Method and system for determining the likelihood of regional rupture of blood vessels

Abstract

A method for determining a regional rupture probability (RRP), which indicates a state of local weakening of a blood vessel, based on parameters correlated with dilation and local weakening of the blood vessel is provided. The method includes receiving multiple images of the blood vessel in a multiphase stack. Geometric models of the lumen and the outer wall of the blood vessel are generated and smoothed to obtain a volume mesh and a surface mesh, respectively. An ILT thickness distribution, local deformation in each phase, and a wall strain distribution indicating the maximum principal strain in the outer wall are determined. Blood flow values ​​in the lumen are obtained, and a wall shear stress distribution indicating wall shear disturbances in the lumen is calculated. The RRP is determined based on the ILT thickness distribution, wall shear stress, and wall strain.

Description

Technical Field

[0001] Field This technology generally relates to the field of biomedical imaging, and more specifically to methods and systems for determining the vulnerability in the region of an aneurysm within a blood vessel and the resulting potential for expansion and rupture.

Background Art

[0002] Background Abdominal and thoracic aortic aneurysms are generally asymptomatic and slowly progressive. If left untreated, the aneurysm gradually enlarges and leads to rupture, an event with a 90% mortality rate.

[0003] The clinical management of aortic aneurysms depends on the assessment of the maximum aortic diameter as a marker of the risk of rupture. However, significant individual variations have been reported as evidence of the low predictability of the maximum diameter of the blood vessel.

[0004] <00000l8>Local hemodynamic forces are known to play an important role in the regulation of vascular function and in promoting local structural remodeling in response to long-term changes in flow. However, no clear insight into the local vulnerability of the aortic wall has yet been found.

Summary of the Invention

Problems to be Solved by the Invention

[0005] Summary The objective of this technology is to improve at least some of the disadvantages existing in the prior art. Embodiments of this technology can provide and / or expand the scope of approaches and / or methods for achieving the aims and objectives of this technology.

Means for Solving the Problems

[0006] Embodiments of this technology enable the estimation of the likelihood of in vivo rupture of an aneurysm within a blood vessel by using a 3D model of the vessel and computational fluid dynamics simulations to determine local characteristics and hemodynamic indicators. More specifically, this technology enables the evaluation of individual aortas based on parameters correlated with local weakening, dilation, and rupture of the vessel, and provides a rationale for clinical decisions by performing calculations based solely on images acquired by medical imaging devices.

[0007] Embodiments of this technology were developed based on the developers' understanding that local hemodynamic forces are known to play a crucial role in regulating vascular function and promoting local structural remodeling in response to long-term flow changes. Aortic dilation and rupture are associated with low wall shear stress (WSS) loading (less than 0.4 Pa) and intraluminal thrombus (ILT) accumulation.

[0008] More specifically, based on the above, the developers of this technology recognized that clear insights into the weakening of the aortic wall have yet to be found.

[0009] Since this technology relates to local mechanical properties and aortic function, the deformability of an aortic aneurysm is determined by in vivo strain measurement, and the state of aortic fragility is correlated with local deformation, local intraluminal thrombus (ILT) thickness, and hemodynamic indicators obtained by computational fluid dynamics (CFD) simulation, or alternatively, by 4D flow MRI data of a given patient. In one embodiment, the results are combined to obtain a regional rupture probability (RRP) that indicates the state of fragility and the probability of rupture in a given region.

[0010] Accordingly, embodiments of this technology relate to a method and system for determining the likelihood of regional rupture, which indicates a state of localized weakening of a blood vessel, based on parameters that correlate with localized weakening, dilation, and rupture of the blood vessel wall.

[0011] According to a broad aspect of the present technology, a computer implementation method is provided for determining the likelihood of rupture of at least one region of a given target blood vessel, the method being executable by a server, the method comprising the step of the server receiving a plurality of images of a given target blood vessel acquired by a medical imaging device, the method comprising the step of the server organizing the plurality of images into a polyphasic stack, where a given phase of the polyphasic stack represents the blood vessel at a given time in the cardiac cycle. The method comprises the steps of: generating a volume mesh of the lumen of a blood vessel and a surface mesh of the outer wall of the blood vessel using a multiphase stack by a server; calculating a thickness parameter based on the surface mesh of the lumen and the surface mesh of the outer wall by a server; determining local deformation in each phase of a multiphase stack by mapping the voxels of the outer wall surface mesh to a multiphase stack by a server; calculating a wall strain parameter indicating the maximum principal strain in the outer wall based on the local deformation in each phase by a server; generating a blood flow parameter based at least partially on the volume mesh of the lumen, wherein the blood flow parameter includes each set of intraluminal blood flow values ​​for a cardiac cycle; calculating a wall shear stress parameter indicating intraluminal wall shear disturbances based on the blood flow parameter by a server; and determining a rupture risk parameter of the blood vessel based on the thickness parameter, wall strain parameter, and wall shear stress parameter, wherein the rupture risk parameter indicates the state of fragility in at least one region of the blood vessel.

[0012] In some embodiments, the steps of generating a volume mesh of the lumen and generating a surface mesh of the outer wall include: generating a first geometric model of the lumen of the vessel and a second geometric model of the outer wall of the vessel by segmenting a multiphase stack by a server; and smoothing the first geometric model to obtain a volume mesh of the lumen and smoothing the second geometric model to obtain a surface mesh of the outer wall by a server.

[0013] In some embodiments of this method, at least one wall shear stress parameter includes time-averaged wall shear stress (TAWSS).

[0014] In some embodiments of this method, the step of generating blood flow parameters includes generating computational fluid dynamics (CFD) simulations of intraluminal blood flow to obtain each set of intraluminal blood flow values ​​for a given cardiac cycle.

[0015] In some embodiments of this method, the step of generating blood flow parameters includes performing a 4D flow MRI acquisition to obtain each set of intraluminal blood flow values ​​for the cardiac cycle.

[0016] In some embodiments of this method, the method further comprises a step of determining the local deformation in each phase of the surface mesh based on a multiphase stack and the surface mesh of the outer wall, prior to the step of calculating the wall strain parameters, and the step of calculating the wall strain parameters is based on the local deformation in each phase of the surface mesh.

[0017] In some embodiments of this method, the step of calculating the thickness parameter includes calculating the thickness of the intraluminal thrombus (ILT) based on the distance between the surface mesh of the outer wall and the surface mesh of the lumen.

[0018] In some embodiments of the present method, the method further comprises a step of receiving a collective-base thickness parameter, a collective-base wall strain parameter, and a collective-base wall shear stress parameter before the step of determining the burstability parameter, and the step of determining the burstability parameter is further based on the collective-base thickness parameter, the collective-base wall strain parameter, and the collective-base wall shear stress parameter.

[0019] In some embodiments of the method, the method further comprises, before the step of estimating the rupture likelihood parameter, defining, by the server, a plurality of patches on the blood vessel. The steps of calculating the thickness parameter, the wall strain parameter, and the wall shear stress parameter comprise calculating, using the plurality of patches, a patch average thickness parameter, a patch average wall strain parameter, and a patch average wall shear stress parameter, and the rupture likelihood parameter is based on the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter.

[0020] In some embodiments of the method, the steps of calculating the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter are further based on a population-based thickness parameter, a population-based wall strain parameter, and a population-based wall shear stress parameter.

[0021] In some embodiments of the method, the method further comprises determining, for each of the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter, respective distribution quartile values.

[0022] In some embodiments of the method, the method further comprises classifying each of the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter based on the respective distribution quartile values.

[0023] In some embodiments of the method, the rupture likelihood parameter is

[0024]

Number

[0025] In some embodiments of the method, each of the categories has a respective value from 1 to 4.

[0026] According to another broad aspect of the present technology, a system is provided for determining the likelihood of rupture indicating a state of local vulnerability of at least one region of a blood vessel of a given subject. The system includes a processor and a computer-readable storage medium connected to the processor and containing instructions. When the processor executes the instructions, it is configured to receive a plurality of images of a blood vessel of a given subject acquired by a medical imaging device. The processor is configured to compile the plurality of images into a multiphase stack, where a given phase of the multiphase stack represents the blood vessel at a given time in the cardiac cycle. The processor uses the multiphase stack to generate a volume mesh of the lumen of the blood vessel and a surface mesh of the outer wall of the blood, calculates a thickness parameter based on the volume mesh of the lumen and the surface mesh of the outer wall, determines local deformations in each phase of the multiphase stack by mapping voxels of the surface mesh of the outer wall to the multiphase stack, calculates a wall strain parameter indicating the maximum principal strain in the outer wall based on the local deformations in each phase, generates a blood flow parameter based at least in part on the volume mesh of the lumen, the blood flow parameter including a respective set of blood flow values in the lumen at a given instant, calculates a wall shear stress parameter indicating wall shear disturbance in the lumen based on the blood flow parameter, and determines a rupture likelihood parameter of the blood vessel based on the lumen thickness parameter, the wall strain parameter, and the wall shear stress parameter, the rupture likelihood parameter being configured to indicate the state of vulnerability of at least one region of the blood vessel.

[0027] In some embodiments, the generation of the volume mesh of the lumen and the generation of the surface mesh of the outer wall include the generation by a server of a first geometric model of the lumen of the blood vessel and a second geometric model of the outer wall of the blood vessel by segmenting the multiphase stack, the smoothing by the server of the first geometric model to obtain the volume mesh of the lumen, and the smoothing by the server of the second geometric model to obtain the surface mesh of the outer wall.

[0028] In some embodiments of the system, at least one wall shear stress parameter includes a time-averaged wall shear stress.

[0029] In some embodiments of the system, the generation of blood flow parameters includes generating computational fluid dynamics simulations of blood flow in the lumen to obtain each set of in-lumen blood flow values ​​for the cardiac cycle.

[0030] In some embodiments of the system, generating blood flow parameters involves performing a 4D flow MRI acquisition to obtain each set of intraluminal blood flow values ​​for the cardiac cycle.

[0031] In some embodiments of the system, the processor is further configured to determine the local deformation of the surface mesh in each phase of the multiphase stack based on the multiphase stack and the surface mesh of the outer wall, before calculating the wall strain parameters, and the calculation of the wall strain parameters is based on the local deformation in each phase of the surface mesh.

[0032] In some embodiments of the system, the thickness parameter is determined based on the distance between the surface mesh of the outer wall and the surface of the lumen.

[0033] In some embodiments of the system, the processor is further configured to receive collective-based thickness parameters, collective-based wall strain parameters, and collective-based wall shear stress parameters before determining the regional burstability parameters. The determination of the regional burstability parameters is further based on the collective-based thickness parameters, collective-based wall strain parameters, and collective-based wall shear stress parameters.

[0034] In some embodiments of the system, the processor is further configured to define multiple patches on the vessels before estimating the rupture probability parameter, and the step of calculating the thickness parameter, wall strain parameter, and wall shear stress parameter comprises the step of using the multiple patches to calculate the patch average thickness parameter, patch average wall strain parameter, and patch average wall shear stress parameter, and the region rupture probability parameter is based on the patch average thickness parameter, patch average wall strain parameter, and patch average wall shear stress parameter.

[0035] In some embodiments of the system, the calculation of the patch-average thickness parameter, patch-average wall strain parameter, and patch-average wall shear stress parameter is further based on the collective-based thickness parameter, collective-based wall strain parameter, and collective-based wall shear stress parameter.

[0036] In some embodiments of the system, the processor is further configured to determine the distribution quartiles for each of the patch-average thickness parameter, the patch-average wall strain parameter, and the patch-average wall shear stress parameter.

[0037] In some embodiments of the system, the processor is further configured to classify each of the patch-mean thickness parameter, patch-mean wall strain parameter, and patch-mean wall shear stress parameter based on their respective distribution quartile values.

[0038] In some embodiments of the system, the burstability parameter is,

[0039]

number

[0040] In some embodiments of the system, each category has a value ranging from 1 to 4.

[0041] definition In the context of this specification, “server” is a computer program that runs on appropriate hardware and receives requests (e.g., from electronic devices) over a network (e.g., a communication network), and executes or causes to execute such requests. The hardware may be a single physical computer or a single physical computer system, but is not required to be so for the purposes of this technology. In this context, the use of the term “server” is not intended to mean that all tasks (e.g., received instructions or requests) or any particular task are received, executed, or caused to be executed by the same server (i.e., the same software and / or hardware), but rather that any number of software elements or hardware devices may be involved in receiving / transmitting, executing, or causing to be executed any task or request, or the results of any task or request, and all of this software and hardware may be one server or more servers, both of which are included in the terms “at least one server” and “server.”

[0042] In the context of this specification, “electronic device” is any computing device or computer hardware capable of running software appropriate for the relevant task at hand. Therefore, some (non-exclusive) examples of electronic devices include general-purpose personal computers (desktops, laptops, netbooks, etc.), mobile computing devices, smartphones, and tablets, as well as network equipment such as routers, switches, and gateways. Note that in this context, an electronic device is not excluded from functioning as a server to other electronic devices. The use of the expression “electronic device” does not exclude the use of multiple electronic devices in receiving / transmitting, executing, or causing to execute any task or request, or the results of any task or request, or any step of any method described herein. In the context of this specification, “client device” refers to any of the set of end-user client electronic devices associated with a user, such as a personal computer, tablet, or smartphone.

[0043] In the context of this specification, the term “computer-readable storage medium” (also referred to as “storage medium” and “storage device”) is intended to include all non-temporary media of any nature and type, including but not limited to RAM, ROM, disks (such as CD-ROMs, DVDs, floppy disks, and hard drives), USB keys, solid-state drives, and tape drives. Multiple components, including two or more media components of the same type and / or two or more media components of different types, can be combined to form a computer information storage medium.

[0044] In the context of this specification, “database” is any structured collection of data, regardless of its particular structure, database management software, or computer hardware on which the data is stored, implemented, or otherwise made available. The database may reside on the same hardware as the processes that store or utilize the information stored in the database, or it may reside on separate hardware, such as a dedicated server or multiple servers.

[0045] In the context of this specification, the term “information” includes information of any nature or type that can be stored in a database. Therefore, information includes, but is not limited to, audiovisual works (images, videos, audio recordings, presentations, etc.), data (location data, numerical data, etc.), text (opinions, comments, questions, messages, etc.), documents, spreadsheets, lists of words, etc.

[0046] In the context of this specification, unless otherwise expressly provided, “indication” of an information element may be the information element itself, or a pointer, reference, link, or other indirect mechanism that enables the recipient of the instruction to find a network, memory, database, or other computer-readable media location from which the information element can be obtained. For example, a representation of a document may include the document itself (i.e., its contents), or a unique document descriptor that identifies a file with respect to a particular file system, or any other means that directs the recipient of the instruction to a network location, memory address, database table, or other location from which the file can be accessed. As those skilled in the art will recognize, the degree of precision required for such an instruction depends on the degree of any prior understanding of the interpretation to be given to the information exchanged between the sender and receiver of the instruction. For example, if it is understood prior to the communication between the sender and receiver that the instruction of an information element takes the form of a database key for an entry in a particular table in a given database containing the information element, then the transmission of the database key is all that is necessary to effectively convey the information element to the receiver, even if the information element itself is not transmitted between the sender and receiver of the instruction.

[0047] In the context of this specification, the term “communication network” is intended to include telecommunication networks such as computer networks, the Internet, telephone networks, Telex networks, and TCP / IP data networks (e.g., WAN networks, LAN networks, etc.). The term “communication network” includes wired networks or direct wired connections, as well as wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media, and any combination thereof.

[0048] In the context of this specification, the term “parameter” is intended to include numerical representations of the properties of a system. Parameters may be measured or calculated. Parameters may contain a single value or multiple values ​​and may be represented as vectors, matrices, and tensors. In non-restrictive examples, parameters may represent a single numerical value, a frequency distribution, and a probability distribution.

[0049] In the context of this specification, words such as “first,” “second,” and “third” are used as adjectives solely for the purpose of enabling distinction between the nouns they modify, and not for the purpose of describing any particular relationship between those nouns. Therefore, it should be understood that the use of terms such as “server” and “third server” is not intended to imply any particular order, type, chronological order, hierarchy, or (e.g.) ranking of servers / between servers, nor is their (in isolation) intended to imply that any “second server” must necessarily exist in any given situation. Furthermore, as explained in other contexts within this specification, references to “first” and “second” elements do not preclude the two elements from being the same, actual, real-world elements. Therefore, for example, in some cases, the “first” server and the “second” server may be the same software and / or hardware, while in other cases they may be different software and / or hardware.

[0050] Each embodiment of the present technology has at least one of the purposes and / or aspects described above, but does not necessarily have to have all of them. It should be understood that some embodiments of the present technology arising from an attempt to achieve the purposes described above may not satisfy these purposes and / or may satisfy other purposes not specifically enumerated herein.

[0051] Additional and / or alternative features, embodiments, and advantages of the implementation of this technology will become apparent from the following description, the accompanying drawings, and the accompanying claims.

[0052] Brief explanation of the drawing For a better understanding of this technology, as well as other aspects and further features, refer to the following description used in conjunction with the attached drawings. [Brief explanation of the drawing]

[0053] [Figure 1] A schematic diagram of an electronic device according to a non-limiting embodiment of this technology is shown. [Figure 2] A schematic diagram of a system according to a non-limiting embodiment of this technology is shown. [Figure 3] A schematic diagram of the procedure for determining the likelihood of regional rupture, performed within the system shown in Figure 2, is presented according to a non-limiting embodiment of this technology. [Figure 4A] This shows a 3D geometric model of the outer wall and lumen of an abdominal aortic aneurysm (AAA) with 24 patches, according to a non-limiting embodiment of the present technology. [Figure 4B] This is a left sagittal perspective view of a cardiac gating CT scan showing the site of an aortic rupture in the left posterolateral portion of the aortic wall, as confirmed during subsequent surgery, according to a non-limiting embodiment of the present technology. [Figure 5] This shows the computationally hydrodynamically predicted velocity contour on the longitudinal cross-section of AAA at different points in the cardiac cycle, according to a non-limiting embodiment of this technology. [Figure 6A]The distribution of TAWSS on the lumen surface and the regional mean distribution of TAWSS according to a non-limiting embodiment of this technology are shown. [Figure 6B] The distribution of ILT thickness on the exterior wall surface and the regional average distribution of ILT thickness according to a non-limiting embodiment of this technology are shown. [Figure 6C] The distribution of the maximum principal strain on the exterior wall surface and the regional average distribution of the maximum principal strain are shown according to a non-limiting embodiment of this technology. [Figure 7] This shows the regional rupture probability (RRP) calculated for a patch on the aortic wall with an estimated rupture site, according to one non-limiting embodiment of the technology. [Figure 8] A flowchart illustrating a method for determining the likelihood of regional rupture according to a non-limiting embodiment of this technology is shown. [Figure 9] A flowchart illustrating a method for determining the likelihood of regional rupture according to a non-limiting embodiment of this technology is shown. [Modes for carrying out the invention]

[0054] Detailed explanation The examples and conditional statements listed herein are primarily intended to help the reader understand the principles of the Art and are not intended to limit its scope to such specifically listed examples and conditions. Those skilled in the art will understand that various configurations not expressly described or illustrated herein can nevertheless embody the principles of the Art and fall within its spirit and scope.

[0055] Furthermore, to aid understanding, the following description can illustrate a relatively simplified implementation of the technology. As those skilled in the art will understand, various embodiments of the technology can be more complex.

[0056] In some cases, useful examples of modifications to the Art may be included. These are merely for the purpose of aiding understanding and are not intended to define or indicate the scope of the Art. These modifications are not an exhaustive list, and a person skilled in the art may nevertheless make other modifications while remaining within the scope of the Art. Furthermore, where no examples of modifications are provided, it should not be construed that modifications are impossible and / or that what is described is the only way to implement that element of the Art.

[0057] Furthermore, all descriptions in this specification listing the principles, aspects, and embodiments of the present art, as well as specific examples thereof, are intended to encompass both their structural and functional equivalents, whether they are currently known or to be developed in the future. Therefore, for example, any block diagram in this specification will be understood by those skilled in the art to represent a conceptual diagram of an exemplary circuit embodying the principles of the present art. Similarly, any flowchart, flow diagram, state transition diagram, pseudocode, etc., can be substantially represented in a computer-readable medium and will be understood to represent various processes that can be performed by such a computer or processor, whether such a computer or processor is explicitly indicated or not.

[0058] The functionality of the various elements shown in the figure, including any functional block labeled “Processor” or “Graphics Processing Unit,” may be provided using dedicated hardware, as well as hardware capable of running software in conjunction with appropriate software. Where provided by a processor, functionality may be provided by a single dedicated processor, a single shared processor, or multiple individual processors, some of which may be shared. In some non-limiting embodiments of this technology, the processor may be a general-purpose processor, such as a central processing unit (CPU), or a processor dedicated to a specific purpose, such as a graphics processing unit (GPU). Furthermore, the explicit use of the terms “Processor” or “Controller” should not be interpreted as referring exclusively to hardware capable of running software, and may implicitly include, but is not limited to, digital signal processor (DSP) hardware, network processors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), read-only memory (ROM), random-access memory (RAM), and non-volatile storage devices for storing software. Other conventional and / or custom hardware may also be included.

[0059] A software module, or a module that is simply implied to be software, may be represented herein as any combination of flowchart elements or other elements indicating the execution and / or text description of process steps. Such modules may be executed by hardware, either explicitly or implicitly indicated.

[0060] Using these fundamentals appropriately, we will now consider some non-limiting examples to illustrate various embodiments of this technology.

[0061] Referring to Figure 1, a schematic diagram of an electronic device 100 suitable for use in several non-limiting embodiments of the present technology is shown.

[0062] Electronic devices The electronic device 100 comprises various hardware components, including one or more single-core or multi-core processors, collectively represented by a processor 110, a graphics processing unit (GPU) 111, a solid-state drive 120, random-access memory 130, a display interface 140, and an input / output interface 150.

[0063] Communication between various components of the electronic device 100 may be enabled by one or more internal and / or external buses 160 (e.g., PCI bus, Universal Serial Bus, IEEE 1394 "Firewire" bus, SCSI bus, Serial-ATA bus, etc.) through which various hardware components are electronically coupled.

[0064] The input / output interface 150 may be coupled to the touchscreen 190 and / or one or more internal and / or external buses 160. The touchscreen 190 may be part of the display. In some embodiments, the touchscreen 190 is the display. The touchscreen 190 may also be called the screen 190. In the embodiment shown in Figure 1, the touchscreen 190 comprises touch hardware 194 (e.g., pressure-sensitive cells embedded in a layer of the display that enable detection of physical interaction between the user and the display) and a touch input / output controller 192 that enables communication with the display interface 140 and / or one or more internal and / or external buses 160. In some embodiments, the input / output interface 150 may be coupled to a keyboard (not shown), mouse (not shown), or trackpad (not shown) that allows the user to interact with an electronic device 100 in addition to or instead of the touchscreen 190.

[0065] According to an embodiment of this technology, the solid-state drive 120 stores program instructions that are loaded into the random-access memory 130 and are suitable for execution by the processor 110 and / or GPU 111 to estimate the likelihood of a given blood vessel rupture. For example, the program instructions may be part of a library or application.

[0066] The electronic device 100 may be implemented in the form of a server, a desktop computer, a laptop computer, a tablet, a smartphone, a personal digital assistant, or any other device that can be configured to implement the technology, as can be understood by those skilled in the art.

[0067] system Referring to Figure 2, a schematic diagram of System 200 is shown, which is suitable for implementing non-limiting embodiments of the Art. It should be clearly understood that System 200 shown is merely an exemplary embodiment of the Art. Therefore, the following description is intended solely to describe exemplary embodiments of the Art. This description does not define or indicate the scope of the Art. In some cases, useful examples of modifications to System 200 may also be described below. These are merely for the purpose of aiding understanding and again do not define or indicate the scope of the Art. These modifications are not an exhaustive list, and as those skilled in the art will understand, other modifications are likely possible. Furthermore, where this is not done (i.e., no examples of modifications are given), modifications should not be interpreted as impossible, and / or that what is described is the only way to implement that element of the Art. As those skilled in the art will understand, this is likely not the case. Furthermore, it should be understood that System 200 may, in certain cases, provide a simpler implementation of the Art, and in such cases, it is presented in this manner for the purpose of aiding understanding. As those skilled in the art will understand, various embodiments of the Art may be more complex.

[0068] The system 200 includes, in particular, a medical imaging device 210 associated with a workstation computer 215, and a server 230 connected via a communication network 220 through their respective communication links 225.

[0069] Medical devices Generally speaking, the medical imaging device 210 is configured to acquire multiple images of a given blood vessel at different points in time so that a representation of the blood vessel of a given object can subsequently be generated. In one embodiment, the medical imaging device 210 is configured to acquire an electrocardiogram (ECG) gate image.

[0070] The medical imaging device 210 may be any of the following: computed tomography (CT) scanner, magnetic resonance imaging (MRI) scanner, 3D ultrasound, etc. In some embodiments of this technology, the medical imaging device 210 may be one or more medical imaging devices, such as one or more of the computed tomography (CT) scanner, magnetic resonance imaging (MRI) scanner, 3D ultrasound, etc.

[0071] The medical imaging device 210 may be configured with specific parameters for acquiring multiple images.

[0072] As a non-limiting example, in an embodiment in which the medical imaging device 210 is implemented as a CT scanner, a CT protocol can be used that includes a preoperative retrospective gate-type multi-detector CT (MDCT - 64-row multi-slice CT scanner) using variable-dose radiation to capture the RR interval.

[0073] As another non-limiting example, in an embodiment in which the medical imaging device 210 is implemented as an MRI scanner, an MR protocol may be used, which may comprise steady-state T2-weighted high-speed field echo (TE=2.6ms, TR=5.2ms, flip angle 110 degrees, fat suppression (SPIR), echo time 50ms, maximum 25 cardiac phases 2, matrix 256×256, acquired voxel MPS 1.56 / 1.56 / 3.00mm and reconstructed voxel MPS 0.78 / 0.78 / 1.5).

[0074] The medical imaging device 210 includes or is connected to a workstation computer 215.

[0075] Workstation computer The workstation computer 215 is configured to receive and process multiple images from the medical imaging device 210. The workstation computer 215 can receive raw format images and perform tomographic reconstruction using known algorithms and software. Implementations of the workstation computer 215 are known in the art. The workstation computer 215 may be implemented as an electronic device 100, or it may comprise its components such as a processor 110, a graphics processing unit (GPU) 111, a solid-state drive 120, random access memory 130, a display interface 140, and an input / output interface 150.

[0076] In one embodiment, the workstation computer 215 can be integrated into the medical imaging device 210.

[0077] In one embodiment, the workstation computer 215 is configured in accordance with the Medical Digital Imaging and Communication (DICOM) standard for the communication and management of medical imaging information and related data.

[0078] In one embodiment, the workstation computer 215 can store images in a database (not shown).

[0079] The workstation computer 215 is connected to the server 230 via a communication network 220 through a communication link (unnumbered).

[0080] server Generally speaking, server 230 is configured to (i) receive multiple images and process them into a multiphase stack, (ii) generate a 3D geometric model of the lumen and outer wall of a blood vessel from the multiphase stack, (iii) smooth and mesh the 3D geometric model to obtain a mesh of the lumen and outer wall, (iv) calculate thickness parameters based on the mesh of the lumen and outer wall, (v) generate a computational fluid dynamics (CFD) simulation using the lumen mesh to calculate wall shear stress parameters, or alternatively, calculate wall shear stress parameters from 4D flow MRI data of a given patient, (vi) track and map the mesh of the outer wall to calculate wall strain parameters, and (viii) determine regional rupture potential parameters based on the thickness parameters, wall strain parameters, and wall shear stress parameters.

[0081] The method by which server 230 is configured in this way is described in more detail below.

[0082] Server 230 can be implemented as a conventional computer server and may comprise some or all of the components of the electronic device 100 shown in Figure 1. In one embodiment of the present technology, Server 230 can be implemented as a Dell® PowerEdge® Server running the Microsoft® Windows® Server® operating system. Needless to say, Server 230 can be implemented with any other suitable hardware and / or software and / or firmware or a combination thereof. In the non-limiting embodiments of the present technology shown, Server 230 is a single server. In alternative non-limiting embodiments of the present technology, the functions of Server 230 may be distributed and implemented through multiple servers (not shown).

[0083] Implementations of server 230 are well known to those skilled in the art. However, in short, server 230 includes a communication interface (not shown) which is structured and configured to communicate with various entities (e.g., workstation computer 215 and other devices potentially connected to the network) via a communication network 220. Server 230 further includes at least one computer processor (e.g., processor 110 of electronic device 100) which is operably connected to the communication interface and is structured and configured to perform various processes described herein.

[0084] Communication network In some embodiments of this technology, the communication network 220 is the Internet. In alternative, non-limiting embodiments, the communication network 240 can be implemented as any suitable local area network (LAN), wide area network (WAN), private communication network, etc. It should be clearly understood that the implementation forms of the communication network 250 are for illustrative purposes only. How the communication links 225 (not separately numbered) between the workstation computer 215 and / or the server 230 and / or other electronic devices (not shown) and the communication network 220 are implemented depends, among other things, on how each of the medical imaging device 210, the workstation computer 215, and the server 230 is implemented.

[0085] Procedure for determining the likelihood of regional rupture Next, referring to Figure 3, a schematic diagram of the regional rupture potential (RRP) determination procedure 300 according to a non-limiting embodiment of the present technology is shown.

[0086] The RRP determination procedure 300 is performed within the system shown in Figure 2. In one embodiment, the RRP determination procedure 300 may be performed by the server 230. Some steps of the RRP determination procedure 300 may be performed in parallel by the server 230 or an electronic device (such as a workstation computer 215).

[0087] The RRP determination procedure 300 includes, in particular, an image acquisition procedure 310, an image segmentation procedure 320, a smoothing and volume meshing procedure 330, a smoothing and surface meshing procedure 335, an ILT thickness calculation procedure 340, a CFD simulation procedure 350 or a 4D flow MRI acquisition procedure 355, a lumen WSS calculation procedure 360, a motion tracking and mapping procedure 370, a patch application and classification procedure 390, and an RRP calculation procedure 400.

[0088] Image acquisition The image acquisition procedure 310 is performed by the medical imaging device 210 and the workstation computer 215.

[0089] During the image acquisition procedure 310, multiple images of the blood vessels, such as the aorta, of a given patient are received. These multiple images can be received from the workstation computer 215 or directly from the medical imaging device 210.

[0090] In one embodiment where the medical imaging device 210 is a CT scanner, the CT protocol for acquiring CT images may include a preoperative retrospective gated MDCT (64-row multislice CT scanner) using variable-dose radiation to capture the RR interval. In one embodiment where the medical imaging device 210 is an MRI scanner, the MR protocol may include steady-state T2-weighted high-speed field echo (TE=2.6ms, TR=5.2ms, flip angle 110 degrees, fat suppression (SPIR), echo time 50ms, maximum 25 cardiac phases 2, matrix 256×256, acquired voxel MPS 1.56 / 1.56 / 3.00mm and reconstructed voxel MPS 0.78 / 0.78 / 1.5), or similar cine acquisition of a portion of the aorta under study, axial slices.

[0091] The image acquisition procedure 310 organizes multiple images into a multiphase stack. In one embodiment, the multiple images are organized stepwise according to a medical digital imaging and communication (DICOM) stack whose implementation is known in the art.

[0092] In one embodiment, each phase of a multiphase stack can correspond to a time instance of a given patient's cardiac cycle.

[0093] Image acquisition procedure 310 outputs a multiphase stack. Image segmentation The image segmentation procedure 320 receives an image as input that corresponds to one phase of a multiphase stack.

[0094] The image segmentation procedure 320 uses segmentation techniques known to those skilled in the art to identify pixels or voxels belonging to an object such as a blood vessel and / or to arrange pixels or voxels that form the boundaries of the blood vessel in order to generate a 3D geometric model of at least a portion of the blood vessel. The image segmentation procedure 320 may segment the stack based on one or more of pixel intensity, texture, and other attributes using deformable models and techniques such as, but not limited to, low-level segmentation (thresholding, region growth, etc.), model-based segmentation (multispectral, feature mapping, dynamic programming, counter tracking), statistical techniques, fuzzy techniques, and other techniques known in the art.

[0095] The image segmentation procedure 320 generates (i) a 3D geometric model of the lumen of a blood vessel and (ii) a 3D geometric model of the outer wall of a blood vessel, based on a multiphase stack. In one embodiment, the image segmentation procedure 320 can generate a 3D geometric model of the lumen and a 3D geometric model of the outer wall, based on a first phase of the multiphase stack, corresponding to a given time in the cardiac cycle identified as phase 0.

[0096] Referring briefly to Figure 4A, a 3D geometric model of the lumen and outer wall 420 of an infrarenal abdominal aortic aneurysm is shown according to a non-limiting embodiment of the present technology.

[0097] Returning to Figure 3, the image segmentation procedure 320 outputs a 3D geometric model of the lumen and a 3D geometric model of the outer wall.

[0098] Smoothing and volume meshing The smoothing and volume meshing procedure 330 receives a three-dimensional geometric model of the lumen as input.

[0099] Generally speaking, the smoothing and volume meshing procedure 330 filters or denoises the 3D geometric model of the lumen to create its discrete representation, including vertices, edges, and faces.

[0100] The smoothing and volume meshing procedure 330 smooths the 3D geometric model of the lumen and generates a volume mesh of the 3D geometric model of the lumen.

[0101] In one embodiment, the smoothing and meshing procedure 330 generates a volume mesh of the 3D geometric model of the lumen using a top-down approach with an octree method, where a first coarse mesh is defined to enclose the geometric shape, and is then spatially subdivided into smaller elements by swapping and smoothing to achieve the desired mesh quality, with nodes fitting to the geometric surface. To improve the accuracy of the results in this region of interest, a prism boundary layer (finer mesh) is included in the geometric walls. As a non-limiting example, the volume mesh of the lumen may have about 4 million tetrahedral elements.

[0102] The smoothing and volume meshing procedure 330 outputs a volume mesh of the 3D geometric model of the lumen.

[0103] Smoothing and surface meshing The smoothing and surface meshing procedure 335 receives a 3D geometric model of the exterior wall as input. The smoothing and surface meshing procedure 335 filters or denoises the 3D geometric model of the exterior wall to create a discrete representation of it, including vertices, edges, and faces.

[0104] The smoothing and surface meshing procedure 335 smooths the 3D geometric model of the exterior wall and generates a surface mesh of the 3D geometric model of the exterior wall. In one embodiment, the surface mesh of the 3D geometric model of the exterior wall is in the form of small triangular elements or discretized geometric shapes of shells.

[0105] In one embodiment, the smoothing and surface meshing procedure 335 reduces the number of shells using a Taubin filter and / or secondary edge collapse decimation for smoothing. As a non-limiting example, the surface mesh of the outer wall may have about 4,000 triangular shell elements.

[0106] In one embodiment, the resolution of the surface mesh of the 3D geometric model of the exterior wall is at least the same as the pixel size. In one embodiment, the surface mesh of the exterior wall is a deformable mesh.

[0107] The smoothing and surface meshing procedure 335 outputs a surface mesh of the 3D geometric model of the exterior wall.

[0108] Thickness calculation The thickness calculation procedure 340 receives the surface mesh of the outer wall and the volume mesh of the lumen as input.

[0109] The thickness calculation procedure 340 determines the thickness parameters based on the surface mesh of the outer wall and the volume mesh of the lumen.

[0110] Generally speaking, the thickness parameter includes intraluminal thrombus (ILT) thickness measurements. Thickness calculation procedure 340 determines the ILT thickness based on the distance between the outer wall surface mesh and the lumen surface mesh. Thickness calculation procedure 340 obtains the lumen surface mesh based on the volume mesh of the lumen. The thickness parameter is the spatial distribution of ILT thickness measurements.

[0111] In one embodiment, if there is sufficient resolution to distinguish the interface between the ILT surface and the inner surface of the wall, the thickness calculation procedure 340 determines the thickness parameter based on the distance between the inner surface of the wall and the outer surface of the wall. In one embodiment, the thickness calculation procedure 340 can determine the ILT thickness only if the ILT is present. Therefore, in one embodiment, the thickness parameter may include the ILT thickness and / or the wall thickness.

[0112] In one embodiment, the surface mesh of the lumen is obtained from the volume mesh of the lumen. It should be noted that the number of ILT thickness values ​​for the thickness parameter is not limited and depends on how the surface mesh of the outer wall and the surface mesh of the lumen are generated; that is, the ILT thickness can be measured between each element of the surface mesh of the outer wall and the surface mesh of the lumen, or between a subset of elements of the surface mesh of the outer wall and the surface mesh of the lumen.

[0113] Referring briefly to Figure 6B, the distribution of millimeter (mm) ILT thickness measurements 630 and the regional average distribution of ILT thickness measurements 620 on the exterior wall surface are shown according to a non-limiting embodiment of the present technology.

[0114] Returning to Figure 3, the thickness calculation procedure 340 outputs the thickness parameters. Motion tracking and mapping The motion tracking and mapping procedure 370 receives a multiphase stack of images of the surface mesh and all phases of the 3D geometric model of the exterior wall as input.

[0115] In one embodiment, the motion tracking and mapping procedure 370 is performed using MATLAB® (MathWorks, Inc., Natick, Massachusetts, USA) based software Virtual Touch Aortic Aneurysm (ViTAA TM This is implemented by [company name], and its embodiments are described in International Publication No. 2018 / 068153.

[0116] The motion tracking and mapping procedure 370 uploads the surface mesh of the 3D geometric model of the exterior wall created for the first phase onto the multiphase stack.

[0117] The motion tracking and mapping procedure 370 uses an optical flow (OF) algorithm to map the position of each voxel in the surface mesh of the first phase to all subsequent phases. The positions of all voxels in different phases are mapped back to the surface mesh of the first phase, and the position of each node in the geometric shape of the first phase is associated with the corresponding node position in all subsequent phases. Thus, nodal displacements, i.e., different phases, throughout the entire cardiac cycle can be determined.

[0118] In one embodiment, the motion tracking and mapping procedure 370 follows the displacement of an object between images captured in subsequent time steps by detecting grayscale features corresponding to the object and calculating its velocity.

[0119] As a non-limiting example, in the case of a CT image, the node corresponding to the first phase has the node positions corresponding to all subsequent phases.

[0120] From the map of displaced nodes, motion tracking and mapping procedure 370 generates a deformed mesh in all phases. The positions of all voxels in different phases are mapped back to the mesh of the first phase so that the positions of each node in the geometric shape in the first phase are associated with the corresponding node positions in all subsequent phases.

[0121] In other words, the mesh generated from 320 and 335 is used to generate a deformed mesh in all phases by updating the coordinate position for each node of the mesh.

[0122] The motion tracking and mapping procedure 370 outputs the local deformation in each phase of the surface mesh.

[0123] Maximum Distortion Calculation The maximum strain calculation procedure 380 receives local deformations in each phase of the surface mesh and in the surface mesh of the first phase as input.

[0124] The maximum strain calculation procedure 380 uses continuous mechanics techniques to calculate in vivo strain based on the local kinematics in each phase of the surface mesh.

[0125]

number

[0126]

number

[0127] In one embodiment, the maximum strain calculation procedure 380 first calculates the deformation gradient, then calculates the Cauchy-Green deformation tensor from it, and then calculates the Green-Lagrange strain. Next, the maximum strain calculation procedure 380 calculates the principal strains as eigenvalues ​​of the Green-Lagrange strains to generate wall strain parameters. The wall strain parameters are the distribution of the maximum principal strain measurements.

[0128] Note that the number of maximum principal strain values ​​for the wall strain parameter is not limited and depends on the number of displaced nodes tracked on the surface mesh.

[0129] Referring briefly to Figure 6C, the distribution of maximum principal strain on the exterior wall surface 640 and the regional average distribution of maximum principal strain on the exterior wall surface 650 are shown according to a non-limiting embodiment of the present technology.

[0130] Returning to Figure 3, the maximum strain calculation procedure 380 outputs wall strain parameters, which represent the relative displacement in the exterior wall region.

[0131] Computational fluid dynamics (CFD) simulation Computational fluid dynamics (CFD) simulation procedure 350 receives a volume mesh of a 3D geometric model of a lumen as input.

[0132] Generally speaking, CFD simulation procedure 350 simulates blood flow in an arterial shape by using the finite volume method for the numerical implementation of the Navier-Stokes equations that describe fluid flow. CFD simulation procedure 350 uses the finite volume method to solve the discretized form of the Navier-Stokes equations over all finite volume elements within the domain. Because the governing equations are nonlinearly coupled, CFD simulation procedure 350 applies an iterative method to simulate blood flow to obtain a converged numerical solution. It should be noted that in alternative embodiments of this technique, the finite element method or the finite difference method can be used instead of the finite volume method to obtain the same CFD parameters.

[0133] CFD simulation procedure 350 uses a quadratic implicit transient formula and predefined CFD parameters, including, in particular, boundary conditions, viscosity, density, and time step.

[0134] Generally, a computational domain or a discretized geometric shape is defined. CFD simulation procedure 350 applies boundary conditions to the domain inlet, outlet, and walls to solve the Navier-Stokes equations describing fluid dynamics or blood flow. It should be noted that realistic boundary conditions are important for reliability. That is, using unrealistic boundary conditions for the aorta may make it possible to reach a solution, but the results are obviously unreliable because they do not represent realistic fluid dynamics. In one embodiment, the boundary conditions are based on an atlas of velocity boundary conditions obtained from experimental MR measurements and adapted to each individual geometry.

[0135] In one embodiment, the CFD simulation procedure 350 defines the velocity inlet by applying velocity information to the inlet surface of the computational domain. A constant velocity value generates a steady-state simulation while applying the velocity profile for the time required for a time-dependent simulation (transient or transient simulation). In one embodiment, if the boundary condition at the outlet is defined as the outflow boundary condition, the flow rate at the outlet is expressed as a percentage of the inlet flow rate, which may be 50% in each iliac artery, as an unrestricted example.

[0136] CFD simulation procedure 350 uses a rheological model of blood, i.e., Newtonian or non-Newtonian behavior, by using specific viscosity and density that are constant for Newtonian fluids and dependent on the shear rate for non-Newtonian fluids.

[0137] In one embodiment, the rheological model assumes that blood is an isotropic, incompressible Newtonian fluid with a predetermined constant density (e.g., 1060 kg / m³) and dynamic viscosity (e.g., 0.00319 Pa·s). The arterial walls are assumed to be rigid, and a no-slip condition is applied at the fluid interface.

[0138] Generally speaking, the assumption of blood behavior as a Newtonian fluid (i.e., shear stress linearly proportional to shear rate due to a constant viscosity average) is well accepted in larger cardiovascular regions characterized by increasing shear rates. However, at lower shear rates, blood behavior deviates from Newtonian fluid behavior, assuming shear-thinning properties with viscosity varying with shear rate. Non-Newtonian blood behavior can be simulated by using different rheological models (e.g., Ballyk model, Carreau-Yasuda model) to explain the shear rate dependence of blood viscosity.

[0139] In one embodiment, arterial wall motion can be incorporated by fluid-structure interaction (FSI) simulation, where the effects of wall dynamics are assumed to influence the fluid, and vice versa. FSI simulations require assumptions about wall material properties described by constitutive models. While various constitutive models are available, they are not always accurate in capturing the inter-patient and intra-patient heterogeneity characterizing the aortic wall, particularly in the presence of pathological aneurysms. Furthermore, simulations involving moving arterial walls require the definition of constraints to reproduce the effects of surrounding organs with unknown material properties and to limit wall motion. Thus, the assumptions required to simulate moving arterial walls may lead to inaccuracies in the results of CFD simulation procedure 350.

[0140] In one embodiment, the CFD simulation can define a moving boundary CFD simulation in which the effects of wall dynamics are incorporated by imposing wall motion on the simulation using the positions of the wall nodal mesh obtained from a wall strain algorithm.

[0141] CFD simulation procedure 350 uses a time step that defines the time discretization of the equation to be solved, in the case of transient or transient simulations.

[0142] It should be noted that the accuracy of the results from CFD simulation procedure 350 is affected by spatial and temporal discretization, i.e., the mesh element size and time step size. Coarser meshes and larger time step sizes introduce instability and ultimately lead to less accurate results, although they still allow the simulation to be performed. It should be noted that accuracy can be improved by using a volume mesh for the lumen that includes a prism boundary layer in the near-wall region.

[0143] In one embodiment, a mesh sensitivity analysis may be performed during the CFD simulation procedure 350 to identify appropriate mesh improvements to obtain optimal results. The appropriate mesh improvements are applied during the smoothing and volume meshing procedure 330.

[0144] CFD simulation procedure 350 outputs blood flow parameters, which include sets of flow values ​​such as velocity and pressure at each node of the lumen mesh relative to the cardiac cycle.

[0145] Referring briefly to Figure 5, the predicted values ​​of the blood flow parameter 500 after a CFD simulation procedure for the longitudinal cross-section of AAA at different times in the cardiac cycle, according to a non-limiting embodiment of the present technology.

[0146] The blood flow parameter 500 is the form of a velocity contour expressed in meters per second (m / s). The blood flow parameter 500 comprises a first velocity value set 520 during systolic acceleration, a second velocity value set 540 during systolic peak, a third velocity value set 560 during systolic deceleration, and a fourth velocity value set 580 during diastole.

[0147] In one embodiment, the CFD simulation procedure 350 is replaced by calculating blood flow parameters using the 4D flow MRI acquisition procedure 355. The 4D flow MRI acquisition procedure 355 receives a volume mesh of a 3D geometric model of the lumen as input. The 4D flow MRI acquisition procedure 355 uses the volume mesh of the 3D geometric model of the lumen to identify the volume portion where the velocity field is measured.

[0148] In one embodiment, the 4D flow MRI acquisition procedure 355 includes the acquisition of ECG trigger and respiratory trigger data. In one embodiment, the data may be acquired by a medical imaging device 210 when performed as an MRI, or by another MRI (not shown).

[0149] As a non-limiting example, the 4D flow MRI acquisition procedure 355 can use the following acquisition parameters: repetition time = 4.8 ± 0.1 ms, echo time = 2.4 ± 0.1 ms, isotropic in-plane pixel spacing = 2.2 ± 0.2 [1.7~2.9] mm, slice thickness = 2.7 ± 0.3 [2.2~3.5 mm], 2k spatial segments per cardiac time frame, temporal resolution = 38.8 ± 1.2 [36.0-41.6] ms, reception bandwidth = 445-460 Hz / pixel.

[0150] While not limited, several sizes of images can be taken through different reconstruction processes, such as parallel imaging, to perform reconstruction. Images of all spatial axes can then be computed. Phase offset errors can also be corrected during preprocessing. Analysis can then be performed, and during the analysis phase, the dataset is first checked for quality for subsequent visualization and quantitative analysis. Blood flow parameters can then be determined by obtaining the velocity field in the volume corresponding to the volume mesh of the 3D geometric model of the lumen.

[0151] Procedure 355 for acquiring 4D flow MRI outputs blood flow parameters, which include each set of intraluminal flow values ​​for the cardiac cycle.

[0152] Luminous wall shear stress (WSS) calculation Returning to Figure 3, the luminal WSS calculation procedure 360 ​​receives the blood flow parameters calculated during the CFD simulation procedure 350 as input. In one embodiment, the luminal WSS calculation procedure 360 ​​receives the blood flow parameters calculated during the 4D flow MRI acquisition procedure 355 as input.

[0153]

number

[0154] In one embodiment, the lumen WSS calculation procedure 360 ​​derives a total shear stress-based hemodynamic wall descriptor (HWD) from a set of flow values. The HWD incorporates the temporal changes in the magnitude and direction of the WSS vector.

[0155] Luminous WSS calculation procedure 360 ​​calculates the wall shear stress parameter for each node at the lumen surface based on at least one HWD.

[0156] In one embodiment, the lumen WSS calculation procedure 360 ​​determines the first HWD or time-averaged wall shear stress (TAWSS) using equation (1).

[0157]

number

[0158] Here, T is the time interval at which the WSS vector values ​​are measured. Generally, low TAWSS values ​​(less than 0.4 N / m2) are known to stimulate a pro-atherosclerotic endothelial phenotype, characterized by intima-media thickness. Moderate TAWSS values ​​(greater than 1.5 N / m2) induce inactivity and atherosclerotic gene expression profiles. Higher TAWSS values ​​(greater than 10 ÷ 15 N / m2, associated from 25 ÷ 45 N / m2) can lead to endothelial trauma and hemolysis.

[0159] Referring briefly to Figure 5A, the distribution of TAWSS600 and the regional mean distribution of TAWSS610 on the lumen surface are shown in Pascals (Pa) according to a non-limiting embodiment of this technology.

[0160] Returning to Figure 3, in one embodiment, the lumen WSS calculation procedure 360 ​​determines a second HWD or vibration shear index (OSI) by using equation (2).

[0161]

number

[0162] OSI is used to identify areas on the vessel wall that experience highly oscillating WSS directions during the cardiac cycle. Low OSI values ​​occur in areas where flow turbulence is minimal, while high OSI values ​​(up to 0.5) highlight areas where instantaneous WSS deviates from the main flow direction for most of the cardiac cycle, inducing disordered endothelial alignment.

[0163] In one embodiment, the lumen WSS calculation procedure 360 ​​determines a third HWD or relative residence time (RRT) using formula (3).

[0164]

number

[0165] Note that RRT is inversely proportional to the magnitude of the time-averaged WSS vector (i.e., the term in the numerator of the OSI equation). The residence time of particles near the wall is proportional to the combination of OSI and TAWSS. A high RRT indicates low vibrational shear stress.

[0166] The lumen WSS calculation procedure 360 ​​determines the wall shear stress parameters based on TAWSS. In an alternative embodiment of this technique, the lumen WSS calculation procedure 360 ​​may further determine the wall shear stress parameters based on at least one of TAWSS, OSI, and RRT.

[0167] The wall shear stress parameter indicates the turbulence of the flow within the lumen, or the stress component that lies coplane with the region of the lumen cross-section.

[0168] Patch application and classification The patch application and classification procedure 390 receives thickness parameters, wall strain parameters, and wall shear stress parameters as input.

[0169] The patch application and classification procedure 390 defines multiple patches on a vascular geometry comprising an outer wall and lumen, perpendicular to the centerline of the lumen, and determines the patch mean distribution for each of the thickness parameter, wall strain parameter, and wall shear stress parameter.

[0170] As a non-limiting example, the patch application and classification procedure 390 can define 24 patches on the vascular geometry of the outer wall and lumen, and calculate the wall thickness, wall strain, and wall shear stress values ​​for each of the 24 patches. As a non-limiting example, if a given patch corresponds to 40 elements of a mesh in which 40 wall shear stress values ​​exist for the wall shear stress parameter, the patch application and classification procedure 390 can calculate the average of the 40 wall shear stress values ​​for the given patch.

[0171] In one embodiment, the patching and classification procedure 390 receives collective-based values ​​for each of the thickness parameter, wall strain parameter, and wall shear stress parameter.

[0172] In one embodiment, the patch application and classification procedure 390 determines the distributional quartiles of the patch mean distribution of ILT thickness, wall strain, and TAWSS. In one embodiment, the distributional quartiles are determined for each patch mean distribution of patient-specific and population-based distributions.

[0173] In one embodiment, the patch application and classification procedure 390 classifies the ILT thickness, wall strain, and TAWSS for each patch based on the determined distribution quartiles.

[0174] In one embodiment, the patch application and classification procedure 390 assigns categories 1 to 4 to the values ​​of the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter.

[0175] As a non-limiting example, for the thickness parameter, wall strain parameter, and wall shear stress parameter shown in Figures 6A to 6C, respectively, the patch application and classification procedure 390 obtains the values ​​and categories detailed in Table I for each patch of the patch average thickness parameter, patch average wall strain parameter, and patch average wall shear stress parameter.

[0176] [Table 1]

[0177] The patch application and classification procedure 390 outputs the following categories for each patch: patch-average TAWSS, patch-average thickness parameter, and patch-average wall strain parameter.

[0178] Regional rupture potential (RRP) calculation The RRP calculation procedure 400 receives the following categories as input: patch average thickness parameter, patch average wall strain parameter, and patch average wall shear stress parameter.

[0179] In another embodiment, the RRP calculation procedure 400 receives a thickness parameter, a wall strain parameter, and a wall shear stress parameter as input and determines the RRP parameters. In yet another embodiment, the RRP calculation procedure 400 receives a patch-average thickness parameter, a patch-average wall strain parameter, and a patch-average wall shear stress parameter and determines the RRP parameters.

[0180] In one embodiment, the RRP calculation procedure 400 determines a regional rupture probability (RRP) parameter. The RRP parameter can represent the state of regional fragility and the probability of rupture in a region or set of regions of the vascular wall. The RRP parameter takes into account various factors for adverse reconstruction and degeneration of the vascular wall, including but not limited to the aortic wall, and indicates the local state of vascular fragility and the resulting likelihood of dilation and rupture.

[0181] In one embodiment, the RPP parameters correspond to the weighted sum of the inputs. For example, the RRP calculation procedure 400 can determine the RRP parameters using equation (4).

[0182]

number

[0183]

number

[0184] In embodiments where no thrombus is present in a particular artery, the ILT thickness of the thickness parameter can have a value of 0 anywhere, and the RRP calculation procedure 400 determines the RRP based on the wall shear stress parameter and the wall strain parameter, since not all aneurysms exhibit ILT formation.

[0185] In one embodiment, the RRP calculation procedure 400 determines the RRP parameters for patient-specific and population-based distributional quartiles. In one embodiment, the RRP calculation procedure 400 weights the contributions of patient-specific and population-based distributional quartiles to obtain a final RRP parameter estimate. In one embodiment, the RRP calculation procedure 400 accesses a MLA trained to determine the RRP parameters based on clinical data and previously calculated RRP parameters.

[0186] The RRP calculation procedure 400 outputs RRP parameters, which include the rupture probability of each patch or region defined during the patch application and classification procedure 390. In one embodiment, the RRP parameters may be in the form of percentages ranging from 0% (low probability of rupture) to 100% (very high probability of rupture), indicating the likelihood of each patch expanding and rupturing.

[0187] In one embodiment, the RRP calculation procedure 400 outputs RRP parameters having a 3D geometric model of the blood vessels on the display screen of an electronic device, such as the display interface 140 of the electronic device. In one embodiment, the RRP value in the RRP parameters may be determined using a predetermined threshold.

[0188] Referring to Figure 7, RRP parameters 700 are shown, including the RRP value calculated for a patch on the aortic wall surface having an estimated rupture site 720 located at patch LP5, according to a non-limiting embodiment of the present technology.

[0189] Method explanation Figure 8 shows a flowchart of a method 800 for estimating the likelihood of regional rupture of a blood vessel in a given patient, according to a non-limiting embodiment of the present technology.

[0190] Method 800 is performed by a computer machine. For example, Method 800 may be performed by a server 230. In one embodiment, the server 230 comprises a processor 110 and a non-temporary computer-readable storage medium such as a solid-state drive 120 and / or random-access memory 130 for storing computer-readable instructions. The processor 110 is configured to perform Method 800 when it has executed a computer-readable instruction.

[0191] It should be noted that method 800 may be performed by multiple electronic devices. Method 800 begins in step 802.

[0192] Step 802: Receive multiple images of blood vessels. In step 802, the server 230 receives multiple images of a given blood vessel from the workstation computer 215 or the medical imaging device 210.

[0193] In one embodiment, a workstation computer 215 receives multiple images from a medical imaging device 210.

[0194] Method 800 proceeds to step 804. Step 804: Organize multiple images into a polymorphic stack. In step 804, the server 230 organizes multiple images into a polyphase stack. In one embodiment, the workstation computer 215 can organize multiple images into a polyphase stack and transmit the polyphase stack to the server 230. A given phase of the polyphase stack represents a blood vessel at a given time in the cardiac cycle.

[0195] Method 800 proceeds to step 806. Step 806: Generate a first geometric model of the lumen and a second geometric model of the outer wall of the blood vessel by segmenting the multiphase stack. In step 806, the server 230 generates a first 3D geometric model of the vascular lumen and a second 3D geometric model of the vascular wall by segmenting the multiphase stack.

[0196] In one embodiment, a first 3D geometric model of the lumen of a blood vessel and a second 3D geometric model of the outer wall of a blood vessel are generated based on a first phase of a multiphase stack, corresponding to the time of the cardiac cycle, which is identified as phase 0.

[0197] Method 800 proceeds to step 806. Step 808: Smooth and discretize the first geometric model to obtain the volume mesh of the lumen, and smooth and discretize the second geometric model to obtain the surface mesh of the outer wall. In step 808, the server 230 smooths the first geometric model or 3D geometric model of the lumen to obtain a volume mesh of the 3D geometric model of the lumen. In one embodiment, the server 230 generates the volume mesh of the 3D geometric model of the lumen using a top-down method with an octree.

[0198] Server 230 smooths the 3D geometric model of the exterior wall to obtain the surface mesh of the 3D geometric model of the exterior wall. In one embodiment, the surface mesh of the 3D geometric model of the exterior wall is in the form of a discretized geometric shape of small triangular elements.

[0199] It should be noted that the smoothing of the first geometric model for obtaining the volume mesh of the lumen and the smoothing of the second geometric model for obtaining the surface mesh of the outer wall may be performed in parallel or sequentially.

[0200] Method 800 proceeds to step 810. Step 810: Calculate the thickness parameter based on the volume and surface mesh. In step 808, the server 230 calculates the thickness parameter based on the outer wall surface mesh and the lumen volume mesh. In one embodiment, the server 230 first determines the lumen surface mesh based on the lumen volume mesh. In one embodiment, the thickness parameter includes intraluminal thrombus (ILT) thickness measurements. The server 230 determines the ILT thickness based on the distance between the outer wall surface mesh and the lumen surface mesh. In one embodiment, if there is sufficient resolution to distinguish the interface between the ILT surface and the inner surface of the wall, the server 230 determines the thickness parameter based on the distance between the inner surface of the wall and the outer surface of the wall.

[0201] It should be noted that step 810 may be performed at any time after step 806 and before step 818.

[0202] Method 800 proceeds to step 812. Step 812: Determine the local deformation in each phase of the multiphase stack by mapping the voxels of the surface mesh to the multiphase stack. In step 810, the server 230 determines the local deformation in each phase of the multiphase stack by mapping the voxels of the exterior wall surface mesh to the multiphase stack. In one embodiment, the server 230 uses continuous mechanics techniques to obtain a deformation gradient tensor in each phase from the deformation mesh of the exterior wall.

[0203] Method 800 proceeds to step 814. Step 814: Calculate the wall strain parameters based on the local deformation in each phase. In step 814, the server 230 calculates the wall strain parameters or maximum principal strain calculation for all phases along the principal strain direction based on the deformation gradient tensor in each phase.

[0204] Method 800 proceeds to step 816. Step 816: Generate blood flow parameters based at least partially on the volume mesh of the lumen. In one embodiment, in step 816, the server 230 first generates a simulation of intraluminal blood flow based at least partially on a volume mesh. To generate the simulation of intraluminal blood flow, the server 230 uses CFD parameters including boundary conditions, viscosity, density, and time step. The server 230 acquires blood flow parameters, which include each set of intraluminal blood flow values ​​for the cardiac cycle. In one embodiment, step 816 can be replaced by using 4D flow MRI for a given patient. That is, the server 230 acquires blood flow parameters using 4D flow MRI data, which include each set of intraluminal blood flow values ​​for the cardiac cycle.

[0205] In one embodiment, in step 816, the server 230 generates blood flow parameters by performing 4D flow MRI. The velocity field is measured based on a volume mesh of a 3D geometric model of the lumen. 4D flow MRI can be performed by using the medical imaging device 210 if the medical imaging device 210 is an MRI capable of performing 4D flow MRI acquisition, or by using an MRI capable of performing 4D flow MRI acquisition if the medical imaging device 210 is not an MRI.

[0206] The blood flow parameters include each set of intraluminal blood flow values ​​for the cardiac cycle. Method 800 proceeds to step 818.

[0207] Step 818: Determine the wall shear stress parameters based on the blood flow parameters. In step 818, the server 230 determines the wall shear stress (WSS) disturbance based on the CFD results, and calculates the wall shear stress parameter, based on the blood flow parameters, which include each set of intraluminal blood flow values ​​for the cardiac cycle.

[0208] Server 230 derives a hemodynamic wall descriptor (HWD) based on total shear stress from the CFD results. In one embodiment, Server 230 derives a hemodynamic wall descriptor (HWD) based on total shear stress from 4D flow MRI data instead of CFD results. The HWD incorporates the temporal changes in the magnitude and direction of the WSS vector. Server 230 calculates wall shear stress parameters based on the HWD.

[0209] In one embodiment, the wall shear stress parameter includes time-averaged wall shear stress (TAWSS).

[0210] Method 800 proceeds to step 820. Step 820: Calculate the rupture probability parameter of the blood vessel based on the thickness parameter, wall strain parameter, and wall shear stress parameter. In step 820, the server 230 calculates the bursting probability parameter based on the thickness parameter, wall strain parameter, and wall shear stress parameter.

[0211] In one embodiment, the server 230 defines multiple patches on the outer wall perpendicular to the centerline of the lumen and on the vascular geometry of the lumen, and determines the patch mean distribution for each of the wall strain parameter, thickness parameter, and wall shear stress parameter.

[0212] In one embodiment, the server 230 receives collective-based values ​​of wall strain parameters, thickness parameters, and wall shear stress parameters.

[0213] In one embodiment, the server 230 determines the distributional quartiles of the patch-mean distributions of ILT thickness, wall strain, and wall shear stress for the patch-mean thickness parameter, patch-mean wall strain parameter, and patch-mean wall shear stress parameter, respectively. In one embodiment, the distributional quartiles are determined for each patch-mean distribution of patient-specific distributions and population-based distributions.

[0214] In one embodiment, the server 230 classifies the ILT thickness, wall strain, and wall shear stress for each patch based on the determined distribution quartiles.

[0215] In one embodiment, the server 230 assigns categories 1 to 4 to the values ​​of the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter.

[0216] Server 230 determines the regional failure potential (RRP) of each patch based on the categories of patch average thickness parameter, patch average wall strain parameter, and patch average wall shear stress parameter.

[0217] The regional rupture potential parameter indicates the weakening status and rupture potential of each patch in a series of patches on a blood vessel.

[0218] Method 800 ends here. Experimental results Referring to Figures 4A to 47, experimental results determining the possibility of regional rupture in patients using non-limiting embodiments of this technology are described.

[0219] The patient was a 62-year-old male with a subrenal AAA (5.6 cm in diameter), and a preoperative ECG-gated dynamic computed tomography scan showed radiographic findings of an active ruptured aneurysm, allowing for the determination of the likelihood of regional rupture.

[0220] The predicted flow pattern, as shown in Figures 5 and 6A-6C, was characterized by recirculation and low velocity in the aneurysm sac, where low TAWSS values ​​and thick ILT were dominant. A strong negative correlation was observed between region-average TAWSS and ILT thickness (ρ=-0.78, p=5.9e-06). As shown in Figures 5 and 6A-6C, the main flow channels associated with high velocity appeared in the impact regions on the neck and aortic wall, resulting in little high TAWSS, ILT, and high strain, and pointing to moderate positive correlations between region-average TAWSS and maximum principal strain (ρ=0.60, p=0.0022) and between region-average ILT and strain (ρ=-0.61, p=0.0014).

[0221] The AAA rupture was identified during surgical intervention and occurred posterolaterally at the level of patch LP5, around the 5 o'clock position on a clock face, rather than at the location of the maximum diameter, as shown in Figures 4A and 4B. This region showed low patch mean TAWSS, thick ILT, and high maximum principal strain, corresponding to a weak wall RRP, as shown in Figure 7. Table I reproduced above shows all patches with corresponding categories for each descriptor. Patch LP5 was assigned category 1 for TAWSS (low TAWSS), category 4 for ILT (thick thrombus), and category 3 for strain (high deformability).

[0222] Discussion and Conclusion Aneurysm ruptures occurred in areas of reduced blood flow velocity and were characterized by low TAWSS and recirculation associated with thick thrombus deposition, consistent with previously reported findings. While shear stress is unlikely to be the direct cause of rupture, the strong correlation observed between patch-mean TAWSS and ILT suggests a mechanism of thrombus deposition in turbulent areas where low oscillating wall shear stress is dominant. The effects of ILT accumulation contribute to local inflammatory processes and hypoxia, potentially leading to adverse remodeling and loss of structural integrity in the shadow of disease progression.

[0223] Heterogeneous reconstruction is reflected in local in vivo measurements of deformability. A moderate regional correlation was found between TAWSS and strain (neck, LA3, RA3, LA6), a possible consequence of main channel collision. However, ruptured patches showed the opposite trend, exhibiting low TAWSS and high maximum principal strain, as shown in Figures 6A–7, resulting in a high RRP index as an indicator of local weakening. These observations enabled good prediction of rupture location by adding information about the state of local weakening of the wall. This study was limited to one ruptured patient and assumed a rigid aortic wall for CFD simulations. Despite the limitations, the results highlight the importance of local descriptors in assessing aortic wall fragility and demonstrate the predictive power of a combination of hydrodynamic and strain analysis in estimating the likelihood of rupture in individual aneurysms with possible clinical applications.

[0224] At least some embodiments of this technology aim to expand the range of technical solutions for addressing a specific technical problem, namely determining the likelihood of in vivo rupture of blood vessels by using 3D models of blood vessels and computational fluid dynamics simulations, which will be apparent to those skilled in the art as it can save computational resources.

[0225] It should be clearly understood that not all technical effects mentioned herein are necessarily enjoyed in every embodiment of the Art. For example, embodiments of the Art may be implemented without the user enjoying some of these technical effects, and other non-limiting embodiments may be implemented with or without the user enjoying other technical effects.

[0226] Some of these steps and signal transmission-reception are well known in the art and are therefore omitted in certain parts of this description for the sake of simplification. Signals can be transmitted and received using optical means (such as optical fiber connections), electronic means (such as wired or wireless connections), and mechanical means (e.g., pressure-based, temperature-based, or any other suitable physical parameter-based).

[0227] Modifications and improvements to the above-described implementation of this technology may be apparent to those skilled in the art. The foregoing description is intended to be illustrative, not restrictive. Accordingly, the scope of this technology is intended to be limited only by the appended claims.

Claims

1. A computer implementation method for determining the likelihood of rupture indicating a weakened state in at least one region of a given blood vessel, wherein the method is executable by a server. - The server receives multiple images of the blood vessels of the given target, acquired by a medical imaging device. - A step of organizing the plurality of images into a multiphase stack by the server, wherein a given phase of the multiphase stack represents the blood vessel at a given time of the cardiac cycle, - The server generates a volume mesh of the lumen of the blood vessel and a surface mesh of the outer wall of the blood vessel using the multiphase stack, - The server calculates a thickness parameter based on the volume mesh of the lumen and the surface mesh of the outer wall, - The server determines the local deformation in each phase of the multiphase stack by mapping the voxels of the surface mesh of the outer wall to the multiphase stack, - The server calculates a wall strain parameter that indicates the maximum principal strain in the outer wall based on the local deformation in each phase, - A step of generating blood flow parameters based at least partially on the volume mesh of the lumen, wherein the blood flow parameters include a set of blood flow values ​​in the lumen for a cardiac cycle, - The server calculates wall shear stress parameters indicating wall shear disturbances within the lumen based on the blood flow parameters, A method comprising the steps of: determining a rupture probability parameter of the blood vessel based on the thickness parameter, the wall strain parameter, and the wall shear stress parameter using the server, wherein the rupture probability parameter indicates the state of weakening of at least one region of the blood vessel.

2. The method according to claim 1, wherein the wall shear stress parameter includes time-averaged wall shear stress (TAWSS).

3. The method according to claim 1 or 2, wherein the step of generating the blood flow parameters includes generating a computational fluid dynamics (CFD) simulation of the blood flow in the lumen to obtain the respective sets of blood flow values ​​in the lumen for the cardiac cycle.

4. The method according to claim 1 or 2, wherein the step of generating the blood flow parameters includes performing a 4D flow MRI acquisition to obtain the respective sets of intraluminal blood flow values ​​for the cardiac cycle.

5. The above method, before the step of calculating the wall strain parameter, The step further comprises determining the local deformation in each phase of the surface mesh based on the multiphase stack and the surface mesh of the outer wall, The step of calculating the wall strain parameter is based on the local deformation in each phase of the surface mesh, The method according to any one of claims 1 to 4.

6. The method according to any one of claims 1 to 2, wherein the step of calculating the thickness parameter includes calculating the intraluminal thrombus (ILT) thickness based on the distance between the surface mesh of the outer wall and the surface mesh of the lumen.

7. The method described above, prior to the step of determining the rupture possibility parameter, The method further comprises the step of receiving a collective-based thickness parameter, a collective-based wall strain parameter, and a collective-based wall shear stress parameter. The step of determining the bursting possibility parameter is further based on the thickness parameter of the collective base, the wall strain parameter of the collective base, and the wall shear stress parameter of the collective base. The method according to any one of claims 1 to 6.

8. The method, prior to the step of estimating the rupture probability parameter, The server further comprises the step of defining multiple patches on the blood vessel, The step of calculating the thickness parameter, the wall strain parameter, and the wall shear stress parameter includes using the plurality of patches to calculate the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter, The bursting possibility parameter is based on the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter. The method according to any one of claims 1 to 7.

9. The method according to claim 8, wherein the step of calculating the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter is further based on a collective-based thickness parameter, a collective-based wall strain parameter, and a collective-based wall shear stress parameter.

10. The method according to claim 9, further comprising the step of determining the distribution quartile values ​​for each of the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter.

11. The method according to claim 10, further comprising the step of classifying each of the patch mean thickness parameter, the patch mean wall strain parameter, and the patch mean wall shear stress parameter based on the respective distribution quartile values.

12. The aforementioned rupture probability parameter is, [Math 1] The method according to any one of claims 1 to 11.

13. The method according to claim 12, wherein each of the categories assigned to the thickness parameter, each of the categories assigned to the wall strain parameter, and each of the categories assigned to the wall shear stress parameter have values ​​from 1 to 4.

14. The steps of generating the volume mesh of the lumen and generating the surface mesh of the outer wall are, - The server generates a first geometric model of the lumen of the blood vessel and a second geometric model of the outer wall of the blood vessel by segmenting the multiphase stack, The method according to any one of claims 1 to 13, comprising the steps of: smoothing the first geometric model by the server to obtain the volume mesh of the lumen; and smoothing the second geometric model to obtain the surface mesh of the outer wall.

15. A system for determining the likelihood of rupture, indicating the weakened state of at least one region of a given blood vessel, Processor and The system comprises a computer-readable storage medium containing instructions, connected to the aforementioned processor, When the aforementioned instruction is executed by the processor, - Receive multiple images of the blood vessels of the given object, acquired by a medical imaging device. - The multiple images are arranged in a multiphase stack, and a given phase of the multiphase stack represents the blood vessel at a given time in the cardiac cycle. - Using the multiphase stack, the volume mesh of the lumen of the blood vessel and the surface mesh of the outer wall of the blood vessel are generated. Based on the volume mesh of the lumen and the surface mesh of the outer wall, the thickness parameter is calculated. - By mapping the voxels of the surface mesh of the outer wall to the multiphase stack, the local deformation in each phase of the multiphase stack is determined. Based on the local deformation in each phase, calculate the wall strain parameter that represents the maximum principal strain in the outer wall. - Generate blood flow parameters based at least partially on the volume mesh of the lumen, wherein the blood flow parameters include a set of blood flow values ​​in the lumen at a given moment. Based on the blood flow parameters, calculate the wall shear stress parameters that indicate wall shear disturbances within the lumen. A system configured to determine a rupture probability parameter of the blood vessel based on the thickness parameter, the wall strain parameter, and the wall shear stress parameter, wherein the rupture probability parameter indicates a weakening state in at least one region of the blood vessel.

16. The system according to claim 15, wherein the wall shear stress parameter includes time-averaged wall shear stress (TAWSS).

17. The system according to claim 15 or 16, wherein the generation of the blood flow parameters includes generating a computational fluid dynamics (CFD) simulation of the blood flow in the lumen to obtain the respective sets of the blood flow values ​​in the lumen for the cardiac cycle.

18. The system according to claim 15 or 16, wherein the generation of the blood flow parameters includes performing a 4D flow MRI acquisition to obtain each set of the intraluminal blood flow values ​​for the cardiac cycle.

19. The processor, before calculating the wall strain parameters, Based on the multiphase stack and the surface mesh of the outer wall, it is further configured to determine the local deformation in each phase of the surface mesh, The system according to any one of claims 15 to 18, wherein the calculation of the wall strain parameter is based on the local deformation in each phase of the surface mesh.

20. The system according to any one of claims 15 to 19, wherein the thickness parameter is determined based on the distance between the surface mesh of the outer wall and the surface mesh of the lumen.

21. The processor, prior to the determination of the rupture possibility parameter, It is further configured to receive collective-based thickness parameters, collective-based wall strain parameters, and collective-based wall shear stress parameters. The system according to any one of claims 15 to 20, wherein the determination of the bursting potential parameter is further based on the thickness parameter of the collective base, the wall strain parameter of the collective base, and the wall shear stress parameter of the collective base.

22. The processor, prior to the determination of the rupture possibility parameter, Further configured to define multiple patches on the aforementioned blood vessel, The calculation of the thickness parameter, the wall strain parameter, and the wall shear stress parameter comprises the calculation of the patch-average thickness parameter, the patch-average wall strain parameter, and the patch-average wall shear stress parameter using the plurality of patches. The system according to any one of claims 15 to 21, wherein the bursting possibility parameter is based on the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter.

23. The system according to claim 22, wherein the calculation of the patch-average thickness parameter, the patch-average wall strain parameter, and the patch-average wall shear stress parameter is further based on a collective-based thickness parameter, a collective-based wall strain parameter, and a collective-based wall shear stress parameter.

24. The system according to claim 23, wherein the processor is further configured to determine the distribution quartiles for each of the patch average thickness parameter, the patch average wall strain parameter, and the patch average wall shear stress parameter.

25. The system according to claim 24, wherein the processor is further configured to classify each of the patch mean thickness parameter, the patch mean wall strain parameter, and the patch mean wall shear stress parameter based on the respective distribution quartile values.

26. The aforementioned rupture probability parameter is, [Math 2] The system according to any one of claims 15 to 25.

27. ​​The system according to claim 26, wherein each of the categories assigned to the thickness parameter, each of the categories assigned to the wall strain parameter, and each of the categories assigned to the wall shear stress parameter have values ​​from 1 to 4.

28. The generation of the volume mesh of the lumen and the generation of the surface mesh of the outer wall are as follows: - Generation of a first geometric model of the lumen of the blood vessel and a second geometric model of the outer wall of the blood vessel by segmenting the multiphase stack, The system according to any one of claims 15 to 27, comprising: smoothing the first geometric model for obtaining the volume mesh of the lumen; and smoothing the second geometric model for obtaining the surface mesh of the outer wall.

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