Selection method for cerebral embolic protection in TAVI procedure

Abstract

Whether to deploy a cerebral embolic protection CEP device is determined by first acquiring one or more images of at least a portion of the aorta, including the aortic valve. The images are segmented to identify the aortic valve, the aortic arch, and multiple branch vessels downstream of the aortic valve. Plaque within the image segments at or adjacent to the aortic valve is identified, and a vulnerability score associated with the plaque is generated. Blood flow dynamics in the aortic arch and at least one of the multiple branch vessels are evaluated. Thereafter, a determination is made as to whether a CEP device should be deployed based at least in part on the vulnerability score. The CEP device is selected based at least in part on the blood flow dynamics in the aortic arch.

Description

[Technical Field]

[0001] The present disclosure relates generally to systems and methods for determining the need for cerebral embolic protection (CEP) during a procedure such as surgery and selecting an appropriate such protection. In particular, the present disclosure relates to determining an appropriate CEP device for use during a TAVI or TAVR procedure. [Background technology]

[0002] Medical procedures involving arterial blood vessels often create a risk of plaque detaching from the vessel wall. For example, transcatheter aortic valve implantation or replacement (used interchangeably as TAVI / TAVR) creates a risk of plaque fragments detaching and traveling with the blood through various arteries, potentially causing a stroke, among other potential consequences.

[0003] Cerebral embolic protection (CEP) devices have been commercially available for some time and come in several forms. Two examples are Boston Scientific's Sentinel system and KeystoneHeart's TriGuard. The purpose of these devices is to capture plaque debris detached during the TAVI procedure while a prosthetic valve is being inserted. Therefore, CEP devices typically function as a filter, filtering blood flowing through specific vessels branching from the aortic arch. There are typically three major arteries emanating from the aortic arch that require protection. While Sentinel only covers the sternal artery and left common carotid artery, TriGuard additionally covers the left subclavian artery, from which the left vertebral artery arises, thereby covering the three major arteries emanating from the aortic arch. Summary of the Invention [Problem to be solved by the invention]

[0004] In the context of a procedure such as TAVI, not all patients require or benefit from the use of a CEP device. Furthermore, the use of a CEP device may increase the time and / or costs associated with the procedure and may not be desirable in all cases. Therefore, there is a need to determine whether a patient will benefit from the use of a CEP device prior to a TAVI or TAVR procedure. Furthermore, as noted above, there are three major cerebral blood vessels branching from the aortic arch, but not all CEP devices cover all three, and even when a CEP device is determined to be appropriate, not all patients will benefit from coverage of all vessels.

[0005] Therefore, there is a need for a pre-operative method for identifying patients who could benefit from a CEP device during a TAVI procedure, and a method for selecting and implanting an appropriate CEP device once it has been determined that a CEP device is appropriate. [Means for solving the problem]

[0006] A method and system are provided for selecting and implementing a cerebral embolic protection device in the context of a procedure such as a TAVI procedure.

[0007] This method assumes that patients will benefit from a CEP device during a TAVI procedure if they have plaque in the area of ​​the aorta where the new valve will be deployed. Furthermore, if the angle between the aorta and the left subclavian artery is relatively small, a device that covers all three major branches of the aorta will provide benefit. The presence of both features can be detected on computed tomography (CT) images, such as spectral CT images.

[0008] In some embodiments, a method for deploying a cerebral embolic protection (CEP) device is provided, the method including acquiring one or more images of at least a portion of an aorta, the one or more images including an aortic valve, and segmenting the one or more images to identify the aortic valve, the aortic arch, and a plurality of branch vessels downstream of the aortic valve.

[0009] The method then identifies plaque in one or more image segments at or adjacent to the aortic valve and generates a vulnerability score associated with the identified plaque. The method then evaluates blood flow dynamics in the aortic arch and at least one of the plurality of branch vessels.

[0010] The method then determines that a CEP device should be deployed based at least in part on the vulnerability score, and selects a CEP device from the plurality of potential CEP devices based at least in part on the dynamics of blood flow in the aortic arch.

[0011] In some embodiments, the vulnerability score correlates with the risk of at least a portion of the plaque detaching during a surgical procedure applied to the aortic valve, which may be a transcatheter aortic valve implantation (TAVI) procedure applied to the aortic valve.

[0012] In some embodiments, the vulnerability score is based at least in part on the type of implant used in the TAVI procedure.

[0013] In some embodiments, the vulnerability score is based at least in part on the total plaque volume, the spatial organization of the plaque at or adjacent to the aortic valve, and the percentage of lipid in the total plaque volume, hi some such embodiments, the vulnerability score is further based on morphological factors associated with the plaque.

[0014] In some embodiments, the fragility score is determined by an artificial intelligence (AI)-based model trained on known outcomes of previous surgical interventions correlated with corresponding previous images of the aorta, where the previous images include the corresponding aortic valve.

[0015] In some embodiments, the determination that a CEP device should be deployed is based at least in part on the vulnerability score rather than the branch angle, and the CEP device to be deployed is selected based at least in part on the branch angle rather than the vulnerability score.

[0016] In some embodiments, the one or more images are one or more computed tomography (CT) images, and segmentation of the one or more images is achieved by an artificial intelligence-based (AI) model to identify the aortic arch and the left subclavian artery (LSA).

[0017] In some embodiments, the method includes identifying a bifurcation angle between the LSA and the aortic arch, and selecting a CEP device based on a fluid dynamics model of blood flow between the aortic arch and the LSA, the fluid dynamics model determining a probability that plaque in the aortic arch will enter the LSA based at least in part on the identified bifurcation angle.

[0018] In some embodiments, the fluid dynamics model is further based on the size of the LSA, which is determined from the image or is known independently.

[0019] In some embodiments, the plurality of branch vessels includes the brachiocephalic trunk, the left common carotid artery (CCA), and the LSA, and a first CEP device of the plurality of CEP devices covers the brachiocephalic trunk and the CCA but not the LSA, and a second CEP device of the plurality of CEP devices covers the brachiocephalic trunk, the CCA, and the LSA.

[0020] In some embodiments, the bifurcation angle is between the left subclavian artery (LSA) and the aortic arch. The method then includes determining whether a size of the LSA is greater than a threshold size, wherein a first CEP device of the plurality of CEP devices covers the brachiocephalic trunk and the CCA but not the LSA, and a second CEP device of the plurality of CEP devices covers the brachiocephalic trunk, the CCA, and the LSA. If the method determines that a CEP should be deployed and that the LSA is greater than the threshold size or the bifurcation angle is greater than the threshold angle, the method further determines that a second CEP device should be deployed.

[0021] A system for deploying CEP devices is also provided, the system including a plurality of implantable CEP devices, a memory storing a plurality of instructions, and a processor circuit coupled to the memory and configured to execute the instructions to implement the above-described method.

[0022] The instructions are executed to obtain one or more images of at least a portion of the aorta to be processed, the one or more images including the aortic valve; the instructions are further executed to segment the one or more images to identify the aortic valve, the aortic arch, and a plurality of branch vessels downstream of the aortic valve; identify plaque in segments of the one or more images at or adjacent to the aortic valve; and generate a vulnerability score associated with the identified plaque, the vulnerability score correlating with a risk of at least a portion of the plaque detaching during a surgical procedure applied to the aortic valve; and the instructions are further executed to determine that a CEP device should be deployed based on the vulnerability score; identify a bifurcation angle between one of the plurality of branch vessels and the aortic arch; and select a CEP device from a plurality of implantable CEP devices based at least in part on the bifurcation angle.

[0023] The selected CEP device is implanted prior to performing the surgical procedure.

[0024] In some embodiments, the system includes a computed tomography (CT) imaging device, and the processor circuitry is capable of acquiring one or more images from the imaging device. [Brief explanation of the drawings]

[0025] [Figure 1] FIG. 1 is a schematic diagram of a system according to one embodiment of the present disclosure. [Figure 2] FIG. 1 shows the heart and aorta evaluated with a method according to the present disclosure. [Figure 3A] FIG. 1 illustrates the aortic arch as assessed by a method according to the present disclosure. [Figure 3B] FIG. 1 illustrates a scan of an aortic valve assessed with a method according to the present disclosure. [Figure 3C] FIG. 1 shows a scan of the aortic arch evaluated with a method according to the present disclosure. [Figure 4] FIG. 1 illustrates a first potential CEP device deployed according to the methods of the present disclosure. [Figure 5] FIG. 10 illustrates a second potential CEP device deployed according to the methods of the present disclosure. [Figure 6] FIG. 1 illustrates an image processing method according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0026] The description of illustrative embodiments according to the principles of the present invention is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description. In describing the embodiments of the present invention disclosed herein, any reference to direction or orientation is intended merely for convenience of description and is not intended in any way as a limitation on the scope of the present invention. Relative terms such as "lower," "upper," "horizontal," "vertical," "over," "below," "up," "down," "top," "bottom," and derivatives thereof (e.g., "horizontally," "downwardly," "upwardly," etc.) should be interpreted as referring to the orientation currently described or shown in the drawings under discussion. These relative terms are for convenience of description only and do not require that the device be constructed or operated in a particular orientation, unless expressly so indicated. Terms such as "attached," "fixed," "connected," "coupled," "interconnected," and the like refer to a fixed or attached relationship in which structures are fixed or attached to each other, directly or indirectly through intervening structures, and both movable and rigid attachments or relationships, unless expressly stated otherwise. Furthermore, features and advantages of the present invention will be described with reference to the illustrated embodiments. Accordingly, the present invention is not to be expressly limited to such exemplary embodiments, which illustrate some non-limiting combinations of features that may exist alone or in other combinations of features. The scope of the present invention is defined by the claims appended hereto.

[0027] The present disclosure describes one or more best modes presently contemplated for carrying out the present invention. This document is not intended to be understood in a limiting sense, but rather provides examples of the present invention presented for illustrative purposes only, with reference to the accompanying drawings, to advise those skilled in the art of the advantages and structure of the present invention. In the various views of the figures, like reference characters indicate like or similar parts.

[0028] It is important to note that the disclosed embodiment is merely one example of the many advantageous applications of the innovative teachings herein. In general, statements made in the specification of this application do not necessarily limit any of the various claimed disclosures. Moreover, some statements may apply to certain inventive features but not to other inventive features. Generally, unless otherwise indicated, singular elements may be plural, and vice versa, without loss of generality.

[0029] Pre-operative techniques are described for identifying patients who may benefit from a CEP device in a TAVI procedure. Typically, when a patient is scheduled to undergo a TAVI procedure, the methods described herein can be applied to determine whether the patient is a good candidate for a CEP device. Once the patient is determined to be a good candidate for such a CEP device, the methods include selecting, and in some embodiments implanting, an appropriate CEP device prior to the TAVI procedure.

[0030] This method assumes that a patient would benefit from a CEP device during a TAVI procedure if they have plaque in the area of ​​the aorta where the new valve will be placed and the configuration of such plaque suggests the possibility of detachment. Furthermore, if plaque in the bloodstream leaving the surgical site is likely to flow into the left subclavian artery, a device covering all three major branches of the aorta would provide a benefit. To assess such probability, the method can employ a fluid dynamics-based analysis, or a surrogate for such analysis. For example, if the angle between the aortic arch and the left subclavian artery (LSA) is relatively small, such risk may be low. Similarly, the smaller the LSA, the lower the risk of plaque invading the vessel. The presence of such anatomical features can be detected in medical images obtained from the patient.

[0031] Thus, the method typically includes acquiring images of at least a portion of the aorta where the TAVI procedure will be performed. Such images are typically CT images, and may be spectral CT images, capturing the patient's aortic valve, left ventricular outflow tract, and aortic arch. In some embodiments, a CT unit may be employed to generate one or more images as part of the method. However, in many embodiments, imaging will have been performed prior to the procedure, and such existing imaging may be usable to implement the methods described herein.

[0032] Additionally, in some embodiments, preoperative imaging can take forms other than CT. Medical imaging other than CT, such as magnetic resonance imaging (MRI) or positron emission tomography (PET), can use different methods for image processing, and the resulting images may take different forms. This disclosure discusses embodiments in terms of CT imaging. However, it should be understood that the methods and systems described herein can be used in the context of other imaging modalities as well.

[0033] 1 is a schematic diagram of a system 100 according to one embodiment of the present disclosure. As shown, the system 100 generally includes a processing unit 110 and an imaging unit 120.

[0034] The processing device 110 can apply processing routines to measurement data, such as image or projection data, received from the imaging device 120. The processing device 110 can include a memory 113 and a processor circuit 111. The memory 113 can store a plurality of instructions. The processor circuit 111 is coupled to the memory 113 and configured to execute the instructions. The instructions stored in the memory 113 can include processing routines, data associated with the processing routines, such as machine learning algorithms, and various filters for processing the images. While all data is described as being stored in the memory 113, it should be understood that in some embodiments, some data can be stored in a database, which can itself be stored in memory or on a separate, remote system.

[0035] Processing device 110 may further include an input 115 and an output 117. Input 115 may receive information, such as images or measurement data, from imaging device 120. Output 117 may output information, such as processed images, to a user or a user interface device. Output 117 may also output decisions, such as recommendations, generated by the methods described below. The output may include a monitor or display.

[0036] In some embodiments, the processing device 110 may be directly associated with the image capture device 120. In alternative embodiments, the processing device 110 may be separate from the image capture device 120, such that it receives image or measurement data for processing at input 115 via a network or other interface.

[0037] In some embodiments, the imaging device 120 may include image data processing equipment and a spectral or conventional CT scanning unit that generates CT projection data when scanning an object (eg, a patient).

[0038] While a system including an imaging device 120 and a processing device 110 is shown, it should be understood that the method may be implemented directly on the processing device, such as in the context of images received at input 115 over a network. The method described herein generally involves processing images as a component of deploying a cerebral embolic protection (CEP) device in the context of a procedure such as a TAVI procedure. As noted above, imaging occurs prior to such a procedure. As such, previously generated imaging may be acquired by input 115 and evaluated prior to acquiring new images or in lieu of acquiring new images.

[0039] Figure 2 shows a heart 200 and an aorta 210 as assessed by a method according to the present disclosure. Figure 3A shows a more detailed view of the aortic arch 220 shown in Figure 2 as assessed by a method according to the present disclosure. Figure 3B shows a scan of an aortic valve 230 as assessed by a method according to the present disclosure. Figure 3C shows a scan of the aortic arch 220 as assessed by a method according to the present disclosure.

[0040] Figure 4 illustrates a first potential CEP device 400 that may be deployed according to the methods of the present disclosure. Figure 5 illustrates a second potential CEP device 500 that may be deployed according to the methods of the present disclosure. Figure 6 illustrates an image processing method according to the present disclosure.

[0041] As shown in Figures 3B and 3C, one or more images of a portion of a patient can be acquired to evaluate the elements highlighted in the diagram of Figure 2. Such images include the left ventricular outflow tract (LVOT) 240, aortic valve 230, ascending aorta 250, aortic arch 220, and descending aorta 260. Three major branch vessels can be identified from the aortic arch, including the brachiocephalic artery (also called the brachiocephalic trunk or sternal artery) 270, the left common carotid artery (CCA) 280, and the left subclavian artery (LSA) 290.

[0042] As shown in Figures 4 and 5, some CEP devices 400 cover all three major branch vessels 270, 280, 290, while other CEP devices 500 cover only two of such vessels, typically the brachiocephalic artery 270 and the CCA 280.

[0043] In implementing this method, the illustrated system 100 may first acquire (600) at input 115 at least one image of at least a portion of the aorta 210, such as the aortic arch 220 shown in FIG. 3A. It should be understood that the image or images provided should show the aortic valve 230 and the main branch vessels 270, 280, and 290. However, these features will typically be in different planes. Thus, as shown in FIGS. 3B and 3C, a first image 3B may be provided to show the LVOT 240, the aortic valve 230, and the ascending aorta 250, and a second image 3C may be provided to show the three main branch vessels 270, 280, and 290.

[0044] Thus, in the context of this disclosure, when referring to an image acquired by system 100, it should be understood that this may refer to one or more images.

[0045] The method then performs 610 various image processing methods to identify 620 plaque in a segment of the image at or adjacent to the aortic valve 230 and left ventricular outflow tract 240, including the ascending aorta 250. The acquired image (in step 600) may be a CT scan of the patient, and in some embodiments, may be a spectral CT scan.

[0046] Therefore, before identifying plaque (at step 620), the method may first perform various processing methods (at step 610) and apply them to the acquired image. Such processing methods include model-based segmentation, which fits a surface model or voxel mask to the image. Other segmentation approaches can be used as well. Such processing may then be used to identify and segment the various vessels associated with the aorta, including the aortic arch 220 and the three major branch vessels 270, 280, 290.

[0047] The segmentation or other image processing achieved by this method (step 610) can be based on an AI-based model, such as a convolutional neural network (CNN), which can be used to identify the aortic arch 220 and the LSA 290, and a bifurcation angle 300 can be defined between the aortic arch and the LSA.

[0048] Once plaque is identified (at step 620) and determined to be at or adjacent to the aortic valve 230, such plaque may be analyzed (at 630). Because the TAVI procedure involves placement or replacement of the aortic valve 230, any plaque in that area may be disturbed during the procedure, and as such, the analysis (at step 630) determines whether such plaque is likely to detach during the procedure.

[0049] The analysis (in step 630) may consider various factors related to the plaque, including the total volume of the plaque 660, the exact spatial organization of the plaque 670, the specific composition of the plaque 680, such as the percentage of lipids in the total volume of the plaque, and morphological factors related to the plaque, such as the roughness of the plaque surface 690. Aspects of the spatial organization of the plaque 670 that are considered may include the circumferential extent and the extent of the area where the implant is deployed.

[0050] The analysis (in step 630) can take into account not only risk factors associated with the patient, but also external factors 700 such as the type of implant used and the exact nature of the procedure being performed. If the image acquired (in step 600) is a spectral CT scan, specific characteristics of the plaque can be more easily identified during the analysis.

[0051] The analysis (630) can then generate (710) a vulnerability score associated with the plaque identified (in step 620). Such a vulnerability score is a proxy for, and can therefore be correlated with, the risk of plaque detachment during a surgical procedure applied to the aortic valve. The analysis (630) can be an AI model independent of the segmentation model implemented above (step 610). The AI ​​model can be, for example, a convolutional neural network, and can be trained based on a set of previous medical images of a similar type to those acquired (in step 600), e.g., spectral CT scans, and known outcome data regarding intra- and post-procedural complications. In some embodiments, the AI ​​model can be trained based on the amount of plaque in the treatment area before and after the procedure, which can indicate plaque detachment during the procedure.

[0052] In some embodiments, the vulnerability score is based at least in part on the type of implant used in a particular TAVI procedure. In some embodiments, the vulnerability score is based at least in part on the total plaque volume, the spatial organization of the plaque at or adjacent to the aortic valve, and the percentage of lipid in the total plaque volume. In some embodiments, the vulnerability score is further based on morphological factors associated with the plaque, such as surface roughness.

[0053] Once the analysis (at step 630) generates a vulnerability score (710), such score is used to determine (720) whether a CEP device should be deployed to the patient prior to the TAVI procedure. In some embodiments, the vulnerability score (at step 710) can be a single value, and the determination (at step 720) is simply a determination of whether the generated value is above or below a threshold. In other embodiments, the vulnerability score (at step 710) can include additional factors, including, for example, other risk factors associated with the patient 700, and the determination (at step 720) can collate all risk factors to generate a recommendation.

[0054] In some embodiments, further analysis of the image is performed only if the method first determines that a CEP device should be deployed (at step 720). In other embodiments, all image analysis is performed in parallel.

[0055] Therefore, the method further utilizes the segmentation of the aortic arch 220 (performed in step 610) to determine (730) whether plaque detached from the wall of the aorta 210 at or adjacent to the valve 230 is likely to flow into a particular branch vessel. While all vessels can be evaluated, the LSA 290 is typically evaluated in particular. CEP devices 400, 500 considered for implantation typically cover and thereby protect at least the brachiocephalic trunk 270 and left CCA 280. Some CEP devices 400 also cover and thereby protect the LSA 290.

[0056] Thus, the determination (at step 730) generally focuses on the probability that plaque in blood flowing through the aortic arch 220 will enter the LSA 290. This determination (at step 730) may be based on a fluid dynamics model and may include an evaluation of at least one branching angle 300 of the branching vessel relative to the centerline 310 of the aortic arch 220. In particular, the determination (at step 730) may calculate the branching angle 300 of the LSA 290 relative to the aortic arch 220. The calculation may be based on the angle between the local centerline 310 direction in the aortic arch 220 and the proximal LSA 290. A smaller angle 300 between the aortic arch 220 and the LSA 290 indicates that the two vessels are more similar in flow direction and, therefore, results in a higher probability that plaque debris will flow into the LSA with the aortic blood flow.

[0057] Thus, if angle 300 is small, the method may recommend a CEP device 400 that covers all three vessels 270, 280, 290. Alternatively, if angle 300 is larger, the method may instead recommend a CEP device 500 that covers only the brachiocephalic trunk 270 and left CCA 280. In some embodiments, a threshold angle is used, and thus defines a risk range within which a CEP device 400 covering the LSA 290 is recommended. For example, an angle 300 of 0 degrees indicates that the LSA 290 is directly aligned with the flow in the aortic arch 220, an angle less than 90 degrees indicates risk, and an angle greater than or equal to 90 degrees indicates no such risk.

[0058] The method may further consider additional factors as part of the fluid dynamics model to support determination 730. Thus, the segmentation model allows the method to determine the size of LSA 290, such as the diameter of the vessel. In some embodiments, the size of LSA 290 is not determined from the segmentation model but is instead known directly. If LSA 290 is large, there is a higher probability that plaque debris will be swept into the LSA by blood flow in the aorta 210, which may result in the method selecting a CEP device 400 that covers all three vessels. Alternatively, if LSA 290 is smaller, the method may instead recommend a CEP device 500 that covers only the brachiocephalic trunk 270 and left CCA 280. In some embodiments, the assessment of the size of LSA 290 is based on the percentage of the total lumen area of ​​all three arteries, and the LSA is evaluated to determine whether LSA 290 accounts for more than one-third of the total lumen area. In such an embodiment, if the LSA 290 occupies more than one-third of the total lumen area of ​​the three vessels 270, 280, 290, it indicates a risk and a decision (at step 730) results in a recommendation that a CEP device 400 be used that covers the LSA 290. In other embodiments, the LSA 290 may be evaluated relative to the total lumen area of ​​the aortic arch 220.

[0059] Thus, in some embodiments, the method may rely solely or primarily on the bifurcation angle 300 and compare the bifurcation angle to a threshold, such as 90 degrees, to determine whether the LSA 290 should be covered by the CEP device 400. Similarly, the method may rely solely or primarily on the size of the LSA 290, in which case the size may be compared to a threshold, such as one-third of the total lumen area of ​​the three vessels 270, 280, 290, to determine whether the LSA 290 should be covered. In some embodiments, more sophisticated fluid dynamic models may be utilized, and the determination (at step 730) may be based on additional factors, such as the bifurcation angle 300, the size of the LSA 290, and possibly the blood flow velocity in the patient.

[0060] In some such embodiments, an AI model can be utilized to make the decision (at step 730). The AI ​​model is trained based on anatomical and geometric classification, plaque characteristics, and / or the device used, including previous imaging, such as spectral CT imaging, paired with known outcome data regarding complications. The output of such an AI model can be a recommendation of which CEP device should be used. Similar training can be applied to new devices as they come onto the market.

[0061] In some embodiments, the decision that a CEP device should be deployed (at step 720) is based at least in part on the vulnerability score (at step 710) rather than the branch angle 300, and the CEP devices 400, 500 to be deployed are selected at least in part based on the branch angle rather than the vulnerability score. As such, the decision may be discrete. Alternatively, this decision can be used to inform one another.

[0062] Once a decision is made (at step 730), assuming the method determines that use of a CEP device is desirable (at step 720), the method may select a CEP device (at step 740) from a plurality of potential CEP devices based at least in part on the vulnerability score and branch angle.

[0063] Once selected (at step 740), the CEP device is implanted (at 750) prior to a medical procedure such as TAVI.

[0064] In some embodiments, the method is implemented by the system 100 described above, with or without the imaging device 120, which may be a spectral CT device. The system 100 further includes multiple implantable CEP devices 400, 500, where one of the CEP devices 500 is configured to cover and thereby protect only the brachiocephalic trunk 270 and the left CCA 280. The second CEP device 400 is instead configured to cover and thereby protect the brachiocephalic trunk 270, the left CCA 280, and the LSA 290.

[0065] The method is then implemented by the system 100, and once the system determines that a CEP device 400, 500 should be deployed based on the vulnerability score, the system further selects the CEP device for implantation, and the selected CEP device is implanted prior to performing the surgical procedure.

[0066] Although the method and system are described in terms of a TAVI / TAVR procedure, similar methods can be adapted and applied to identify risk during other vascular interventions, for example, risk can be similarly identified for abdominal artery stenting or carotid artery stenting.

[0067] The method according to the present disclosure can be implemented on a computer as a computer-implemented method, on dedicated hardware, or a combination of both. Executable code of the method according to the present disclosure can be stored in a computer program product. Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc. Preferably, the computer program product may include non-transitory program code stored on a computer-readable medium for performing the method according to the present disclosure when the program is run on a computer. In one embodiment, the computer program may include computer program code configured to perform all steps of the method according to the present disclosure when the computer program is run on a computer. The computer program may be embodied in a computer-readable medium.

[0068] While the present disclosure has been described at some length and with some specific examples in terms of multiple illustrated embodiments, the present disclosure is not intended to be limited to any such specific examples or embodiments, or to any particular embodiment, but rather should be construed with reference to the appended claims so as to give the broadest possible interpretation of the claims in view of the prior art and thus effectively encompass the intended scope of the present disclosure.

[0069] All examples and conditional language referenced herein are intended for educational purposes to aid the reader in understanding the principles of the present disclosure and the concepts contributed by the inventors to further advance the art, and should not be construed as being limited to such specifically referenced examples and conditions. Furthermore, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Furthermore, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed to perform the same function, regardless of structure.

Claims

1. 1. A method of operating a system for deploying a cerebral embolic protection device, or CEP device, the system comprising: a processor circuit; the processor circuit acquiring one or more images of at least a portion of the aorta, the one or more images including the aortic valve; the processor circuit segments the one or more images to identify the aortic valve, the aortic arch, and a plurality of branch vessels downstream of the aortic valve; the processor circuit identifying plaque in one or more segments of the image at or adjacent to the aortic valve; generating a vulnerability score associated with the identified plaque; the processor circuit assessing blood flow dynamics in the aortic arch and at least one of the plurality of branch vessels; determining, based at least in part on the vulnerability score, that the CEP device should be deployed; and wherein the processor circuit selects a CEP device from a plurality of potential CEP devices based at least in part on the dynamics of blood flow in the aortic arch.

2. The method of claim 1 , wherein the vulnerability score correlates with a risk of at least a portion of the plaque detaching during a surgical procedure applied to the aortic valve.

3. 3. The method of claim 2, wherein the surgical procedure is a transcatheter aortic valve implantation procedure, or TAVI procedure, applied to the aortic valve.

4. The method of claim 3 , wherein the fragility score is based at least in part on the type of implant used in the TAVI procedure.

5. 3. The method of claim 2, wherein the vulnerability score is based at least in part on total plaque volume, spatial organization of plaque at or adjacent to the aortic valve, and percentage of lipid in the total plaque volume.

6. The method of claim 5 , wherein the vulnerability score is further based on morphological factors associated with the plaque.

7. 6. The method of claim 5, wherein the fragility score is determined by an artificial intelligence-based model trained on known outcomes of previous surgical interventions correlated with corresponding previous images of the aorta, each previous image including a corresponding aortic valve.

8. 6. The method of claim 5, wherein the determination that the CEP device should be deployed is based at least in part on the vulnerability score and not on a bifurcation angle between one of the plurality of branch vessels and the aortic arch, and the CEP device to be deployed is selected based at least in part on the bifurcation angle and not on the vulnerability score.

9. 2. The method of claim 1, wherein the one or more images are one or more computed tomography images, and segmentation of the one or more images to identify the aortic arch and left subclavian artery, or LSA, is achieved by an artificial intelligence based model.

10. The method of claim 9, further comprising the step of the processor circuit identifying a branching angle between the LSA and the aortic arch, the selection of the CEP device being based on a fluid dynamics model of blood flow between the aortic arch and the LSA, the fluid dynamics model determining the probability of plaque in the aortic arch entering the LSA based at least in part on the identified branching angle.

11. The method of claim 10 , wherein the fluid dynamics model is further based on a size of the LSA, the size of the LSA being determined from the image or known independently.

12. 11. The method of claim 10, wherein the plurality of branch vessels includes the brachiocephalic trunk, the left common carotid artery, or in other words, the left CCA, and the LSA, and wherein a first CEP device of the plurality of CEP devices covers the brachiocephalic trunk and the CCA but not the LSA, and a second CEP device of the plurality of CEP devices covers the brachiocephalic trunk, the CCA, and the LSA.

13. The branching angle is between the left subclavian artery, in other words, the LSA, and the aortic arch, and the method further comprises the step of the processor circuit determining whether the size of the LSA is greater than a threshold size; a first CEP device of the plurality of CEP devices covers the brachiocephalic trunk and the common carotid artery, i.e., the CCA, but not the LSA; and a second CEP device of the plurality of CEP devices covers the brachiocephalic trunk, the CCA, and the LSA; 2. The method of claim 1, further determining that the second CEP device should be deployed when determining that the CEP device should be deployed and that the LSA is greater than the threshold size or the branch angle is greater than a threshold angle.

14. 1. A system for deploying a cerebral embolic protection device, or CEP device, comprising: a plurality of implantable CEP devices; a memory for storing a plurality of instructions; a processor circuit coupled to the memory and configured to execute the instructions, the instructions comprising: acquiring one or more images of at least a portion of the aorta to be processed, the one or more images including the aortic valve; segmenting one or more images to identify the aortic valve, the aortic arch, and a plurality of branch vessels downstream of the aortic valve; identifying plaque in a segment of the one or more images at or adjacent to the aortic valve; generating a vulnerability score associated with the plaque, the vulnerability score correlating with a risk of at least a portion of the plaque detaching during a surgical procedure applied to the aortic valve; determining that the CEP device should be deployed based on the vulnerability score; identifying a bifurcation angle between one of the plurality of branch vessels and the aortic arch; selecting the CEP device from a plurality of implantable CEP devices based at least in part on the branching angle; The system wherein the selected CEP device is implanted prior to performing the surgical procedure.

15. 15. The system of claim 14, wherein the surgical procedure is a transcatheter aortic valve implantation procedure applied to the aortic valve.

16. 15. The system of claim 14, wherein the vulnerability score is based at least in part on total plaque volume, spatial organization of plaque at or adjacent to the aortic valve, and percentage of lipid in the total plaque volume.

17. 15. The system of claim 14, wherein the fragility score is determined by an artificial intelligence-based model trained on known outcomes of previous surgical interventions correlated with corresponding previous images of the aorta, each previous image including a corresponding aortic valve.

18. 15. The system of claim 14, wherein the one or more images are one or more computed tomography images, segmentation of the one or more images is achieved by an artificial intelligence-based model to identify the aortic arch and left subclavian artery, or LSA, the bifurcation angle being between the LSA and the aortic arch, and selection of the CEP device is based on a fluid dynamics model of blood flow between the aortic arch and the LSA, the fluid dynamics model determining a probability that plaque in the aortic arch will enter the LSA based at least in part on the identified bifurcation angle.

19. determining whether the bifurcation angle is between the left subclavian artery, i.e., the LSA, and the aortic arch and whether the size of the LSA is greater than a threshold size; a first CEP device of the plurality of CEP devices, when deployed, covers the brachiocephalic trunk and the common carotid artery, i.e., the CCA, but does not cover the LSA, and a second CEP device of the plurality of CEP devices, when deployed, covers the brachiocephalic trunk, the CCA, and the LSA; 15. The system of claim 14, further determining that the second CEP device should be deployed when determining that the CEP device should be deployed and that the LSA is greater than the threshold size or the branch angle is greater than a threshold angle.

20. The system of claim 14 , further comprising a computed tomography imaging device, wherein the processor circuitry acquires the one or more images from the imaging device.

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