Method and system for in vivo strain mapping of aortic dissection

Abstract

A method and system are provided for generating a strain map of a dissected vessel. A multi-phase stack of a dissected vessel is received, a given phase of the multi-phase stack represents the vessel at a given time of the cardiac cycle. A 3D geometric model of the vessel is generated, including a wall of the vessel and a dissection flap. A surface mesh of the vessel of a first phase is generated, the surface mesh including a vessel wall surface mesh and a dissection flap surface mesh. Local deformations at each phase are determined by mapping voxels of the surface mesh of the vessel to the multi-phase stack. A strain map including principal strain values ​​is generated using the local deformations and the vessel wall surface mesh and the dissection flap surface mesh.

Description

[Technical Field]

[0001] field This technology relates to the field of medical imaging. More specifically, this technology relates to a method and system for in vivo evaluation of dissected aortic deformation based on electrocardiogram (ECG) gated images. [Background technology]

[0002] background Aortic dissection (AD) results from the separation of the aortic wall and the formation of a tear in its innermost layer (intima). The initial tear allows blood to flow between the intima and media, leading to further separation of these layers and, in this case, the formation of a second lumen (false lumen) isolated from the true lumen by the remaining intima, called the intima flap or dissection flap. Referring to Figure 1, medical images and DICOM stack slices obtained from static computed tomography (CT) images showing the presence of dissection in the descending portion of the aorta, with arrows pointing to the dissection flap separating the smaller "true" lumen from the larger "false" lumen.

[0003] Depending on the presence of secondary (or re-entry) cracks, AD can be characterized as either a patent or occluded dissociation.

[0004] The onset of aortic dissection results in significant changes to the morphology and hemodynamics of the artery, potentially leading to aortic rupture or impaired perfusion to vital organs due to compression and collapse of the true lumen by a pressurized false lumen. For this reason, despite its low incidence, AD has a high mortality rate (20% before reaching hospital, ranging from 3% / hour in the first 24 hours to up to 90% at one year if untreated)[1] and is associated with comorbidities and long-term complications such as aneurysmal degeneration, which is dilation of the false lumen.

[0005] Despite several risk factors associated with AD (e.g., age, hypertension, connective tissue disorders, deceleration trauma, bicuspid valve, previous cardiac surgery, vasculitis), the pathogenesis of this disease is not fully understood. Aortic aneurysms, intramural hematomas, and perforating atherosclerotic ulcers of the tunica media have been identified as causes of aortic weakening and precursory cracks that can destroy the intima and develop into dissection, but these are not the only causes of AD. For example, cases of aortic aneurysms that develop into dissection are reported in only about 20% of acute dissections, suggesting pre-existing degeneration of the tunica media as a matrix for pathological initiation and a generally different pathological pathway.

[0006] Given that the location of the initial intimal tear and the anatomical involvement of the dissection have been identified as critical to disease progression, a clinically recognized classification of AD was introduced to aid in disease management based on anatomical features related to prognosis. On the one hand, the Stanford classification system distinguishes between type A and type B aortic dissections, with the first dissection involving the ascending thoracic aorta regardless of tear location, and the second dissection occurring beyond the left subclavian artery, thus not involving the ascending segment of the aorta. On the other hand, the DeBakey classification system refers to the location of the initial intimal tear, with DeBakey type I AD originating in the ascending aorta and propagating within or beyond the arch, DeBakey type II AD originating in the ascending aorta and confined to the ascending aorta, and DeBakey type III AD originating in the descending aorta. These classifications allow for the identification of cases requiring surgical repair and those that can benefit from minimally invasive medical procedures. The location of the main tear and the overall anatomical involvement are indeed important factors in defining optimal treatment. More proximal entry tears are associated with poor clinical outcomes and higher mortality, thus determining the need for more invasive treatments and recommended interventions (such as open or endovascular repair) for type A AD, even if the patient does not present with significant complications such as poor perfusion or comorbidities. Conversely, type B AD has been treated conservatively with beta-blockers to control blood pressure unless significant complications are present and surgical intervention is not required.

[0007] The temporal progression of aortic dissection and the duration of symptoms are also significant causes of mortality and therefore influence clinical decisions. The acute phase of AD is defined as the first two weeks from the initial onset of symptoms of aortic tear, with the subsequent weeks representing the so-called subacute phase (2 weeks to 1 month from the initial onset) and chronic phase (more than 1 month from the onset). Patients with type A AD are typically treated as emergency cases that benefit from acute phase interventions, and therefore rarely reach the chronic phase. The chronic phase is more common in patients with type B AD, and the subacute phase is a crucial period for monitoring potential long-term complications in type B cases that are usually stable in the short term and without complications.

[0008] Despite recognized clinical guidelines, the management of aortic aorta remains debated and divisive. A typical debate concerns the management of type B and residual type B aortic dissection in patients who have undergone surgical repair of the ascending aorta but have residual dissection in the descending portion of the artery.

[0009] While residual type B dissection without complications may appear stable in the short term, it can progress to and develop complications such as false lumen aneurysms (20-40% within 1-5 years), thus increasing patient risk. There is disagreement regarding the management of these cases, and little clinical consensus on whether early intervention may be beneficial for some patients in preventing long-term adverse outcomes. More recent literature and evidence suggest that endovascular repair (TEVAR) should be considered in addition to pharmacological treatment to improve long-term outcomes and prevent late complications in appropriate subjects. The endovascular option, while less invasive, is not without risks, as stent graft insertion can be problematic in already damaged aorta with weakened walls and may promote retrograde dissection or partial thrombosis.

[0010] Another factor in determining how to manage AD is the dynamic behavior of the dissecting flap exposed to pulsating blood flow, as this can impair the placement and durability of the stent graft. In the acute and subacute phases, the dissecting flap is prone to movement during the cardiac cycle, but as the disease progresses to the chronic phase, it becomes thicker, more fibrous, and characterized by more rigid behavior.

[0011] The TEVAR approach should typically be selected after an accurate assessment of the benefits to treatment-related risks on a patient-specific basis. In this regard, different studies have reported false lumen patency and maximum aortic diameter (greater than 40 mm) as risk factors for later aneurysm formation and adverse outcomes. Some findings suggest that a larger false lumen and a larger entry fissure may induce higher flow velocity in the false lumen, thereby promoting its patency; therefore, accurately assessing the size of the false lumen and the entry fissure may be essential.

[0012] The complex management of AD stems from the anatomical complexity of the dissected aorta, as well as the strong dependence and interaction between geometric shape and hemodynamics that influence the interaction of the two lumens. In this regard, the pressure difference between the true lumen and false lumen can cause compression and dynamic occlusion of the true lumen, potentially leading to reduced blood flow to downstream organs of the dissection and thus increasing the risk of ischemic events. Furthermore, a pressurized false lumen can be favorable for dilation and aneurysmal degeneration if a large pressure difference is created between the two lumens and maintained throughout the cardiac cycle.

[0013] Another factor contributing to the long-term outcomes of Alzheimer's disease (AD) is pseudolumen thrombosis, often facilitated by slow flow stagnation. While complete pseudolumen thrombosis is associated with higher survival rates, partial thrombosis, which can cause occlusion of distal fissures, hindering blood reflow and leading to increased pressure, is associated with adverse outcomes and increased mortality.

[0014] Imaging modalities such as CT, 3D, or 4D MRI provide anatomical information along with fluid dynamic information that can aid in the clinical evaluation of a dissected aorta, but they do not provide non-invasive pressure measurements in the false lumen and dissected region. Current standard treatment relies on the use of contrast-enhanced CT imaging for the identification and evaluation of dissected aorta.

[0015] Therefore, there is a need to improve the assessment of the severity of dissected blood vessels. [Overview of the project] [Problems that the invention aims to solve]

[0016] overview The purpose of this technology is to improve at least some of the shortcomings of the prior art. One or more embodiments of this technology can provide and / or expand the range of approaches and / or methods for achieving the aims and objectives of this technology.

[0017] One or more embodiments of this technology were developed based on the developers' recognition of a clinical need to improve the management of AD with methods and systems that can complement standard medical imaging techniques and provide an objective assessment of dissection severity, a measure of aortic wall fragility relative to non-dissected areas, and an assessment of the dynamic behavior and interaction of dissection flaps, true lumen, and false lumen, which may support risk stratification and case-by-case treatment decision-making.

[0018] More specifically, based on the above, the developers of this technology recognized that AD is complex to model and simulate using computational fluid dynamics (CFD) and fluid-structure interaction (FSI) simulation techniques, which generally require the assumption of homogeneous material properties, but may not be suitable for AD because the tissues of different parts of a dissected aorta have different material properties, and there is often no blood flow in the false lumen of the dissected vessel.

[0019] This technology enables a substantially objective assessment of aortic dissection compared to current standard treatments by providing information accessible only by analysis using the methods and systems described herein, complementing anatomical assessments based on medical images. This technology can be used to assist in risk stratification on a patient-specific basis and outcome prediction for treatment selection purposes, thus changing the patient care standard in the field of aortic dissection.

[0020] Thus, one or more embodiments of this technology relate to methods and systems for in vivo strain mapping of aortic dissection.

Means for Solving the Problems

[0021] In a broad aspect of the present technology, a method for generating a strain map of dissociated blood vessels of a given subject is provided. The method is executed by a processor, and the method includes receiving a multiphase stack generated from a plurality of images of dissociated blood vessels of a given subject, wherein a given phase of the multiphase stack represents the dissociated blood vessels at a given point in the cardiac cycle, and generating a 3D geometric model of at least a portion of the dissociated blood vessels using at least a portion of the multiphase stack, wherein the 3D geometric model includes the wall of the dissociated blood vessels and the dissociation flap. The method includes generating a surface mesh of at least a portion of the dissociated blood vessels for a first phase of the multiphase stack using the 3D geometric model, wherein the surface mesh of at least a portion of the dissociated blood vessels includes a blood vessel wall surface mesh and a dissociation flap surface mesh, and determining local deformation at each phase of the multiphase stack by mapping voxels of the surface mesh of the dissociated blood vessels to the multiphase stack at each phase using the surface mesh of at least a portion of the dissociated blood vessels and the multiphase stack. The method includes generating a set of strain maps and a given strain map of the set of strain maps including principal strain values on the surface of the dissociated blood vessels for corresponding phases of the cardiac cycle using the local deformation at each phase and the blood vessel wall surface mesh and the dissociation flap surface mesh, and outputting the set of strain maps.

[0022] In one or more embodiments of the method, the method further includes generating a maximum strain map showing the maximum principal strain value over the cardiac cycle using the set of strain maps, and outputting the maximum strain map.

[0023] In one or more embodiments of the method, the method further includes generating an interactive model of the dissociated blood vessels using the 3D geometric model and the set of strain maps, and transmitting the interactive model of the dissociated blood vessels for display on a display interface connected to the processor.

[0024] In one or more embodiments of the present method, the step of generating a set of strain maps includes, for a given strain map, the step of projecting the strain in the principal curvature direction in order to obtain circumferential and axial strain values ​​on the surface mesh of the dissociated blood vessels.

[0025] In one or more embodiments of the present method, the step of generating a 3D geometric model of at least a portion of the dissected blood vessels using a multiphase stack includes the step of segmenting the multiphase stack to obtain segmented dissected blood vessels, and obtaining a 3D geometric model using the segmented dissected blood vessels.

[0026] In one or more embodiments of the method, the method further includes, prior to the step of receiving a multiphase stack generated from a plurality of images, a step of receiving a plurality of images acquired using an electrocardiogram (ECG) gated medical imaging device, and a step of generating a multiphase stack using the plurality of images.

[0027] In one or more embodiments of this method, the step of generating a surface mesh includes the step of smoothing a 3D geometric model in order to obtain a surface mesh of dissociated blood vessels.

[0028] In one or more embodiments of the present method, the step of determining local deformation in each phase of a multiphase stack by mapping voxels of a surface mesh to a multiphase stack, using a surface mesh and a multiphase stack, includes the step of using an optical flow algorithm.

[0029] In one or more embodiments of this method, the 3D geometric model of at least a portion of the dissected blood vessel includes markings for true lumens and false lumens.

[0030] In one or more embodiments of the present method, the method further includes the steps of evaluating the mobility of a dissecting flap using a set of strain maps of the dissected vessel, and identifying false lumen pressurization and true lumen compression over the cardiac cycle.

[0031] In one or more embodiments of this method, the 3D geometric model of at least a portion of the dissected blood vessel further includes markings of healthy, non-dissected regions of the blood vessel.

[0032] In one or more embodiments of the method, the method further includes the step of determining regional vulnerability of dissected vessels using a set of strain maps of dissected vessels over the cardiac cycle and markings of healthy non-dissected areas.

[0033] In one or more embodiments of the present method, the method further includes the step of predicting the expansion of a dissecting fissure within a dissected vessel using a set of strain maps of the dissecting flap.

[0034] In one or more embodiments of the method, the method further includes the step of repeating the method on a second multiphase stack of dissected vessels of a given subject acquired in subsequent time, thereby obtaining a further 3D geometric model of the dissected vessels and a set of further strain maps in subsequent time.

[0035] In one or more embodiments of the method, the method further includes the step of generating a further interactive model, including the geometry and strain progression of a dissected vessel, using a 3D geometric model, a set of strain maps, a further 3D geometric model, and further strain maps at each phase of the cardiac cycle.

[0036] In one or more embodiments of the method, the method further includes the step of predicting further regional vulnerability in the dissected vessels using a set of strain maps and a further set of strain maps.

[0037] In one or more embodiments of the method, the method further includes the step of predicting further expansion of dissociative cracks in dissociated blood vessels using a set of strain maps and a further set of strain maps.

[0038] In a broad embodiment of the present technology, a system is provided comprising a processor and a non-temporary storage medium operably connected to the processor. The non-temporary storage medium comprises stored computer-readable instructions, and the processor is configured to execute a computer-readable instruction, receiving a multiphase stack generated from multiple images of a dissected vessel of a given subject, wherein a given phase of the multiphase stack represents the dissected vessel at a given point in time of the cardiac cycle, and generating a 3D geometric model of at least a portion of the dissected vessel using at least a portion of the multiphase stack, wherein the 3D geometric model includes the walls and dissection flaps of the dissected vessel. The processor is configured to perform the following: generate a surface mesh of at least a portion of the dissected vessels for a first phase of a multiphase stack using a 3D geometric model, wherein the surface mesh of at least a portion of the dissected vessels includes a vessel wall surface mesh and a dissection flap surface mesh; determine local deformation in each phase of the multiphase stack by mapping the voxels of the surface mesh of the dissected vessels to the multiphase stack in each phase using the surface mesh of at least a portion of the dissected vessels and the multiphase stack; generate a set of strain maps and a given strain map of a set of strain maps including principal strain values ​​on the surface of the dissected vessels for the corresponding phase of the cardiac cycle using the local deformation in each phase and the vessel wall surface mesh and dissection flap surface mesh; and output the set of strain maps.

[0039] In one or more embodiments of the system, the processor is further configured to use a set of strain maps to generate a maximum strain map showing the maximum principal strain value over a cardiac cycle, and to output the maximum strain map.

[0040] In one or more embodiments of the system, the processor is further configured to perform the following: generate an interactive model of the dissected vessel using a set of 3D geometric models and strain maps; and transmit the interactive model of the dissected vessel for display on a display interface connected to the processor.

[0041] In one or more embodiments of the system, generating a set of strain maps involves projecting strain in the principal curvature direction to obtain circumferential and axial strain values ​​on the surface mesh of the dissociated blood vessels for a given strain map.

[0042] In one or more embodiments of the system, generating a 3D geometric model of at least a portion of the dissected vessels using a multiphase stack includes segmenting the multiphase stack to obtain segmented dissected vessels, and using the segmented dissected vessels to obtain a 3D geometric model.

[0043] In one or more embodiments of the system, the processor is further configured to receive a plurality of images acquired using an electrocardiogram (ECG) gated medical imaging device, prior to receiving a polyphase stack generated from the plurality of images, and to generate a polyphase stack using the plurality of images.

[0044] In one or more embodiments of the system, generating a surface mesh involves smoothing a 3D geometric model to obtain a surface mesh of dissociated blood vessels.

[0045] In one or more embodiments of the system, determining the local deformation in each phase of the multiphase stack by mapping voxels of the surface mesh to the multiphase stack, using a surface mesh and a multiphase stack, includes using an optical flow algorithm.

[0046] In one or more embodiments of the system, the 3D geometric model of at least a portion of the dissected blood vessel includes markings for true lumens and false lumens.

[0047] In one or more embodiments of the system, the processor is further configured to perform the following: evaluate the mobility of the dissecting flap using a set of strain maps of the dissected vessel; and identify false lumen pressurization and true lumen compression over the cardiac cycle.

[0048] In one or more embodiments of the system, the 3D geometric model of at least a portion of the dissected blood vessels further includes markings of healthy, non-dissected regions of the blood vessels.

[0049] In one or more embodiments of the system, the processor is further configured to determine regional vulnerability of dissected vessels using a set of strain maps of dissected vessels over the cardiac cycle and markings of healthy, non-dissected regions.

[0050] In one or more embodiments of the system, the processor is further configured to use a strain map of the dissociation flap to predict the expansion of the dissociation crack within the dissociated vessel.

[0051] In one or more embodiments of the system, the processor is further configured to execute computer-readable instructions on a second polyphase stack of dissected vessels of a given subject acquired in subsequent time, thereby obtaining a further 3D geometric model of the dissected vessels and a set of further strain maps in subsequent time.

[0052] In one or more embodiments of the system, the processor is further configured to generate a further interactive model, including the geometry and strain progression of a dissected vessel, using a 3D geometric model, a set of strain maps, a further 3D geometric model, and further strain maps at each phase of the cardiac cycle.

[0053] In one or more embodiments of the system, the processor is further configured to use a set of strain maps and a further set of strain maps to predict further regional vulnerability in the dissected vessels.

[0054] In one or more embodiments of the system, the processor is further configured to use a set of strain maps and a further set of strain maps to predict further expansion of dissociative cracks in dissected blood vessels.

[0055] 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 communications network), and executes or can cause those requests to be executed. The hardware may be a single physical computer or a single physical computer system, but is not required 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 made 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.”

[0056] 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 an electronic device in this context 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, performing, or causing to perform any task or request, or the results of any task or request, or any step in 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.

[0057] 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.

[0058] In the context of this specification, “database” is any structured collection of data, regardless of its specific structure, database management software, or the computer hardware on which the data is stored, implemented, or otherwise made available. A 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.

[0059] 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.

[0060] 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 indication to find a network, memory, database, or other computer-readable media location from which the information element can be obtained. For example, an indication of a document may include the document itself (i.e., its contents), or it may be a unique document descriptor that identifies the file with respect to a particular file system, or any other means that leads the recipient of the indication to a network location, memory address, database table, or any other location from which the file can be accessed. As those skilled in the art will recognize, the degree of precision required for such indication 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 indication. For example, if it is understood prior to the communication between the sender and receiver that an indication 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 indication.

[0061] 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.

[0062] 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 nouns they modify, and not for the purpose of describing a 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.

[0063] 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.

[0064] Additional and / or alternative features, aspects, and advantages of the implementation of this technology will become apparent from the following description, the accompanying drawings, and the accompanying claims.

[0065] Brief explanation of the drawing To better understand this technology, as well as other embodiments and further features, refer to the following description used in conjunction with the attached drawings. [Brief explanation of the drawing]

[0066] [Figure 1] This image shows a slice of the aorta obtained from static computed tomography (CT) imaging, illustrating the presence of dissection in the descending portion of the aorta. [Figure 2] A schematic diagram of an electronic device according to one or more non-limiting embodiments of this technology is shown. [Figure 3] A schematic diagram of a communication system according to one or more non-limiting embodiments of this technology is shown. [Figure 4] This is a schematic diagram of an aortic dissection (AD) strain mapping procedure performed in the system shown in Figure 3, according to one or more non-limiting embodiments of the present technology. [Figure 5] An example of a 3D geometric model of residual type B aorta in the descending aorta after surgery for type A aorta is shown, as illustrated by one or more non-limiting embodiments of this technology. [Figure 6] A flowchart of a method for in vivo strain mapping of dissected blood vessels, which can be performed by non-limiting embodiments of this technology, is shown. [Modes for carrying out the invention]

[0067] 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 be devised to embody the principles of the Art and to fall within its spirit and scope.

[0068] Furthermore, to aid understanding, the following description illustrates a relatively simplified implementation of the technology. As those skilled in the art will understand, various embodiments of the technology can be more complex.

[0069] 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.

[0070] Furthermore, all descriptions herein 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. Thus, for example, any block diagram herein 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.

[0071] The functionality of the various elements shown in the diagram, including any functional block labeled “Processor” or “Graphics Processing Unit,” may be provided using dedicated hardware and hardware capable of running software in relation to 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.

[0072] 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 a module may be executed by hardware, either explicitly or implicitly indicated.

[0073] Using these fundamentals appropriately, we will now consider some non-limiting examples to illustrate various embodiments of this technology.

[0074] Referring to Figure 2, a schematic diagram of an electronic device 100 suitable for use in several non-limiting embodiments of the present technology is shown.

[0075] Electronic devices The electronic device 100 comprises various hardware components, including one or more single-core or multi-core processors, which are 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.

[0076] 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.

[0077] 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 2, the touchscreen 190 comprises touch hardware 194 (for example, 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.

[0078] 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 perform aortic dissection in vivo strain mapping. For example, the program instructions may be part of a library or application.

[0079] 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.

[0080] system Referring to Figure 3, a schematic diagram of a communication system 200, referred to as System 200, is shown, which is suitable for implementing non-limiting embodiments of the Art. It should be clearly understood that the illustrated System 200 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, if this is not done (i.e., no examples of modifications are described), 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 provide a simpler implementation of the Art in certain cases, 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 this technology may be more complex.

[0081] 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 its respective communication links 225 (which are not numbered separately).

[0082] Medical devices The medical imaging device 210 is configured, in particular, to acquire multiple images of a given subject's blood vessels at different points in time so that a representation of the subject's blood vessels can subsequently be generated.

[0083] In one or more embodiments, the medical imaging device 210 includes an ECG gate type medical imaging device.

[0084] The medical imaging device 210 may include one of the following: a computed tomography (CT) scanner, a magnetic resonance imaging (MRI) scanner, or a 3D ultrasound scanner.

[0085] In some embodiments of this technology, the medical imaging device 210 may comprise one or more medical imaging devices, such as a computed tomography (CT) scanner, a magnetic resonance imaging (MRI) scanner, and a 3D ultrasound scanner.

[0086] The medical imaging device 210 may be configured with specific acquisition parameters for acquiring multiple images of blood vessels over the cardiac cycle.

[0087] As a non-limiting example, in one or more embodiments in which the medical imaging device 210 is implemented as a CT scanner, a CT protocol can be used that includes a preoperative retrospective gated multi-detector CT (MDCT - 64-row multislice CT scanner) using variable-dose radiation to capture the RR interval.

[0088] As another non-limiting example, in one or more embodiments in which the medical imaging procedure comprises an MRI scanner, the MR protocol may include steady-state T2-weighted fast-field echo (TE=2.6ms, TR=5.2ms, flip angle 110 degrees, fat suppression (SPIR), echo time 50ms, up to 25 cardiac phases, matrix 256×256, acquired voxel MPS (measurement, phase and slice coding direction) 1.56 / 1.56 / 3.00mm and reconstructed voxel MPS 0.78 / 0.78 / 1.5), or similar cine acquisition, axial slices of a portion of the aorta under study. The medical imaging device 210 includes, among other things, a workstation computer 215 for data transmission, or is connected to one thereto.

[0089] Workstation computer The workstation computer 215 is configured, in particular, to (i) control the parameters of the medical imaging device 210 and trigger image acquisition, and (ii) receive and process multiple images from the medical imaging device 210.

[0090] In one or more embodiments, the workstation computer 215 can receive images in raw format and perform tomographic reconstruction using known algorithms and software.

[0091] 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 have 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.

[0092] In one or more embodiments, the workstation computer 215 can be at least partially integrated with the medical imaging device 210.

[0093] In one or more embodiments, the workstation computer 215 is configured in accordance with the Medical Digital Imaging and Communication (DICOM) standard for communication and management of medical imaging information and related data.

[0094] In one or more embodiments, the workstation computer 215 can store images in a local database (not shown).

[0095] The workstation computer 215 is connected to the server 230 via the communication network 220 through its respective communication links 225. In one or more embodiments, the workstation computer 215 can transmit images and / or polyphase stacks to the server 230 and database 235 for storage and processing.

[0096] In one embodiment, the multiphase stack includes a plurality of 3D images, each captured at a different time or phase. In each phase, the 3D image includes a plurality of voxels, each associated with its respective 3D position and parameter values ​​such as color values, grayscale values, and intensity values.

[0097] server The server 230 is configured to, in particular, (i) receive multiple images of the dissected vessel acquired by the medical imaging device 210, (ii) use the multiple images of the dissected vessel to generate a multiphase stack in which each phase corresponds to a given moment in the cardiac cycle, (iii) use the multiple images and the multiphase stack to generate a 3D geometric model of the dissected vessel, (iv) use the 3D geometric model of the dissected vessel to generate a surface mesh of the dissected vessel, including a vessel wall surface mesh and a dissecting flap surface mesh, (v) use the surface mesh of the dissected vessel and the multiphase stack to determine the node displacement of the surface mesh throughout the cardiac cycle to obtain the local deformation of the dissected vessel in each phase, (vi) determine a strain map of the dissected vessel in each phase of the cardiac cycle, and (vii) use the strain map and the 3D geometric model of the dissected vessel to generate an interactive model of the dissected vessel.

[0098] The method by which server 230 is configured in this way is described in more detail below.

[0099] Server 230 can be implemented as a conventional computer server and may include some or all of the components of the electronic device 100 shown in Figure 2. In one or more embodiments of this technology, Server 230 is Microsoft TM Windows Server TM Dell operating system TM PowerEdge TM It can be implemented as a server. 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 illustrated technology, server 230 is a single server. In alternative non-limiting embodiments of the technology, the functions of server 230 may be distributed and implemented through multiple servers (not shown).

[0100] 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 network 220) via a communication network 220. Server 230 further includes at least one computer processor (e.g., processor 110 or GPU 111 of electronic device 100) which is operably connected to the communication interface and is structured and configured to perform various processes described herein.

[0101] In one or more embodiments, the server 230 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, a random access memory 130, a display interface 140, and an input / output interface 150.

[0102] database Although database 235 is directly connected to server 230, in one or more alternative embodiments, database 235 may be communicatively coupled to server 230 via communication network 220 without departing from the teachings of this technique. Although database 235 is schematically shown as a single entity herein, database 235 may be configured in a distributed manner, for example, database 235 may have different components, each component configured for a particular type of retrieval from or storage into.

[0103] Database 235 may be a collection of structured data, regardless of its specific structure or the computer hardware on which the data is stored, implemented, or otherwise made available. Database 235 may reside on the same hardware as processes that store or utilize the information stored in database 230, such as server 230, or it may reside on separate hardware, such as one or more other electronic devices (not shown) directly connected to server 230 and / or connected to the communication network 220. Database 230 can receive data from server 230 for its storage and can provide stored data to server 230 for its use.

[0104] The database 235 is configured to store, in particular, (i) images acquired by the medical imaging device 210, (ii) DICOM multiphase stacks, (iii) 3D geometric models of blood vessels, (iv) strain maps of dissected blood vessels, and (v) interaction models of dissected blood vessels.

[0105] Communication network In some embodiments of this technology, the communication network 220 is the Internet. In alternative, non-limiting embodiments, the communication network 220 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 220 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.

[0106] The communication network 220 may be used to transmit data packets between the workstation computer 215, the server 230, and the database 235. For example, the communication network 220 may be used to transmit requests between the workstation computer 215 and the server 230.

[0107] In one embodiment, the server 230 may be part of a picture archiving and communication system (PACS).

[0108] In another embodiment, the server 230 may be omitted. In this case, the workstation computer 215 communicates with or is connected to the database 235 and is configured, among other things, to (i) receive multiple images of the dissected vessel acquired by the medical imaging device 210, (ii) use the multiple images of the dissected vessel to generate a multiphase stack in which each phase corresponds to a given moment in the cardiac cycle, (iii) use the multiple images and the multiphase stack to generate a 3D geometric model of the dissected vessel, (iv) use the 3D geometric model of the dissected vessel to generate a surface mesh of the dissected vessel, including a vessel wall surface mesh and a dissection flap surface mesh, (v) use the surface mesh of the dissected vessel and the multiphase stack to determine the node displacement of the surface mesh over the entire cardiac cycle to obtain local deformation of the dissected vessel at each phase, (vi) determine a strain map of the dissected vessel at each phase of the cardiac cycle, and (vii) use the strain map and the 3D geometric model of the dissected vessel to generate an interactive model of the dissected vessel.

[0109] Aortic Dissection Strain Mapping Procedure Referring now to Figure 4, a schematic diagram of an aortic dissection (AD) strain mapping procedure 300 according to one or more non-limiting embodiments of the present technology is shown.

[0110] The AD strain mapping procedure 300 is performed within the system 200 shown in Figure 3. In one or more embodiments, the AD strain mapping procedure 300 may be performed by a server 230. In one or more other embodiments, the AD strain mapping procedure 300 may be performed by a workstation computer 215 connected to a medical imaging device 210. Some steps of the AD strain mapping procedure 300 may be performed in parallel by the server 230 or an electronic device (such as the workstation computer 215), as will be recognized by those skilled in the art.

[0111] The objective of AD strain mapping procedure 300 is to receive images of dissected vessels acquired during a given patient's cardiac cycle and to use the received images of the dissected vessels to generate a strain map of the dissected vessels in the given patient.

[0112] The AD strain mapping procedure 300 allows for the visualization and evaluation of interactions between the channels created by the dissociation flap (e.g., true lumen and false lumen), including the presence of blood in the false lumen, which can lead to complications resulting from pressurizing the false lumen, causing compression of the true lumen throughout the cardiac cycle, and restricting blood supply to downstream organs.

[0113] The AD strain mapping procedure 300 includes, in particular, an image acquisition procedure 310, an image segmentation procedure 320, a smoothing and surface meshing procedure 330, a motion tracking and mapping procedure 360, a strain calculation procedure 370, and an interactive model generation procedure 380.

[0114] Image acquisition The image acquisition procedure 310 is configured, in particular, to (i) receive images of the patient's dissected blood vessels acquired during the cardiac cycle, and (ii) use the received images of the dissected blood vessels to generate a multiphase stack thereof.

[0115] In one or more embodiments, images of the dissected vessel are obtained from a subject known to have aortic dissection, which may have been diagnosed by a physician. In one or more other embodiments, images of the dissected vessel may be obtained without prior knowledge of aortic dissection, and may be detected, for example, during the image segmentation procedure 320.

[0116] During the image acquisition procedure 310, multiple images of a blood vessel, such as the aorta of a given subject, are received. These multiple images may be received from a workstation computer 215, directly from the medical imaging device 210, or from a database such as database 235. In one or more embodiments, the multiple images of the blood vessel include an image of the aorta with a dissecting flap. It will be understood that the type of aortic dissection in the dissected vessel is not limited.

[0117] In one or more embodiments where the medical imaging device 210 has a CT scanner, the CT protocol for CT image acquisition may include preoperative retrospective gating MDCT (64-row multislice CT scanner) with 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 fast-field echo (TE=2.6ms, TR=5.2ms, flip angle 110 degrees, fat suppression (SPIR), echo time 50ms, max 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.

[0118] 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.

[0119] In one or more embodiments, each phase of the multiphase stack corresponds to a time instance of a given patient's cardiac cycle.

[0120] Image acquisition procedure 310 outputs a multiphase stack. Image segmentation The image segmentation procedure 320 is configured, in particular, to (i) receive as input images corresponding to a set of multiphase stacks, and (ii) generate a 3D geometric model of the dissected blood vessels based on the received input images.

[0121] In one or more embodiments, the input image corresponding to one phase of the multiphase stack includes a dissociation flap indicator. In a non-limiting example, the indicator may be provided by an operator via an input / output interface 150 such as a keyboard. The dissociation flap indicator may be received simultaneously with the multiphase stack or at a different time.

[0122] In one or more alternative embodiments, the image segmentation procedure 320 includes automatic identification of dissociation flaps. In this case, the image segmentation procedure 320 can use one or more machine learning (ML) models trained to recognize dissociation flaps in images of blood vessels. The image segmentation procedure 320 can perform segmentation by using the ML models to classify pixels as belonging to healthy tissue, dissociation portions, true lumen, false lumen, etc.

[0123] Therefore, the image segmentation procedure 320 includes receiving one phase of a multiphase stack with markings of the true lumen and false lumen within the dissected vessel.

[0124] The image segmentation procedure 320 uses segmentation techniques known to those skilled in the art to identify pixels or voxels belonging to objects such as blood vessels and / or to arrange pixels or voxels that form the boundaries of the blood vessels in order to generate a 3D geometric model of at least a portion of the blood vessels. It should be understood that any suitable segmentation technique may be used. The image segmentation procedure 320 can segment the stack based on one or more of pixel intensity, texture, and / or other attributes using deformable models and techniques such as 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, but is not limited to these. In one or more other embodiments, at least a portion of the image segmentation procedure 320 may be performed by a human operator by manually drawing the boundaries of the dissected blood vessels.

[0125] Image segmentation procedure 320 generates a 3D geometric model of the dissected vessel using a multiphase stack, the 3D geometric model of the vessel includes representations of at least the vessel wall and the dissecting flap wall.

[0126] In one or more embodiments, the 3D geometric model of the dissected vessel includes at least the true lumen, false lumen, dissection flap, and, if present in the image, the healthy (non-dissected) portion of the vessel.

[0127] A 3D geometric model of a dissected vessel, as well as a 3D geometric model of a vessel including representations of the vessel and dissected flap, may correspond to or be used to obtain a 3D geometric representation of the true lumen and false lumen.

[0128] In one or more embodiments, the image segmentation procedure 320 can generate a 3D geometric model of the dissected vessel corresponding to a given time in the cardiac cycle, identified as phase 0, based on a first or given phase of the multiphase stack. It will be understood that a 3D geometric model of the dissected vessel can be generated using any phase of the multiphase stack.

[0129] The false lumen within a dissected blood vessel corresponds to the lumen formed by the dissecting flap, which is separated from the true lumen within the blood vessel.

[0130] Figure 5 shows an example of a 3D geometric model 500 of residual type B aorta after surgery for type A aorta (surgical repair of the ascending aorta), output by the image segmentation procedure 320. The 3D geometric model 500 includes presentation of the repaired ascending aorta and aortic arch 510, true lumen 520, and false lumen 530. In the imaged portion of this aorta, an entry fissure beyond the left subclavian artery is identified.

[0131] Returning to Figure 4, the image segmentation procedure 320 outputs a 3D geometric model of the dissected blood vessel.

[0132] Smoothing and surface meshing The smoothing and surface meshing procedure 330 is configured, among other things, to (i) receive a 3D geometric model of the dissociated vessel, including the vessel walls and dissociation flaps, and (ii) use the 3D geometric model of the dissociated vessel to generate a surface mesh of the dissociated vessel, including a vessel wall surface mesh and a dissociation flap surface mesh. In one embodiment, the surface mesh of the dissociated vessel corresponds to a discrete representation of the 3D geometric model of the dissociated vessel, including nodes, vertices, edges, faces, etc.

[0133] In one or more embodiments, the surface mesh of the dissected blood vessel includes a surface mesh of the true lumen and a surface mesh of the false lumen of the dissected blood vessel.

[0134] The smoothing and surface meshing procedure 330 receives a 3D geometric model of the dissected vessel, including representations of the vessel wall and dissecting flap, as input.

[0135] In one embodiment, the smoothing and surface meshing procedure 330 filters or denoises the 3D geometric model of the dissociated blood vessels before generating a surface mesh of the dissociated blood vessels.

[0136] In the same or another embodiment, the smoothing and surface meshing procedure 330 smooths the 3D geometric model of the dissociated vessels before generating a surface mesh of the dissociated vessels.

[0137] It should be understood that any suitable method can be used to generate a surface mesh of dissected blood vessels from a 3D geometric model of the dissected vessels. For example, polygon modeling may be used.

[0138] In one embodiment, the surface mesh of the 3D geometric model of the dissected blood vessel is in the form of a discretized geometric shape of small triangular elements or small polygonal elements such as shells. In alternative embodiments of the art, it will be understood that smoothing of the 3D geometric model may be optional.

[0139] In one embodiment, the smoothing and surface meshing procedure 330 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 a dissociated blood vessel may have about 4,000 triangular shell elements.

[0140] In one embodiment, the resolution of the surface mesh of the 3D geometric model of the dissected blood vessel is at least the same as the pixel size. In one embodiment, the surface mesh of the dissected blood vessel is a deformable mesh.

[0141] The smoothing and surface meshing procedure 330 obtains a surface mesh of the vessel wall and a surface mesh of the dissected flap, corresponding to the surface mesh of the true lumen wall and the false lumen wall of the dissected vessel.

[0142] In one embodiment, a surface mesh for the true lumen and a surface mesh for the false lumen are generated to obtain a surface mesh for the dissociation flap.

[0143] The smoothing and surface meshing procedure 330 outputs a surface mesh of the dissected vessels, which includes a vessel wall surface mesh and a dissected flap surface mesh.

[0144] Motion tracking and mapping The motion tracking and mapping procedure 360 ​​is configured, among other things, to (i) receive the surface mesh of the dissociated vessel, including the surface mesh of the vessel wall and the surface mesh of the dissociated flap; (ii) receive a multiphase stack of the dissociated vessel for all phases; (iii) track and map the position of each voxel of the surface mesh node in the first stage (used to generate the surface mesh of the dissociated vessel) to all subsequent stages of the cardiac cycle in order to obtain the node displacement of the surface mesh for the entire cardiac cycle; and (iv) use the displaced nodes of the surface mesh to determine the local deformation of the surface mesh of the dissociated vessel in all phases of the cardiac cycle.

[0145] The motion tracking and mapping procedure 360 ​​receives as input the surface mesh of the 3D geometric model of the dissected blood vessel output by the image segmentation procedure 320 and a multiphase stack of images of all phases output by the image acquisition procedure 310. In one or more embodiments, the motion tracking and mapping procedure 360 ​​receives the 3D geometric model and the multiphase stack from the database 235.

[0146] In one embodiment, the motion tracking and mapping procedure 360 ​​is performed using the software Virtual Touch Aortic Aneurysm (ViTAA). TM This is carried out by using ), and embodiments thereof are described in International Patent Application Publication No. 2018 / 068153.

[0147] The motion tracking and mapping procedure 360 ​​uploads the surface mesh of the 3D geometric model of the dissociated blood vessels created for the first phase onto a multiphase stack.

[0148] The motion tracking and mapping procedure 360 ​​first imports the surface mesh of the dissociated vessels in the first phase into the three-dimensional space of the image of the first phase, thereby identifying the corresponding voxel in the image of the first phase for each node of the surface mesh of the dissociated vessels in the first phase. For each node of the surface mesh of the dissociated vessels in the first phase, the voxel position of its corresponding voxel in the first phase is assigned to the node. Next, the motion tracking and mapping procedure 360 ​​tracks the position of its corresponding voxel for each node of the surface mesh of the dissociated vessels across all subsequent phases, thereby mapping each voxel position of the surface mesh of the dissociated vessels in 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 each node position of the geometric shape in the first phase is associated with the corresponding node position in all subsequent phases, thereby obtaining the respective deformed surface mesh for each phase. Therefore, node displacements throughout the entire cardiac cycle, i.e., different phases, can be determined for the true lumen and false lumen, and thus for the dissociation flap. As a result, a deformed surface mesh is obtained for each phase, and each node of the deformed surface mesh for a given phase is assigned the voxel position of its corresponding voxel in the image of the given phase.

[0149] In one or more embodiments, the mapping of voxel positions of the surface mesh of the dissociated vessels in the first phase to all subsequent phases is performed using an optical flow (OF) algorithm. It will be understood that node displacement can be tracked using other techniques known in the art.

[0150] In one embodiment, the motion tracking and mapping procedure 360 ​​tracks the displacement of an object, such as a given point, between images captured in subsequent time steps by detecting grayscale features corresponding to an object and calculating its velocity. In one or more embodiments, the motion tracking and mapping procedure 360 ​​uses a trained machine learning model to track an object in the images.

[0151] As a non-limiting example, in the case of a CT image, the node corresponding to the first phase will have node positions corresponding to all subsequent phases.

[0152] From the map of displaced nodes, motion tracking and mapping procedure 360 ​​generates a mesh for each phase. The positions of all voxels in different phases are mapped back to the surface mesh of the first phase, so that each node position in the surface mesh shape in the first phase is associated with the corresponding node positions in all subsequent phases. In other words, the initial surface mesh, i.e., the surface mesh of the first phase, is used to track the corresponding voxels in subsequent phases and generate a deformed surface mesh in all phases by updating the coordinate position (or displacement) of each node in the initial surface mesh.

[0153] The motion tracking and mapping procedure 360 ​​outputs local deformations of the surface mesh at each phase for true and false lumens within the dissected vessel. In one embodiment, the local deformation at a given phase includes the voxel positions of each node in the surface mesh at a given phase, i.e., for each node in the surface mesh, the position of the voxel corresponding to the node at a given phase. In another embodiment, the local deformation at a given phase includes changes in the voxel positions of each node in the surface mesh between a previous phase and a given phase, such as changes in voxel positions from a first phase to a given phase.

[0154] Distortion calculation The strain calculation procedure 370 is configured to, in particular, (i) receive local deformation of the surface mesh of the dissected vessel, including local deformation of the vessel wall and dissection flap in each phase of the cardiac cycle, (ii) determine the strain distribution in each phase of the cardiac cycle based on the surface mesh of the vessel wall and dissection flap in the first phase of the cardiac cycle, and (iii) the local deformation and surface mesh.

[0155] The strain calculation procedure 370 receives local deformation of the vessel wall and local deformation of the dissociation flap at each phase of the cardiac cycle, corresponding to the local deformation of the true lumen and false lumen within the dissociated vessel.

[0156] In one or more embodiments, the strain calculation procedure 370 receives local deformation of the vessel wall and local deformation of the dissociation flap at each phase of the cardiac cycle from the motion tracking and mapping procedure 360. In one or more other embodiments, the strain calculation procedure 370 receives local deformation of the vessel wall and local deformation of the dissociation flap at each phase of the cardiac cycle from the database 235.

[0157] Strain calculation procedure 370 uses continuum mechanics techniques to calculate in vivo strain based on the local mechanics at each phase of the surface mesh and dissociation flap of the vascular wall. Strain calculation procedure 370 calculates the strain at each node of the surface mesh for each phase of the cardiac cycle, resulting in a strain distribution or strain map for each phase of the cardiac cycle.

[0158] As a non-limiting example, referring to one triangular element of the surface mesh in the first phase of the cardiac cycle, the reference coordinate system is defined by the center of Node 1, three vectors defined as A1 from Node 1 to Node 2, A2 from Node 1 to Node 3, and A3 which is the unit vector perpendicular to the first two. These nodes and vectors define the undeformed reference configuration of the triangular element. Next, A1, A2, and A3 are mapped to the corresponding spatial vectors a1, a2, a, centered at the new position of Node 1 in the current deformed configuration, for each subsequent phase of the cardiac cycle. Due to the additional tissue non-compressibility constraints imposed on the in-plane vectors (A1, A2, and a1, a2) and the out-of-plane vector a3 known from mesh tracking through each subsequent phase, all components of the deformation gradient tensor F are calculated based on the system of equations [a k i =F i I [A k I where k = 1, 2, 3, [A k I is the I-th component of A k , [a k i is the i-th component of a k , and F i I is the i-th component of the tensor F. One skilled in the art will understand that this example refers to triangular shell elements, but shell elements having shapes other than triangular may also be used.

[0159] In one embodiment, the strain calculation procedure 370 takes the deformation gradient F, calculates the right Cauchy - Green deformation tensor C = F T F and the non - linear Green - Lagrange strain tensor as E = 1 / 2×(C - I), and then diagonalizes this to obtain the principal strain values.

[0160] ​​​​The strain calculation procedure 370 calculates principal strain values ​​along the principal strain direction as eigenvalues ​​of the diagonalized Green-Lagrange strain tensor. In one or more embodiments, the strain calculation procedure 370 determines principal strain values ​​along the principal strain direction of the vessel wall surface mesh and the dissection flap to obtain a distribution of strain measurements or strain maps at each phase of the cardiac cycle that represent the relative displacement between the outer wall region of the vessel and the dissection flap region. The strain calculation procedure 370 outputs a set of strain maps of the cardiac cycle, each strain map containing principal strain values ​​corresponding to each phase of the cardiac cycle.

[0161] In one or more embodiments, the strain calculation procedure 370 determines the projection of strain in the principal curvature direction to obtain circumferential and axial strain values ​​for each displacement node tracked on the surface mesh of the dissociated blood vessels.

[0162] In one or more embodiments, the strain calculation procedure 370 determines the maximum strain map by using a set of strain maps. The strain calculation procedure 370 determines the maximum principal strain value as the maximum of three principal strain values ​​along the principal strain direction at each phase of the cardiac cycle in order to obtain the distribution of the maximum principal strain measurement or maximum principal strain map over the cardiac cycle of the surface mesh of the vascular wall and the anatomical flap.

[0163] Therefore, the strain calculation procedure 370 outputs at least one of a set of strain maps containing principal strain values ​​over the cardiac cycle, and a maximum principal strain map containing the maximum principal strain value over the cardiac cycle.

[0164] It will be understood that the number of strain maps in a set of strain maps is not limited and depends on the number of phases queried across the cardiac cycle during the image acquisition procedure 310.

[0165] Please understand that the number of principal strain or maximum principal strain values ​​is not limited and depends on the number of displacement nodes that define the tracking surface mesh.

[0166] As a non-limiting example, in one embodiment where the ECG gate-type dynamic acquisition procedure generates a multiphase DICOM stack containing 10 phases, the strain calculation procedure 370 outputs 10 strain maps, one for each phase of the cardiac cycle, and also outputs a final map of the maximum principal strain over the cardiac cycle.

[0167] As another non-limiting example, in one or more embodiments in which the image acquisition procedure 310 includes a low-resolution dynamic MRI acquisition, the final strain map may be used in combination with one or more of the following to better identify the presence and location of small entry and / or re-entry cracks along the intima-dissociation flap: T1-weighted spin-echo (black blood) MRI acquisition, 4D flow MRI acquisition, and phase-contrast MRI acquisition.

[0168] Interactive model generation The interactive model generation procedure 380 is configured, in particular, to (i) receive at least one of a set of strain maps and a maximum strain map of the dissected vessel over the cardiac cycle, (ii) receive a 3D geometric model of the dissected vessel, and (iii) generate an interactive model of the dissected vessel using the 3D geometric model of the dissected vessel and at least one of the set of strain maps and the maximum strain map.

[0169] Interactive model generation procedure 380 generates an interactive model of the dissected vessel, including the strain values ​​for each phase of the cardiac cycle, using 3D geometric models and strain maps at different points in the cardiac cycle.

[0170] Additionally or alternatively, interactive model generation procedure 380 generates an interactive model of the dissected vessel, including the maximum strain value for the entire cardiac cycle, using a 3D geometric model and a maximum strain map. It will be understood that the maximum strain map is determined based on a set of strain maps.

[0171] In one or more embodiments, the interactive model generation procedure 380 overlays the final distortion map onto the original image acquisition or, if available, any of the additional acquisitions described above, so that the image fusion and joining information is displayed simultaneously.

[0172] In one or more embodiments, the interactive model includes a 3D model of the dissected vessel, and the distortion at different locations on the dissected vessel can be visualized at different times during the cardiac cycle. The interactive model generation procedure 380 can use different types of visual indicators to color-code different values ​​of distortion and enable the display of distortion.

[0173] The interactive model allows for the display of strain maps of the aortic wall and dissection flap at each phase of the cardiac cycle, enabling comparison between dissected and non-dissected areas of the aorta (if present on the image) to assist in the evaluation of individual aortic dissections and the distinction between true and false lumens.

[0174] Furthermore, using an interactive model, it is possible to visualize dissected vessels, including true lumen, false lumen, and strain maps, at different angles, locations, and levels of detail, display the images used to generate a 3D representation of the dissected vessel, and display information about the dissected vessel and a given patient, as well as any other relevant information that can be used by healthcare professionals to evaluate the dissected vessel.

[0175] In one or more embodiments, the AD strain mapping procedure 300 is repeated for a given patient at a subsequent time (i.e., after acquiring new images of the dissected vessels of a given patient), and the results (i.e., strain maps and geometric models) may be included in an interactive model so that the temporal progression of the strain and the geometric shape of the dissected vessels can be evaluated and compared.

[0176] Interactive model generation procedure 380 outputs an interactive model of the dissected vessel, including a strain map over the cardiac cycle.

[0177] In one or more embodiments, the interactive model generation procedure 380 transmits an interactive model for display on a display interface, such as on the display interface 140 of a workstation computer 215 or an electronic device 100.

[0178] In one or more embodiments, the AD strain mapping procedure 300 includes an AD strain analysis procedure 390.

[0179] AD Distortion Analysis The AD strain analysis procedure 390 is configured to perform further analysis using the strain map of the dissected blood vessels output by the strain calculation procedure 370 and the interactive model generation procedure 380.

[0180] In one or more embodiments, the AD strain analysis procedure 390 may use one or more machine learning (ML) models trained to perform aortic dissection analysis. As a non-limiting example, one or more such ML models may be trained on the output of the AD strain mapping procedure 300 in combination with clinical and medical data.

[0181] In one or more embodiments, the AD strain analysis procedure 390 performs an analysis of strain maps of the aortic wall and dissection flap at each phase of the cardiac cycle to enable comparison between dissected and non-dissected areas of the aorta (if present on the image) in order to assist in the evaluation of individual aortic dissections and the distinction between true and false lumens.

[0182] In one or more embodiments, the AD strain analysis procedure 390 performs an analysis of strain maps of the aortic wall and dissection flap at each phase of the cardiac cycle to provide information on the mobility of the dissection flap and to identify false lumen compression and true lumen compression throughout the cardiac cycle, in order to assist in clinical evaluation for diagnostic and disease management purposes.

[0183] In one or more embodiments, the AD strain analysis procedure 390 performs an analysis of strain maps of the dissection flap at each phase of the cardiac cycle to identify regional fragmentation near the crack and at the crack margin that may predict crack propagation and crack expansion that promotes increased false lumen flow and patency, and thus assist in the clinical evaluation and diagnosis of individual dissections.

[0184] In one or more embodiments, the AD strain analysis procedure 30 performs an analysis of strain maps of the aortic wall and dissection flap over the cardiac cycle with one or more follow-up scans compared to a baseline scan to evaluate the temporal progression of strain and identify rapid changes in local strain indicating rapid degenerative and weakening processes that are likely to adversely affect disease progression, thus supporting the clinical evaluation and diagnosis of individual dissections.

[0185] The AD strain mapping procedure 300 allows for the visualization and evaluation of interactions between the channels created by the dissociation flap (e.g., true lumen and false lumen), including the presence of blood in the false lumen, which can lead to complications resulting from pressurizing the false lumen, compressing the true lumen over the cardiac cycle, and restricting blood supply to downstream organs. The AD strain mapping procedure 300 allows for the identification and identification of crack points within the dissociated vessel using strain, which can then be used to predict further cracks, as well as to understand the interactions between the true lumen and false lumen within the dissociated vessel.

[0186] The AD strain mapping procedure 300 assumes uniform material properties of the aorta, eliminating the need for shear stress calculations, thickness calculations, and computational fluid dynamics (CFD) or fluid-structure interaction (FSI) simulations, and thus providing a more efficient and realistic assessment of strain within dissected vessels.

[0187] In one embodiment, computational fluid dynamics (CFD) simulations may be used in conjunction with strain maps to estimate true and false lumen blood flow when an obvious false lumen flow is identified, and to provide complementary information regarding hemoperfusion to organs downstream of the dissociation.

[0188] Method explanation Figure 6 shows a flowchart of method 600 for generating a strain map of dissected blood vessels, method 600 can be implemented by one or more non-limiting embodiments of the present technology.

[0189] Method 600 is performed by a processing device. For example, Method 600 may be performed by a server 230 or a workstation computer 215. In one embodiment, the server 230 comprises a processor 110 and / or a GPU 111 operably connected to a non-temporary storage medium such as a solid-state drive 120 and / or random-access memory 130 that stores computer-readable instructions. The processing device is configured to perform Method 600 when it has executed a computer-readable instruction.

[0190] It should be noted that method 600 may be performed by multiple electronic devices. Method 600 begins with processing step 602.

[0191] In processing step 602, the processing device receives multiple images of a given subject's blood vessels during the cardiac cycle, acquired by using an ECG-gate type medical imaging device such as a medical imaging device 210.

[0192] In one or more other embodiments, multiple images are received from at least one of a workstation computer 215, a database 235, and a medical imaging device 210.

[0193] In processing step 604, the processing device organizes multiple images into a multiphase stack. In one or more embodiments, a workstation computer 215 can organize multiple images into a multiphase stack and transmit the multiphase stack to a server 230. A given phase of the multiphase stack represents a dissected vessel at a given time in the cardiac cycle.

[0194] In processing step 606, the processing device generates a 3D geometric model of the dissected vessel by segmenting the multiphase stack. The 3D geometric model includes the walls and dissection flaps of the dissected vessel. The processing device generates the 3D geometric shape based on a first phase of the multiphase stack, which corresponds to the time of the cardiac cycle, identified as phase 0.

[0195] In one or more embodiments, the processing device uses one or more machine learning models to perform segmentation of multiple images and / or multiphase stacks to obtain a 3D geometric model of the dissected vessel, including the true lumen and false lumen defined by the dissecting flap. In one or more other embodiments, the processing device receives markings of the dissecting flap and the aortic wall to generate a 3D geometric model of the dissected vessel.

[0196] In processing step 608, the processing device uses a 3D geometric model to generate a surface mesh of at least a portion of the dissociated blood vessels in the first phase of the multiphase stack, the surface mesh including a wall surface mesh and a dissociated flap surface mesh.

[0197] In one or more embodiments, the surface mesh of the dissected blood vessel includes a surface mesh of the true lumen and a surface mesh of the false lumen of the dissected blood vessel.

[0198] In one or more embodiments, the processing device smooths and meshes the 3D geometric model of the dissociated blood vessels in order to obtain a surface mesh of the 3D geometric model of the dissociated blood vessels. In one embodiment, the surface mesh of the 3D geometric model of the dissociated blood vessels is in the form of a discretized geometric shape of small triangular elements.

[0199] In processing step 610, the processing device uses the surface mesh and multiphase stack of at least a portion of the dissected blood vessels to determine the local deformation in each phase of the multiphase stack by mapping the voxels of the surface mesh to the multiphase stack.

[0200] In one or more embodiments, the processing device maps each voxel position of 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 each node position of the geometric shape in the first phase is associated with the node positions corresponding to all subsequent phases. The processing device determines the node displacements across the entire cardiac cycle, i.e., across different phases. The processing device then uses the node displacements across the entire cardiac cycle to determine the local deformations of each phase of the multiphase stack, i.e., at different times during the cardiac cycle. The local deformations indicate the node displacements and allow monitoring of the position of portions of the surface of true and false lumens as blood flows through dissected vessels during the cardiac cycle of a given patient.

[0201] Method 600 proceeds to processing step 612. In processing step 612, the processing device uses the local deformation in each phase and the surface mesh of the vessel wall and the surface mesh of the dissociation flap to generate a set of strain maps and a given strain map of a set of strain maps that include principal strain values ​​in the surface mesh of the vessel wall and the surface mesh of the dissociation flap at a given phase of the cardiac cycle. The set of strain maps includes at least one strain map for a given phase of the cardiac cycle. In one or more embodiments, the strain map includes axial strain values ​​and circumferential strain values ​​of the dissociated vessel.

[0202] In one or more embodiments, the set of strain maps includes strain maps for each phase of the cardiac cycle.

[0203] In one or more embodiments, method 600 proceeds to processing step 616. In one or more alternative embodiments, method 600 may end at processing step 612.

[0204] In processing step 614, the processing device generates a maximum strain map showing the maximum principal strain value in the surface mesh of the vascular wall and the surface mesh of the dissociation flap over the cardiac cycle, based on a set of strain maps. To obtain a maximum principal strain map of the surface mesh of the vascular wall and the dissociation flap over the cardiac cycle, the processing device determines the maximum principal strain value as the maximum of three principal strain values ​​at each phase of the cardiac cycle.

[0205] In one or more embodiments, method 600 proceeds to step 616. In one or more alternative embodiments, method 600 may end at processing step 614.

[0206] In processing step 616, the processing device generates an interactive model of the dissected blood vessel using a 3D geometric model and at least one of a set of strain maps and the maximum strain map.

[0207] In one or more embodiments, the processing device transmits an interactive model of the dissected blood vessel for display. In one or more embodiments, the processing device transmits an interactive model of the dissected blood vessel for display on a display screen such as a display interface 140 and / or an input / output interface 150.

[0208] Method 600 ends here. Processing steps 602-616 may be repeated at different times for a given patient to assess the progression of the dissected vessel. In one or more embodiments, the processing device uses strain maps to predict regional weakening in the dissected vessel. Additionally or alternatively, the processing device uses strain maps to predict the expansion of the dissection crack within the dissected vessel.

[0209] 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.

[0210] 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).

[0211] 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 method for generating a distortion map of dissected blood vessels of a given subject, wherein the method is performed by a processor, A step of receiving a multiphase stack generated from multiple images of the dissected blood vessel of a given subject, wherein each phase of the multiphase stack represents the dissected blood vessel at each point in the cardiac cycle, A step of generating a 3D geometric model of at least a portion of the dissociated blood vessel using at least a portion of the multiphase stack, wherein the 3D geometric model includes the walls and dissociation flaps of the dissociated blood vessel. A step of generating a surface mesh of at least the portion of the dissociated vessel for a given one of the phases of the multiphase stack using the 3D geometric model, wherein the surface mesh of at least the portion of the dissociated vessel includes a vessel wall surface mesh and a dissociation flap surface mesh. The steps include determining local deformation in each phase of the multiphase stack by mapping each node of the surface mesh of the dissociated blood vessel to each voxel of each phase of the multiphase stack using at least a portion of the surface mesh of the dissociated blood vessel and the multiphase stack, A step of generating a set of strain maps using the local deformation in each of the phases and the vessel wall surface mesh and the dissociated flap surface mesh, wherein a given strain map of the set of strain maps includes principal strain values ​​in the surface mesh of the dissociated vessel for the corresponding phase of the cardiac cycle. The step includes outputting the set of strain maps, The step of generating the set of strain maps is a method comprising the step of projecting strain in the principal curvature direction to obtain circumferential and axial strain values ​​on the surface mesh of the dissociated blood vessels for the given strain maps.

2. The step of generating the 3D geometric model of at least a portion of the dissociated blood vessels using the multiphase stack is: The method according to claim 1, comprising the steps of segmenting the multiphase stack to obtain segmented dissociated blood vessels, and using the segmented dissociated blood vessels to obtain the 3D geometric model.

3. The method according to claim 1, wherein the step of mapping each node of the surface mesh to each of the respective voxels of each of the phases of the multiphase stack is performed using an optical flow algorithm.

4. The method according to claim 1, wherein the 3D geometric model of at least a portion of the dissected blood vessel includes markings for true lumens and false lumens.

5. A step of evaluating the mobility of the dissected flap using the set of strain maps of the dissected blood vessel, The method according to claim 4, further comprising the step of identifying pressurization of the false lumen and compression of the true lumen over the cardiac cycle.

6. The 3D geometric model of at least a portion of the dissociated blood vessel further includes markings of the healthy, non-dissociated region of the blood vessel. The method according to claim 1, further comprising the step of the processor determining regional vulnerability of the dissected vessel using the set of strain maps of the dissected vessel over the cardiac cycle and the markings of the healthy non-dissected regions.

7. The method according to claim 1, further comprising the step of the processor using the set of strain maps of the dissociation flap to predict the expansion of the dissociation crack in the dissociated blood vessel.

8. The method according to claim 1, further comprising the step of repeating the method on a second multiphase stack of the dissociated vessels of a given subject acquired over subsequent time, thereby obtaining a further 3D geometric model of the dissociated vessels and a further set of strain maps for subsequent time.

9. The method of claim 8, further comprising the step of the processor using the set of strain maps and the further set of strain maps to predict at least one of further regional weakening in the dissected vessel and further expansion of the dissecting crack in the dissected vessel.

10. Processor and A non-temporary storage medium operably connected to the processor, wherein the non-temporary storage medium includes computer-readable instructions stored therein. Equipped with, When the processor executes the computer-readable instruction, Receiving a multiphase stack generated from multiple images of a dissected blood vessel of a given subject, wherein each phase of the multiphase stack represents the dissected blood vessel at each point in the cardiac cycle. Using at least a portion of the multiphase stack, generate a 3D geometric model of at least a portion of the dissected blood vessel, wherein the 3D geometric model includes the walls and dissection flaps of the dissected blood vessel. Using the 3D geometric model, generate a surface mesh of at least a portion of the dissociated vessel for one of the phases of the multiphase stack, wherein the surface mesh of at least a portion of the dissociated vessel includes a vessel wall surface mesh and a dissociation flap surface mesh. Using at least a portion of the surface mesh and the multiphase stack of the dissociated blood vessel, each node of the surface mesh of the dissociated blood vessel is the multiphase stack By mapping each phase to its respective voxel, the local deformation in each phase of the multiphase stack is determined, To generate a set of strain maps using the local deformation in each of the phases, the vessel wall surface mesh and the dissociated flap surface mesh, wherein a given strain map in the set of strain maps includes principal strain values ​​in the surface mesh of the dissociated vessel for the corresponding phase of the cardiac cycle. It is configured to output the aforementioned set of strain maps and to perform the following: The system for generating the set of strain maps includes projecting strain in the principal curvature direction to obtain circumferential and axial strain values ​​on the surface mesh of the dissociated blood vessels for the given strain map.

11. Using the multiphase stack, generating the 3D geometric model of at least a portion of the dissociated blood vessels is: The system according to claim 10, comprising segmenting the multiphase stack to obtain segmented dissociated blood vessels, and using the segmented dissociated blood vessels to obtain the 3D geometric model.

12. The system according to claim 10, wherein the mapping of each node of the surface mesh to each of the voxels of each of the phases of the multiphase stack is performed using an optical flow algorithm.

13. The system according to claim 10, wherein the 3D geometric model of at least a portion of the dissected blood vessel includes markings for true lumens and false lumens.

14. The aforementioned processor, The mobility of the dissected flap is evaluated using the set of strain maps of the dissected blood vessel, The system according to claim 13, further configured to perform the following: identify pressurization of the false lumen and compression of the true lumen over the cardiac cycle.

15. The 3D geometric model of at least a portion of the dissociated blood vessel further includes markings of the healthy, non-dissociated region of the blood vessel. The system according to claim 10, wherein the processor is further configured to determine regional vulnerability of the dissected vessels using the set of strain maps of the dissected vessels over the cardiac cycle and the markings of the healthy non-dissected regions.

16. The system according to claim 10, wherein the processor is further configured to use the set of strain maps to predict the expansion of a dissociative fissure in the dissociated blood vessel.

17. The system according to claim 10, wherein the processor is further configured to execute the computer-readable instructions on a second polyphase stack of the dissected blood vessels of a given subject acquired in subsequent time, thereby obtaining a further 3D geometric model of the dissected blood vessels and a further strain map of subsequent time.

18. The system according to claim 17, wherein the processor is further configured to use the set of strain maps and the further set of strain maps to predict at least one of further regional weakening in the dissected vessel and further expansion of the dissecting crack in the dissected vessel.

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