herein. FIG. 19C shows multiple frames along the lumenogram with different amounts of blood in each frame.

[0210] Referring now to Figure 20, results of testing performed by the applicant according to the concepts of the present invention are shown. The applicant trained and tested a CNN algorithm for blood detection using a training dataset containing image data collected from 70 pullbacks. The applicant evaluated the algorithm and found an accuracy of 99.57%, a sensitivity of 98.0%, and a specificity of 99.6%. Testing resulted in 0.38% false positive results (105 images) and 0.05% false negative results (14 images).

[0211] Applicants have also trained and tested the same CNN algorithm for guide catheter detection. The training dataset consists of image data collected from 70 pullbacks. Applicants have evaluated the algorithm and found it to have an accuracy of 99.99%, a sensitivity of 99.99%, and a specificity of 100%.

[0212] Referring now to FIG. 21, a graphical representation of a neural network in accordance with the concepts of the present invention is shown. FIG. 21 illustrates an algorithm (e.g., algorithm 1015 described herein) configured as a neural network. In some embodiments, algorithm 1015 includes a neural network configured to identify boundaries of an imaged lumen (e.g., lumen segmentation) by analyzing longitudinal information (e.g., longitudinal methods), as shown in FIG. 21. Alternatively or additionally, algorithm 1015 may include a neural network configured to perform lumen segmentation using a dual domain model of polar and Cartesian coordinates for analyzing individual image slices, as described with reference to FIG. 2 or herein. In some embodiments, algorithm 1015 is configured to perform lumen segmentation using both dual domain and longitudinal methods. By analyzing image data using multiple methods, a more robust solution may be achieved. For example, without the information interpreted from the longitudinal model, the algorithm 1015 may not be able to distinguish between the lumen of the main artery and the lumen of the side branch, as shown, for example, in Figures 21A and 21B.

[0213] Further, referring to Figures 21A and 21B, there are shown image frames and longitudinal image data, respectively, in accordance with the concepts of the present invention. Figure 21A shows an image frame including a portion where it is difficult to distinguish between the wall of the lumen of the imaged vessel and a portion of a side branch of the imaged vessel. Figure 21B shows longitudinal image data of the same vessel, showing that the unknown portion of the image frame actually includes a portion of a side branch. By combining both domains, algorithm 1015 provides a more robust lumen segmentation.

[0214] Further, with reference to Figures 22A and 22B, there are shown representations of segmentation by a combined method and an example of segmented image data, respectively, in accordance with the concepts of the present invention. As shown in Figure 22A, lumen segmentation performed by analyzing individual frames of image data (e.g., two-dimensional slices of the image data) may be combined with segmentation performed by analyzing a longitudinal model of the image data to produce a refined segmentation result. Figure 22B shows a frame of image data including a portion of a side branch, where the segmented lumen follows the lumen profile rather than the side branch profile.

[0215] In some embodiments, algorithm 1015 (e.g., algorithm 1015 of FIG. 21 and / or FIG. 2) is configured to skip one or more layers of the neural network and perform one of a number of trained image processing applications (e.g., each module of algorithm 1015 uses only the layers of the neural network necessary to perform segmentation).

[0216] 23 and 24, various representations of data collected by the applicant according to the concepts of the present invention are shown. In some embodiments, the system 10 includes an AI algorithm (e.g., algorithm 1015 described herein). The algorithm 1015 can be trained to perform lumen segmentation as described herein. The applicant trained and tested such an algorithm using training data including image data collected from 65 pullbacks. The applicant evaluated the algorithm using a weighted Dice score. A sample of the results is shown in FIG. 23. FIG. 24 shows an example of a segmented image with a Dice score of about 0.8. The applicant's testing showed that 90% of the segmented images have a Dice score above 0.795.

[0217] 25A-26B, various representations of data collected by the applicant according to the concepts of the present invention are shown. In some embodiments, the system 10 comprises an AI algorithm (e.g., algorithm 1015 described herein). The algorithm 1015 may be trained to perform stent detection (e.g., to automatically quantify one or more stent features (e.g., stent area and / or apposition). The algorithm 1015 may be configured to delineate and quantify side branch extent and / or quantify stent healing. In some embodiments, the algorithm 1015 comprises a "DD2Net Full Fusion Architecture" (e.g., similar to algorithm 1015 described herein with reference to FIG. 9). The applicant trained and tested such algorithm using training data having image data including approximately 24,000 images collected from 70 pullbacks. Examples of segmented images are shown in FIGS. 25A and 25B. Applicants evaluated the stent segmentation algorithm by calculating the percentage of actual stent struts segmented relative to the total number of stent struts in each image frame (e.g., determined manually and / or otherwise). Testing resulted in an average score of over 99.2 for 24 pullbacks. Figure 26B shows a comparison of the average number of identified struts versus actual struts for the sample of pullbacks tested. Testing yielded a false positive rate of 0.46% and a false negative rate of 0.15%.

[0218] 27A-28, various representations of data collected by applicants according to the concepts of the present invention are shown. In some embodiments, system 10 includes an AI algorithm (e.g., algorithm 1015 described herein). Algorithm 1015 can be trained to perform flow diverter detection (e.g., to automatically identify flow diverter coverage of aneurysms and / or to identify malpositions). Applicants trained and tested such algorithms using training data having image data including approximately 3500 images collected from five pullbacks. Examples of segmented images are shown in FIGS. 27A and 27B. Applicants evaluated the diverter segmentation algorithm by calculating the percentage of actual diverter strut segmentations relative to the total number of diverter struts in each image frame (e.g., determined manually and / or otherwise). Testing resulted in an average score of greater than 97.1% over five pullbacks. FIG. 28 shows the average matches identified at each pullback, as well as the false positives and false negatives. The test results showed a false positive rate of 0.9% and a false negative rate of 0.05%.

[0219] 29, a method of capturing image data and applying AI algorithms to the image data to develop improved medical procedures and obtain regulatory approval for those procedures is shown, according to the concepts of the present invention. Diagnostic and / or therapeutic medical procedure data, including OCT image data and other clinical data collected by system 10 and other medical devices, is collected at one or more (e.g., multiple) clinical sites (CS). This collected medical procedure data (also referred to as "MP data") may be transferred to a centralized data storage and / or processing location (e.g., server 400 described herein (e.g., a cloud-based server shown in FIG. 29)). The MP data may be transferred from server 400 to one or more clinical site CS. The MP data (e.g., from multiple patients treated at multiple clinical site CS) may be analyzed via one or more algorithms (e.g., AI algorithms such as algorithm 1015 described herein) of system 10 to generate improved treatment plans for future patient diagnostic and / or therapeutic treatments. The output of these AI algorithms may be prepared by the manufacturer MFG of the system 10 as one or more regulatory submissions to be submitted to one or more regulatory authorities RA, as shown in FIG. 29. Once regulatory approval is obtained, these treatment plans generated by the one or more AI algorithms of the system 10 may be provided to the clinical site CS for use in future medical procedures. For example, the regulatory approved AI algorithm 1015 may comprise an algorithm that analyzes the collected MP data (e.g., at least the OCT data) and provides feedback to the clinician almost immediately, including a diagnosis, a treatment plan, and / or other medical information for the clinician. In other words, this feedback to the clinician is provided by the system 10 at the time at least a portion of the analyzed data is collected (e.g., at the time the most recent data is collected), eliminating the need to transfer the MP data to a location away from the clinical site (off-site location) for analysis and return to the clinical site CS (e.g., avoiding delays of hours or days).

[0220] In some embodiments, for example, to protect patient confidentiality, the MP data is encrypted before being transferred between the server 400 and the clinical site CS. In some embodiments, each clinical site CS may be assigned a unique private encryption key. This may prevent (or at least inhibit) a first location (first site) CS from (e.g., accidentally or maliciously) receiving MP data from a second location (second site) CS and being able to decrypt that data (e.g., without the unique key). In some embodiments, the system 10 encrypts the MP data sufficiently to comply with patient privacy laws (e.g., HIPAA laws).

[0221] The MP data captured by the system 10 and processed by the one or more AI algorithms 1015 of the system 10 may include OCT data (e.g., HF-OCT data), angiography data, FFR data, and / or flow data (e.g., data collected at pre-treatment, treatment, and / or post-treatment follow-up procedures). In some embodiments, the algorithms 1015 include AI algorithms configured to analyze the image data to identify and / or characterize one or more of a vessel lumen (e.g., lumen wall), one or more side branches, one or more insertion devices (e.g., guide catheters, imaging catheters, and / or guidewires), and combinations thereof. In some embodiments, the algorithms 1015 include AI algorithms configured to analyze the image data to identify and / or characterize one or more of a stenosis (e.g., left main stenosis), diffuse disease, aneurysms, and combinations thereof.

[0222] In some embodiments, the system 10 may be configured (e.g., via the AI-based algorithms 1015) as a “virtual clinical expert” or “remote clinical expert.” For example, the system 10 may be configured to perform a procedure evaluation (e.g., a procedure evaluation including analysis of OCT image data, angiography image data, or both). In these embodiments, the system 10 may be configured to evaluate one or more of pullback length, flushing procedure effectiveness, guide catheter engagement, removal of blood distal to the lesion, and combinations thereof. In these embodiments, the system 10 may be configured to provide “real-time coaching” to one or more users of the system 10. The system 10 may be configured (e.g., via the AI-based algorithms 1015) to provide improved image interpretation (e.g., explicitly redirecting clinician time to human tasks (e.g., interpersonal decision-making and / or creative tasks)).

[0223] In some embodiments, system 10 may be configured (e.g., via AI-based algorithms 1015) as a "virtual service technician." For example, system 10 may be configured to analyze (e.g., automatically analyze) image brightness. System 10 may be configured to identify trends across catheters used in one or more clinical procedures. System 10 may be configured to detect problems with components of system 10.

[0224] In some embodiments, system 10 may be configured (e.g., via AI-based algorithms 1015) to improve user (e.g., clinician) performance and / or improve the outcome of medical procedures, such as by improving a clinician's image interpretation capabilities, reducing variability between clinical practices, improving procedural success rates for less-frequent users of system 10, and / or minimizing errors.

[0225] In some embodiments, system 10 may be configured (e.g., via AI-based algorithm 1015) to provide predictive information (e.g., stent implantation index data, flow diverter implantation index data, coil implantation index data, and combinations thereof, in which case algorithm 1015 provides predictive index information).

[0226] In some embodiments, the MP data (e.g., anonymized MP data) stored on server 400 may be accessed by third parties (e.g., clinical site CSs and other research collaborators of the manufacturer MFG). In some embodiments, a financial transaction is associated with accessing the data and / or receiving an analysis of the data (e.g., performed by AI-based algorithm 1015), such as when the financial transaction includes a payment to the manufacturer of system 10. The MP data stored on server 400 may provide a significant data barrier for the manufacturer of system 10.

[0227] In some embodiments, the system 10 is configured to encode the image data with information relevant to processing of the image data. For example, the system 10 may include a standard imaging probe 100 and an improved imaging probe 100 (e.g., an improved imaging probe that encodes the collected image data with information that enables advanced image processing). The embedded information allows the analysis functions of the system 10 to be enabled or disabled based on the imaging probe 100 used to collect the image data. In some embodiments, the system 10 identifies the type of imaging probe 100 being used by an RFID tag embedded in the probe.

[0228] The above-described embodiments should be understood as illustrative examples only, and further embodiments are contemplated. Features described in this specification in connection with any embodiment may be used alone or in combination with other features described, or in combination with one or more features of other embodiments, or in combination with any combination of other embodiments. Moreover, equivalents and modifications not described above may be employed without departing from the scope of the inventive concept as defined in the appended claims.

Claims

1. 1. An imaging system for use with a patient, comprising: an imaging probe; an imaging assembly configured to optically couple to the imaging probe; and a processing unit; The imaging probe comprises: an elongate shaft having a proximal end, a distal portion, and a lumen extending between the proximal end and the distal portion; a rotatable optical core having a proximal end and a distal end, the optical core being at least partially disposed within the lumen of the shaft; an optical assembly disposed near the distal end of the optical core, the optical assembly configured to direct light toward and collect reflected light from tissue to be imaged; the imaging assembly is configured to illuminate light into the imaging probe and to receive the reflected light collected by the optical assembly; the processing unit comprises a processor and a memory coupled to the processor; the memory is configured to store instructions for the processor to execute an algorithm; the imaging system is configured to record image data based on the reflected light collected by the optical assembly; the image data includes data collected from a portion of a blood vessel during a pullback procedure; the algorithm is configured to analyze the image data; the algorithm is configured to perform segmentation of the image data; the segmentation includes segmentation of side branches; The imaging system, wherein the algorithm is configured to calculate computational fluid dynamics of the portion of the blood vessel.

2. The image data includes OCT image data. The imaging system of claim 1 .

3. The segmentation further comprises at least one selected from the group consisting of a processing device segmentation, a guide catheter segmentation, a guidewire segmentation, an implant segmentation, an intravascular implant segmentation, a flow diverter segmentation, a lumen segmentation, and combinations thereof. The imaging system of claim 1 .

4. the algorithm comprises a neural network adapted to perform the segmentation; The imaging system of claim 1 .

5. the algorithm is configured to generate a reliability metric configured to represent the quality of the results of the image processing step; 5. The imaging system of claim 1.

6. the algorithm comprises an artificial intelligence algorithm; 5. The imaging system of claim 1.

7. the artificial intelligence algorithm comprises a machine learning algorithm, a deep learning algorithm, or a neural network; 7. The imaging system of claim 6.

8. the algorithm comprises a neural network and is configured to skip one or more layers in the neural network; 7. The imaging system of claim 6.

9. the algorithm comprises a single neural network trained to perform two or more image segmentation processes; 7. The imaging system of claim 6.

10. The artificial intelligence algorithm is trained to perform side branch segmentation, and the algorithm achieves an average weighted Dice score of 0.81 or greater.

7. The imaging system of claim 6.

11. the algorithm is configured to receive image data in a single image domain; the algorithm is configured to transform the image data into one or more additional image domains; 5. The imaging system of claim 1.

12. the algorithm is configured to process the image data in one or more image domains; The one or more image domains: selected from the group consisting of a polar domain, a Cartesian domain, a longitudinal domain, a frontal image domain, and a domain generated by calculating image features, and combinations thereof; The image features include: primary and / or secondary features, image texture, image entropy, homogeneity, correlation, contrast, energy, and / or any other image feature; 5. The imaging system of claim 1.

13. further comprising a graphical user interface configured to be displayed to a user; 5. The imaging system of claim 1.

14. the graphical user interface is configured to provide an indicator of image data quality.

14. The imaging system of claim 13.

15. the indicator of image data quality is displayed in relation to a cross-sectional OCT image.

15. The imaging system of claim 14.

16. the graphical user interface is configured to allow a user to review the results of the image processing steps; 14. The imaging system of claim 13.

17. the graphical user interface is further configured to allow a user to approve the results of the image processing steps.

17. The imaging system of claim 16.

18. the graphical user interface is further configured to allow a user to edit the results of the image processing steps.

17. The imaging system of claim 16.

19. the algorithm comprises an artificial intelligence algorithm; the image processing step is performed by the artificial intelligence algorithm; 17. The imaging system of claim 16.

20. the graphical user interface comprises a plurality of workspaces; The data displayed in each of the workspaces is synchronized.

14. The imaging system of claim 13.

21. The data is synchronized by a time index.

21. The imaging system of claim 20.

22. The data is synchronized by location index.

21. The imaging system of claim 20.

23. configured to acquire image data before and after an interventional procedure; 5. The imaging system of claim 1.

24. The algorithm is configured to compare the image data before the interventional procedure with the image data after the interventional procedure to quantify the effect of the interventional procedure.

24. The imaging system of claim 23.

25. the algorithm comprises an artificial intelligence algorithm; 25. The imaging system of claim 24.

26. the algorithm comprises a bias; 5. The imaging system of claim 1.

27. further comprising a user interface; the bias is input and / or modifiable via the user interface; 27. The imaging system of claim 26.

Images