Method and system for identifying vessel deformations

Abstract

A method for identifying deformation of a target blood vessel is described, the method being executed by a processor and including: receiving an initial 3D model of the target blood vessel, the initial 3D model being generated from an initial medical image of the blood vessel acquired at an initial time point; receiving a subsequent 3D model of the target blood vessel, the subsequent 3D model being generated from a subsequent medical image of the blood vessel acquired at a subsequent time point; registering the initial 3D model with the subsequent 3D model, thereby obtaining an registered 3D model; and identifying at least one deformation parameter of the blood vessel based on the registered 3D model.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 485,560, filed February 17, 2023.

[0002] The present technology relates to the field of medical imaging, and more particularly to methods and systems for identifying deformations in blood vessels. [Background technology]

[0003] Measuring vascular geometric changes is a key component in understanding the progression and changes that blood vessels undergo over time. These changes can reflect the underlying pathophysiological state unique to each diseased vessel and can be a strong indicator of impending rupture in aortic aneurysms. The rate of vessel expansion has also been shown to be complex, and understanding deformation in three dimensions (3D) is important for capturing subtle differences in vascular growth when monitoring abdominal aortic aneurysms (AAAs). The accuracy of current radiology standards for measuring aneurysmal sac size is controversial. Reliability of manual measurements is estimated to be in the range of 3 mm to 4 mm, or ±10% of the normal aortic size. Geometric deformation artifacts can occur due to human error in aneurysmal sac size measurement and accuracy issues in various imaging modalities, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US). Errors can also be introduced into growth analysis through changes in acquisition parameters (resolution) or from cardiac motion over the cardiac cycle. Producing consistent and reproducible measurements of deformed vessels is important in understanding the progression of diseased vessels and eliminating erroneous measurements due to artifacts in image acquisition.

[0004] Therefore, a need exists for a method and system for identifying deformations in blood vessels. Summary of the Invention

[0005] The purpose of this technique is to provide automatic detection of vascular blood deformations, such as vascular growth.

[0006] The present technique minimizes these errors through the use of shape-based distributions to correct for both noise artifacts and high-dimensional geometric distances. In some embodiments, the purpose of the present technique is to provide a method that can be applied to 3D images of the vasculature to detect changes in the geometric characteristics of the vessel in terms of changes and deformations in the diameter of the aortic wall and lumen. The technique also provides local measurements of deformation and evolution of localized changes in vessel shape. Localized changes can be measured using consistent landmarks in the shape available at each time point, along with deformations to the structure of the vessel itself.

[0007] In at least some embodiments, two 3D models of a blood vessel are generated from two images or scans captured at different times, and the registration between the two 3D models is used to assess local shape changes to the blood vessel over time. These changes can be used to observe changes in the local structure of the blood vessel, while the registration is used to generate shape-based estimates of local surface deformation and distance through subsequent scans. The use of shape- and probability-based surface deformation estimation to generate correspondences between points on the 3D models allows for comparison of multiple image acquisitions, i.e., blood vessel deformation can be compared between CT, MR, ultrasound, and / or any device that generates 3D images.

[0008] In at least some embodiments, to measure the surface deformation of the vascular structure, a deformation gradient is calculated from the change in the 3D model of the vessel from the initial scan to the subsequent scan.

[0009] In at least some embodiments, landmarks along the vessel geometry, such as arterial bifurcations, are used to obtain relative positions of measurements and align measurements from the initial scan to subsequent scans. These measurements may include measurements and distributions of maximum, minimum, and / or mean vessel diameters. Additionally, volumetric changes of both lumen and wall geometry may be calculated based on the volume contained within a given landmark in both scans.

[0010] This technology makes it possible to automatically identify changes in blood vessels between two 3D images of blood vessels taken at different times, without needing intensity, pixel, or voxel information.

[0011] By performing the analysis on a 3D model generated based on acquired images, the registration step can be applied to any imaging device where reconstructions are created from segmented structures. As a result, the technique is device-independent; that is, it can be used across devices such as CT, MR, and ultrasound. The technique also simplifies and provides a method for comparing images across imaging devices.

[0012] This technology allows clinicians to reliably assess localized changes in vessel geometry over time. For example, identifying high localized growth in the neck or scaffolding zone of the aorta and / or iliac arteries may assist clinicians in determining whether an EVAR stent is likely to migrate or develop an endoleak. As another example, identifying high localized growth in an aortic aneurysm with a maximum diameter below that typically treated may indicate the need for treatment to avoid eventual rupture.

[0013] According to a first broad aspect, there is provided a method for identifying deformation of a blood vessel of a subject, the method being executed by a processor and including: receiving an initial 3D model of the subject's blood vessel, the initial 3D model being generated from an initial medical image of the blood vessel acquired at an initial time point; receiving a subsequent 3D model of the subject's blood vessel, the subsequent 3D model being generated from a subsequent medical image of the blood vessel acquired at a subsequent time point; registering the initial 3D model with the subsequent 3D model, thereby obtaining an registered 3D model; and identifying at least one deformation parameter of the blood vessel based on the registered 3D model.

[0014] In one or more embodiments, the step of receiving the subsequent 3D model includes receiving a subsequent medical image of the blood vessel; segmenting the subsequent medical image, thereby obtaining a subsequent segmented image; and generating a subsequent 3D model of the blood vessel based on the subsequent segmented image.

[0015] In one or more embodiments, the step of receiving the subsequent 3D model includes receiving an initial medical image of the blood vessel; segmenting the initial medical image, thereby obtaining an initial segmented image; and generating an initial 3D model of the blood vessel based on the initial segmented image.

[0016] In one or more embodiments, the initial 3D model and the subsequent 3D model each include a 3D surface model.

[0017] In one or more embodiments, the 3D surface model includes a polygonal model.

[0018] In one or more embodiments, the step of registering the initial 3D model to the subsequent 3D model is performed using a fixed registration method and a deformable registration method.

[0019] In one or more embodiments, the step of registering the initial 3D model to the subsequent 3D model is performed using a fixed registration method, an affine registration method, and a deformable registration method.

[0020] In one or more embodiments, the method further includes identifying landmarks in the 3D initial model and the 3D subsequent model.

[0021] In one or more embodiments, the step of registering the initial 3D model with respect to the subsequent 3D model is performed based on the identified landmarks.

[0022] In one or more embodiments, the method further includes scaling one of the 3D initial model and the 3D subsequent model based on the identified landmarks.

[0023] In one or more embodiments, the step of aligning the initial 3D model with respect to the subsequent 3D model includes identifying a first centerline of the initial 3D model; identifying a second centerline of the subsequent 3D model; and aligning the first centerline with respect to the second centerline.

[0024] In one or more embodiments, the step of determining at least one deformation includes determining a deformation gradient based on the registered 3D models.

[0025] In one or more embodiments, the step of identifying at least one transformation includes identifying given transformation parameters for the initial 3D model and identifying given transformation parameters for the subsequent 3D model.

[0026] In one or more embodiments, the given deformation parameter comprises at least one of diameter, volume, length, surface area, equivalent circle diameter, and asymmetry.

[0027] According to another broad aspect, there is provided a system for identifying deformations in a blood vessel of a subject, the system including: a processor; and a non-transitory storage medium operably connected to the processor, the non-transitory storage medium including computer-readable instructions stored on the storage medium; the processor, when executing the computer-readable instructions, is configured to: receive an initial 3D model of the subject's blood vessel, the initial 3D model generated from an initial medical image of the blood vessel acquired at an initial time point; receive a subsequent 3D model of the subject's blood vessel, the subsequent 3D model generated from a subsequent medical image of the blood vessel acquired at a subsequent time point; register the initial 3D model with the subsequent 3D model, thereby obtaining a registered 3D model; and identify at least one deformation of the blood vessel based on the registered 3D model.

[0028] In one or more embodiments, the processor is further configured to receive a subsequent medical image of the blood vessel; segment the subsequent medical image, thereby obtaining a subsequent segmented image; and generate a subsequent 3D model of the blood vessel based on the subsequent segmented image.

[0029] In one or more embodiments, the processor is further configured to receive an initial medical image of the blood vessel; segment the initial medical image, thereby obtaining an initial segmented image; and generate an initial 3D model of the blood vessel based on the initial segmented image.

[0030] In one or more embodiments, the initial 3D model and the subsequent 3D model each include a 3D surface model.

[0031] In one or more embodiments, the 3D surface model includes a polygonal model.

[0032] In one or more embodiments, the processor is configured to register the initial 3D model to the subsequent 3D model using a fixed registration method and a deformable registration method.

[0033] In one or more embodiments, the processor is configured to register the initial 3D model to the subsequent 3D model using a fixed registration method, an affine registration method, and a deformable registration method.

[0034] In one or more embodiments, the processor is further configured to identify landmarks in the 3D initial model and the 3D subsequent model.

[0035] In one or more embodiments, the processor is configured to align the initial 3D model with the subsequent 3D model based on the identified landmarks.

[0036] In one or more embodiments, the processor is further configured to scale one of the initial 3D model and the subsequent 3D model based on the identified landmarks.

[0037] In one or more embodiments, the processor is configured to identify a first centerline for the initial 3D model; identify a second centerline for the subsequent 3D model; and align the first centerline with the second centerline.

[0038] In one or more embodiments, determining the at least one deformation includes determining a deformation gradient based on the registered 3D models.

[0039] In one or more embodiments, the processor is configured to identify given deformation parameters for the initial 3D model and to identify given deformation parameters for the subsequent 3D model.

[0040] In one or more embodiments, the given deformation parameter comprises at least one of diameter, volume, length, surface area, equivalent circle diameter, and asymmetry.

[0041] A.Definition

[0042] In the context of this specification, a "server" is a computer program running on appropriate hardware that can receive requests (e.g., from electronic devices) over a network (e.g., a communications network) and fulfill those requests or cause those requests to be fulfilled. The hardware may be a physical computer or a physical computer system, but need not be either, in the context of the present technology. In this context, the use of the term "server" is not intended to imply that all tasks (e.g., received instructions or requests) or any particular task will be received, executed, or caused to be executed by the same server (i.e., the same software and / or hardware). It is intended to mean that any number of software elements or hardware devices may be involved in receiving / sending, executing, or causing to be executed any task or request, or the result of any task or request. All this software and hardware may be one server or multiple servers, both of which are included in the terms "at least one server" and "server."

[0043] In the context of this specification, an "electronic device" is any computer-related device or computer hardware capable of executing software appropriate for the relevant task at hand. Thus, some (non-limiting) 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. It should be noted that an electronic device in this context does not exclude acting as a server for other electronic devices. The use of the expression "electronic device" does not exclude multiple electronic devices from being used to receive / send, execute, or cause to be executed a task or request, or the results of a task or request, or any method step, described herein. In the context of this specification, a "client device" refers to any of a variety of end-user client electronic devices associated with a user, such as a personal computer, tablet, smartphone, etc.

[0044] In the context of this specification, the expression "computer-readable storage medium" (also referred to as "storage medium" and "storage") is intended to include non-transitory media of whatever nature and type, including but not limited to RAM, ROM, disks (CD-ROM, DVD, floppy disk, hard drive, etc.), USB keys, solid state drives, tape drives, etc. A computer information storage medium may be formed by combining multiple components, including two or more media components of the same type and / or two or more media components of different types.

[0045] In the context of this specification, a "database" is any structured collection of data, regardless of its particular structure, database management software, or computer hardware on which the data is stored, implemented, or otherwise made available. 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.

[0046] In the context of this specification, the expression "information" includes information of any nature or type that can be stored in a database, and thus includes, but is not limited to, audiovisual works (images, films, sound recordings, presentations, etc.), data (location data, numerical data, etc.), text (opinions, comments, questions, messages, etc.), documents, spreadsheets, word lists, etc.

[0047] In the context of this specification, unless explicitly provided otherwise, a "representation" of an information element may be the information element itself, or it may be a pointer, reference, link, or other indirection mechanism that allows the recipient of the representation to locate a network, memory, database, or other computer-readable medium location where the information element can be obtained. For example, a representation of a document may include the document itself (i.e., its contents), a unique document descriptor that identifies the file with respect to a particular file system, or other means that directs the recipient of the representation to a network location, memory address, database table, or other location where the file can be accessed. As those skilled in the art will recognize, the degree of precision required in such a representation depends on the degree of prior understanding of any interpretation to be given to the information exchanged between the sender and recipient of the representation. For example, if, prior to communication between the sender and recipient, a representation of an information element is known to take the form of a database key for an entry in a particular table of a given database that contains the information element, then merely transmitting the database key can effectively convey the information element to the recipient, even though the information element itself has not been transmitted between the sender and recipient of the representation.

[0048] In the context of this specification, the expression "communications network" is intended to include telecommunications networks such as computer networks, the Internet, telephone networks, telex networks, TCP / IP data networks (e.g., WAN networks, LAN networks, etc.), etc. The term "communications network" includes wired networks or direct-wired connections, wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media, and combinations of any of the above.

[0049] In the context of this specification, words such as "first," "second," and "third" are used as adjectives solely to distinguish between the nouns they modify, and not to describe a particular relationship between those nouns. Thus, for example, it should be understood that the use of the terms "server" and "third server" is not intended to suggest a particular order, type, chronology, hierarchy, or (e.g.) ranking between servers, nor is their use (in and of itself) intended to suggest that a "second server" must necessarily be present in any given situation. Furthermore, as described elsewhere herein, reference to a "first" element and a "second" element does not exclude the two elements from being the same actual, real-world element. Thus, for example, in some cases, the "first" server and the "second" server may be the same software and / or hardware, while in other cases, they may be different software and / or hardware.

[0050] Each embodiment of the present technology will have at least one, but not necessarily all, of the above-described objects and / or aspects, and it should be understood that some aspects of the present technology that arise from seeking to achieve the above-described object may not meet that object and / or may meet other objects not specifically recited herein.

[0051] Additional and / or alternative features, aspects, and advantages of embodiments of the present technology will become apparent from the following. For a better understanding of the present technology, as well as other aspects and further features thereof, reference is made to the following description taken in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0052] [Figure 1] 1 shows a schematic diagram of an electronic device in accordance with one or more non-limiting embodiments of the present technology. [Figure 2]1 shows a schematic diagram of a communication system in accordance with one or more non-limiting embodiments of the present technology; [Figure 3] 3 shows a schematic diagram of a vascular deformation detection procedure performed within the system of FIG. 2 in accordance with one or more non-limiting embodiments of the present technology. [Figure 4] (A) Shows the initial 3D model of the aorta and the subsequent 3D model of the aorta during the alignment process. (B) Shows the centerlines of the initial and subsequent 3D models of Figure 4A before and after the alignment procedure. [Figure 5] We show that a common shape space defined by the vessel centerlines is used to obtain a subsection of the dataset. [Figure 6] 1 shows the distribution of diameter measurements at a single level of the vessel shape taken at a given point along its centerline. [Figure 7A] 1 shows the distribution of changes in growth measurements generated for a vessel measured at a given number of divisions in a direction perpendicular to the vessel centerline. [Figure 7B] 1 shows the distribution of changes in growth measurements generated for a vessel measured at a given number of divisions in a direction perpendicular to the vessel centerline. [Figure 7C] 1 shows the distribution of changes in growth measurements generated for a vessel measured at a given number of divisions in a direction perpendicular to the vessel centerline. [Figure 8] 10 shows a growth measure applied to multiple data points on a model, connections, polygons, and regions of the model. [Figure 9] 1 shows an exemplary initial 3D model of a vessel registered against a subsequent 3D model of the same vessel. [Figure 10] 1 shows exemplary variations in deformation parameters of a blood vessel. [Figure 11] 10 shows exemplary variations of deformation parameters for different views of the perivascular area. [Figure 12] (A) Exemplary circles placed along the length of a vessel, each defining a landmark for defining a longitudinal segment. (B) Longitudinal segmentation of a vessel obtained using the circles in Figure 12A. DETAILED DESCRIPTION OF THE INVENTION

[0053] The examples and conditional language described herein are intended primarily to aid the reader in understanding the principles of the present technology, and are not intended to limit the scope of the present technology to such specifically described examples and conditions. It will be understood that those skilled in the art may devise various configurations that, although not explicitly described or shown herein, embody the principles of the present technology and are included within its spirit and scope.

[0054] Furthermore, to aid in understanding, the following description may describe relatively simplified implementations of the technology. Those skilled in the art will appreciate that various implementations of the technology may be more complex.

[0055] In some cases, what are believed to be useful examples of modifications to the technology may also be described. This is done merely to aid in understanding and, again, does not define the scope or delimit the technology. These modifications are not an exhaustive list, and one of ordinary skill in the art may nonetheless make other modifications while remaining within the scope of the technology. Furthermore, if examples of modifications are not described, it should not be construed that modifications are not possible and / or that what is described is the only way to implement that element of the technology.

[0056] Furthermore, all statements herein reciting principles, aspects, and implementations of the present technology, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof, whether now known or developed in the future. Thus, for example, those skilled in the art will understand that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the present technology. Similarly, flowcharts, flow diagrams, state transition diagrams, pseudocode, and the like, may be substantially embodied on computer-readable media and will be understood to represent various processes that may be executed by such a computer or processor, whether or not a computer or processor is explicitly depicted.

[0057] The functionality of the various elements shown in the figures may be provided through the use of dedicated hardware and hardware capable of executing software in conjunction with appropriate software, including any functional blocks labeled as a "processor" or "graphics processing unit." When provided by a processor, the functionality may be provided by a single dedicated processor, by a single shared processor, or by multiple individual processors, some of which may be shared. In some non-limiting embodiments of the present technology, the processor may be a general-purpose processor such as a central processing unit (CPU), or a specialized processor for a specific purpose such as a graphics processing unit (GPU). Furthermore, explicit use of the terms "processor" or "controller" should not be construed as referring solely to hardware capable of executing software; it may implicitly include, without limitation, 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 for storing software. Other hardware, conventional and / or custom, may also be included.

[0058] Software modules, or simply modules implied to be software, may be represented herein as any combination of flowchart elements or other elements showing the execution and / or textual descriptions of process steps. Such modules may be executed by explicitly or implicitly shown hardware.

[0059] With these basic elements in place, we now turn to a few non-limiting examples illustrating various implementations of aspects of the present technology.

[0060] Referring to FIG. 1, a schematic diagram of an electronic device 100 suitable for use with some non-limiting embodiments of the present technology is shown.

[0061] Electronic Devices

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

[0063] Communication between the various components of 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.) to which the various hardware components are electronically coupled.

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

[0065] According to an embodiment of the present technology, solid state drive 120 stores program instructions suitable for loading into random access memory 130 and execution by processor 110 and / or GPU 111 for performing in vivo strain mapping of aortic dissection. For example, the program instructions may be part of a library or an application.

[0066] 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 device configured to implement the present technology, as may be understood by one skilled in the art.

[0067] system

[0068] Referring to FIG. 2 , a schematic diagram of a communication system 200 is shown, which will be referred to as system 200, and which is suitable for implementing a non-limiting embodiment of the present technology. It should be clearly understood that the illustrated system 200 is merely an exemplary implementation of the present technology. Accordingly, the description that follows is intended solely as a description of an exemplary example of the present technology. This description is not intended to define the scope or delimit the present technology. In some cases, what are believed to be useful examples of modifications to system 200 are described below. This is done merely to aid in understanding and, again, is not intended to define the scope or delimit the present technology. These modifications are not an exhaustive list, and other modifications are likely possible, as would be understood by one skilled in the art. Furthermore, if a modification is not made (i.e., if no example of a modification is mentioned), it should not be interpreted as meaning that the modification is not possible and / or that what is described is the only way to implement that element of the present technology. As would be understood by one skilled in the art, this is likely not the case. Furthermore, it will be understood that system 200 may, in certain instances, provide simple implementations of the present technology, and in such cases, they are presented in this manner to aid in understanding. As will be appreciated by those skilled in the art, various implementations of the present technology may be more complex.

[0069] The system 200 includes, among other things, a medical imaging device 210 associated with a workstation computer 215 and a server 230 each coupled in a communications network 220 via communications lines 225 (not individually numbered).

[0070] medical devices

[0071] The medical imaging device 210 is configured, among other things, to acquire multiple images of a given target's blood vessel at different times so that a 3D representation of the given target's blood vessel may then be generated.

[0072] In one or more embodiments, the medical imaging device 210 includes an ECG-gated medical imaging device.

[0073] The medical imaging device 210 may include one of a computed tomography (CT) scanner, a magnetic resonance imaging (MRI) scanner, a 3D ultrasound, or the like.

[0074] In some embodiments of the present technology, the medical imaging device 210 may include multiple medical imaging devices, such as a CT scanner, an MRI scanner, a 3D ultrasound scanner, and the like.

[0075] The medical imaging device 210 may be configured with specific acquisition parameters to acquire multiple images of the blood vessels over the cardiac cycle.

[0076] 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 including preoperative retrospective gated multi-detector CT (MDCT—64-row multi-slice CT scanner) with variable dose radiation may be used to capture the RR intervals.

[0077] As another non-limiting example, in one or more embodiments in which the medical imaging procedure includes an MRI scanner, the MR protocol can include a steady-state T2-weighted fast field echo (TE=2.6 ms, TR=5.2 ms, flip angle 110 degrees, fat suppression (SPIR), echo time 50 ms, maximum 25 cardiac phases, matrix 256×256, acquisition voxel MPS (measurement, phase, and slice encoding directions) 1.56 / 1.56 / 3.00 mm and reconstruction voxel MPS 0.78 / 0.78 / 1.5), or similar cine acquisition of axial slices of a portion of the aorta under study. The medical imaging device 210 includes, or is connected to, a workstation computer 215 for data transmission, among other things.

[0078] Workstation Computer

[0079] The workstation computer 215 is configured to, among other things: (i) control parameters of the medical imaging device 210 and perform image acquisition; and (ii) receive and process images from the medical imaging device 210 .

[0080] In one or more embodiments, the workstation computer 215 may receive the images in raw data format and perform tomographic reconstruction using known algorithms and software.

[0081] Implementations of workstation computer 215 are known in the art. Workstation computer 215 may be implemented as electronic device 100 or include its components, such as processor 110, graphics processing unit (GPU) 111, solid-state drive 120, random access memory 130, display interface 140, and input / output interface 150.

[0082] In one or more other embodiments, the workstation computer 215 may be at least partially integrated into the medical imaging device 210 .

[0083] In one or more embodiments, the workstation computer 215 is configured in accordance with the Digital Imaging and Communications in Medicine (DICOM) standard for communication and management of medical imaging information and associated data.

[0084] In one or more embodiments, the workstation computer 215 may store the images in a local database (not shown).

[0085] The workstation computers 215 are each connected to a server 230 through a communications network 220 via communications lines 225. In one or more embodiments, the workstation computers 215 may transmit images and / or multi-phase stacks to the server 230 and database 235 for storage and processing thereof.

[0086] In one or more embodiments, a multi-phase stack includes multiple 3D images, each acquired at a different time or phase, where at each phase, the 3D image includes multiple voxels, each having an associated 3D position and a parameter value, such as brightness, grayscale value, intensity value, etc.

[0087] server

[0088] The server 230 may, among other things: (i) receiving an initial 3D model of a target vessel, the initial 3D model being generated from an initial medical image of the vessel acquired at an initial time point; (ii) receiving a subsequent 3D model of the target vessel, the subsequent 3D model being generated from a subsequent medical image of the vessel acquired at a subsequent time point, i.e., the subsequent medical image being acquired at a second time point that is later than (or occurs after) the first time point at which the initial medical image was acquired; (iii) registering the initial 3D model with respect to the subsequent 3D model, thereby obtaining a registered initial model; and (iv) determining at least one deformation of the vessel based on the registered initial model and the subsequent 3D model.

[0089] As described in more detail below, server 230 may also be configured to generate an initial 3D model of the vessel and subsequent 3D models of the vessel. In another embodiment, the generation of the 3D models may be performed by a server other than server 230.

[0090] How server 230 is configured to do so is described in more detail later in this specification.

[0091] Server 230 may be implemented as a conventional computer server and may include some or all of the components of electronic device 100 shown in FIG. 2. In one example of one or more implementations of the present technology, server 230 may be implemented as a Dell™ PowerEdge™ server running the Microsoft™ Windows Server™ operating system. Of course, server 230 may be implemented in any other suitable hardware and / or software and / or firmware, or combination thereof. In the illustrated, non-limiting embodiment of the present technology, server 230 is a single server. In an alternative, non-limiting implementation of the present technology, the functionality of server 230 may be distributed and implemented across multiple servers (not shown).

[0092] Implementations of server 230 are well known to those skilled in the art. However, briefly described, server 230 includes a communication interface (not shown) structured and configured to communicate with various entities (e.g., workstation computer 215 and other devices that may be coupled to network 220) via communication network 220. Server 230 further includes at least one computer processor (e.g., processor 110 or GPU 111 of electronic device 100) operatively coupled to the communication interface and structured and configured to execute the various processes described herein.

[0093] In one or more embodiments, server 230 may be implemented as electronic device 100 or may include its components, such as processor 110, graphics processing unit (GPU) 111, solid-state drive 120, random access memory 130, display interface 140, and input / output interface 150.

[0094] Database

[0095] 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 communications network 220 without departing from the teachings of the present technology. Although database 235 is shown generally herein as a single entity, it will be understood that database 235 may be configured in a distributed manner, e.g., database 235 may have different components, each configured to perform a particular type of retrieval from or storage to it.

[0096] Database 235 may be a structured collection of data, regardless of its particular 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 the processes that store or utilize the information stored in database 230, such as server 230, or may reside on separate hardware, such as one or more other electronic devices (not shown) connected directly to server 230 and / or connected to communications network 220. Database 230 may receive data from server 230 for its storage and may provide stored data to server 230 for its use.

[0097] The database 235 includes, among other things:

[0098] (i) storing images being acquired by the medical imaging device 210;

[0099] (ii) storing DICOM multiphase stacks;

[0100] (iii) memorizing a 3D geometric model of the blood vessel;

[0101] (iv) memorizing the strain map of the dissected vessel; and

[0102] (v) configured to store an interactive model of a dissected blood vessel;

[0103] communication network

[0104] In some embodiments of the present technology, the communications network 220 is the Internet. In alternative non-limiting embodiments, the communications network 220 may be implemented as any suitable local area network (LAN), wide area network (WAN), private communications network, etc. It should be expressly understood that the implementation of the communications network 220 is for illustrative purposes only. How the communications lines 225 (not separately numbered) between the workstation computer 215 and / or server 230 and / or another electronic device (not shown) and the communications network 220 are implemented will depend, among other things, on how each of the medical imaging device 210, the workstation computer 215, and the server 230 are implemented.

[0105] Communications network 220 may be used to transmit data packets between workstation computer 215, server 230, and database 235. For example, communications network 220 may be used to transmit requests between workstation computer 215 and server 230.

[0106] In one or more embodiments, the server 230 may be part of a picture archiving and communication system (PACS).

[0107] In another embodiment, server 230 may be omitted, in which case workstation computer 215 is in communication with or connected to database 235; and is configured to, among other things, receive an initial 3D model of the target vessel generated from an initial medical image of the vessel acquired at an initial time point; receive a subsequent 3D model of the target vessel generated from a subsequent medical image of the vessel acquired at a subsequent time point; register the initial 3D model with the subsequent 3D model, thereby obtaining a registered initial model; and identify at least one deformation of the vessel based on the registered initial model and the subsequent 3D model.

[0108] In some embodiments, the workstation computer 215 may also be configured to generate an initial 3D model of the vessel and subsequent 3D models of the vessel.

[0109] Referring now to FIG. 3, a schematic diagram of a procedure 300 for identifying or characterizing deformations in a blood vessel is shown, in accordance with one or more non-limiting embodiments of the present technology.

[0110] Procedure 300 is performed within system 200 of Figure 2. In one or more embodiments, procedure 300 may be performed by server 230. It is contemplated that several steps of AD strain mapping procedure 300 may be performed in parallel by server 230 or an electronic device (such as workstation computer 215), as will be recognized by one of ordinary skill in the art.

[0111] The purpose of procedure 300 is to acquire images of blood vessels at different time points, generate 3D models of the images, align the generated 3D models, and identify deformations in the blood vessels based on the aligned 3D models.

[0112] The procedure 300 includes, among other steps, an image acquisition procedure 302, an image modeling procedure 304, a registration procedure 306, and a deformation identification procedure 308.

[0113] Image acquisition procedure

[0114] The image acquisition procedure 310 includes, among other things:

[0115] (i) receiving images of the patient's blood vessel or a portion of a blood vessel acquired during the cardiac cycle; and

[0116] (ii) configured to use the received images of the dissected blood vessel to generate a 3D image of the blood vessel or a portion of the blood vessel during the cardiac cycle. In one or more embodiments, the blood vessel is the aorta.

[0117] In one or more embodiments, the images of the dissecting vessels are acquired from a subject known to have an aortic aneurysm, which may have been diagnosed by a physician. In one or more other embodiments, the images of the vessels may be acquired without prior knowledge of the aortic aneurysm, which may be detected, for example, during the image segmentation procedure 302.

[0118] During image acquisition procedure 310, multiple images of a blood vessel, such as the aorta, of a given subject are received. The multiple images may be received from workstation computer 215, directly from 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 an aorta having an aneurysm. It will be understood that the type of aortic dissection in the dissected blood vessel is not limited.

[0119] In one or more embodiments in which the medical imaging device 210 includes a CT scanner, the CT protocol for CT image acquisition can include preoperative retrospective gated MDCT (64-slice multislice CT scanner) using variable dose radiation to capture RR intervals. In one or more embodiments in which the medical imaging device 210 is an MRI scanner, the MR protocol can include steady-state T2-weighted fast field echo (TE=2.6 ms, TR=5.2 ms, flip angle 110°, fat suppression (SPIR), echo time 50 ms, maximum 25 cardiac phases 2, matrix 256×256, acquisition voxel MPS 1.56 / 1.56 / 3.00 mm, and reconstruction voxel MPS 0.78 / 0.78 / 1.5), or similar cine axial tomographic imaging of the aortic region under study.

[0120] The image acquisition procedure 310 organizes the images into a multi-phase stack. In one or more embodiments, the images are organized in a phased manner according to the Digital Imaging and Communications in Medicine (DICOM) stack, the implementation of which is known in the art.

[0121] In one or more embodiments, each phase of the multi-phase stack corresponds to an instance in time within a given patient's cardiac cycle.

[0122] The image acquisition procedure 310 outputs a first or initial 3D image of the target vessel or portion of the vessel, and second or subsequent 3D images of the vessel or portion of the vessel taken at different times. The initial and subsequent 3D images are acquired at the same phase, i.e., at the same instance in the cardiac cycle, but at different times. For example, the subsequent 3D images may be acquired weeks or months after the first 3D image. However, the initial 3D image represents the target vessel at a reference phase, and the subsequent 3D images also represent the target vessel at the same reference phase.

[0123] Image modeling procedure

[0124] The image modeling procedure 304 includes, among other things:

[0125] (i) receiving 3D images of blood vessels;

[0126] (ii) segmenting the 3D image; and

[0127] (iii) configured to generate a 3D model of the blood vessel based on the segmented 3D image.

[0128] It should be understood that any suitable segmentation method for identifying blood vessels in the received 3D images may be used. For example, the image modeling procedure 304 may use one or more machine learning (ML) models that have been trained to recognize vascular elements, such as the inner and outer surfaces of blood vessels. In this case, the image modeling procedure 304 may perform segmentation by using the ML models to classify pixels as belonging to the vascular surface.

[0129] Image segmentation may be performed based on one or more of pixel intensity, texture, and / or other attributes, for example, using deformable models and techniques such as, but not limited to, low-level segmentation (thresholding, region growing, etc.), model-based segmentation (multispectral, feature maps, dynamic programming, counter tracking), statistical methods, fuzzy methods, as well as other methods known in the art. In one or more other embodiments, at least a portion of the image segmentation may be performed by a human operator manually drawing vessel boundaries.

[0130] Once the 3D image is segmented, the image modeling procedure 304 generates a 3D model of the blood vessel. In some embodiments, the 3D model includes a 3D surface model, i.e., only the inner and outer surfaces of the blood vessel are represented in the 3D model.

[0131] In one or more embodiments, the 3D model of the blood vessel comprises a cloud of points. In another embodiment, the 3D model comprises a polygon mesh. In this case, the image modeling procedure 304 applies a polygon modeling method to obtain the 3D model.

[0132] Once the 3D model of the blood vessels is generated, it is stored in memory.

[0133] Alignment Procedure

[0134] The alignment procedure 306 includes, among other things:

[0135] (i) receiving an initial or first 3D model of the blood vessel and subsequent 3D models of the blood vessel; and

[0136] (ii) configured to align the initial 3D model with the subsequent 3D model;

[0137] In one or more embodiments, the registration procedure 306 includes a scaling step to ensure that the initial and subsequent 3D models are the same scale. In one or more embodiments, the initial 3D model is corrected, i.e., scaled up or down, to have the same scale as the subsequent image. In another embodiment, the subsequent 3D model is corrected, i.e., scaled up or down, to have the same scale as the initial 3D model.

[0138] In one or more embodiments, landmarks present on the vessel (represented in the corresponding 3D model) are used in the scaling step, as known in the art.

[0139] Once the initial and subsequent 3D models are at the same scale, a registration procedure 306 registers the initial and subsequent 3D models according to the chronological order in which the respective 3D images were acquired, i.e., registers the initial 3D model with respect to the subsequent 3D models. It should be understood that any suitable registration method may be used.

[0140] In one or more embodiments, by addressing the problem in the time direction of change, i.e., by aligning the initial 3D model with the subsequent 3D model (but not the subsequent 3D model with respect to the initial 3D model), artifacts resulting from incorrect mapping are limited to those related to the extension, rather than erroneous geometric results.

[0141] In one or more embodiments, the registration procedure 306 uses the centerlines of the 3D models for registration, where the centerlines of the initial 3D model and the subsequent 3D model are first identified, and the centerline of the initial 3D model is registered with the centerline of the subsequent model.

[0142] In one or more embodiments, the registration procedure includes two registration steps, where a fixed registration is performed first, followed by a deformable registration.

[0143] In one or more embodiments, the fixed registration is performed using an iterative nearest neighbor method to provide alignment of the initial 3D model to the subsequent 3D model and reduce or minimize an error metric that measures the goodness of alignment between the initial 3D model and the subsequent 3D model. The alignment is performed using a fixed transformation with up to six degrees of freedom. During the fixed registration, the initial 3D model may only be rotated and / or translated using a single transformation that is applied uniformly to all points of the 3D initial model.

[0144] A deformable registration is then performed using the rigidly transformed initial 3D model and the subsequent 3D model. During the deformable registration, different transformations can be applied to different sections of the rigidly transformed initial 3D model to reduce error metrics associated with different regions or individual points of the model. The transformations can include rotation(s), translation(s), scale(s), shear(s), and / or the like.

[0145] In another embodiment, the registration procedure includes three registration steps: first a rigid registration, then an affine registration, and finally a deformable registration.

[0146] A fixed registration is first performed to fixally align the initial 3D model to the subsequent 3D model. The fixedly aligned initial 3D model is then registered to the subsequent 3D model using an affine registration. For example, the fixed registration method described above may be used.

[0147] In one or more embodiments, affine registration uses an iterative nearest neighbor method to reduce or minimize an error metric that measures the goodness of alignment between two models. During affine registration, the alignment between the models may be performed using an affine transformation that allows up to 13 degrees of freedom. During affine registration, the rigidly registered initial 3D model may only be rotated, translated, scaled, and / or sheared using a single transformation that is applied uniformly to all points of the rigidly registered 3D initial model.

[0148] The affinely registered initial 3D model is then further registered to the subsequent 3D model using deformable registration as described above.

[0149] FIG. 4A shows an initial 3D model of the aorta and a subsequent 3D model of the aorta during the registration process, i.e., after fixed registration, after affine registration, and after deformable registration. FIG. 4B shows the centerlines of the initial and subsequent 3D models before and after the registration procedure. As shown, the centerlines of the registered initial and subsequent 3D models are superimposed substantially on top of each other. FIG. 9 shows exemplary initial and subsequent 3D models after the initial 3D model has been registered with the subsequent 3D model.

[0150] In a further embodiment, the registration procedure comprises a single registration, i.e., a fixed registration.

[0151] The output 308 of the registration procedure includes the registered initial and subsequent 3D models, i.e., the initial 3D model registered with respect to the subsequent 3D model, stored in memory.

[0152] Deformation Identification Procedure

[0153] The variant identification procedure 308 includes, among other things:

[0154] (i) receiving the initial 3D model and the subsequent 3D model aligned with respect to the subsequent 3D model; and

[0155] (ii) configured to identify vascular deformations, such as vascular growth, based on the two aligned 3D models.

[0156] In at least some embodiments, determining the deformation of the blood vessel includes determining at least one deformation parameter of the blood vessel.

[0157] The deformation parameter may be one of the following: volume, diameter, surface area, length, diameter or equivalent circle diameter, asymmetry, and the like.

[0158] In some embodiments, a vessel segment refers to a longitudinal segment of a vessel extending along a given portion of the vessel's length, e.g., along a given portion of the vessel's centerline. A vessel may be divided longitudinally into multiple segments, each extending along a respective length along the vessel, each positioned at a respective location along the vessel's length. Landmarks positioned along the vessel's length can be used to demarcate / define the longitudinal segments. The landmarks may be manually selected by the user or may be predefined. FIG. 12A shows landmarks in the form of circle positions at different locations along the length of a vessel model. The landmarks define the boundary between two longitudinal segments. FIG. 12B shows various longitudinal segments obtained using the landmarks of FIG. 12A.

[0159] The volume may correspond to the volume of the lumen of the blood vessel, the volume contained within the inner wall of the blood vessel, the volume contained within the outer wall of the blood vessel, etc. In some embodiments, the volume refers to the volume of the entire blood vessel. In other embodiments, the volume refers to the volume of a predefined longitudinal section of the blood vessel, or the volume of at least two predefined longitudinal sections of the blood vessel.

[0160] The diameter may correspond to the diameter of the lumen of a blood vessel, the distance between the centerline of the blood vessel and the inner wall of the blood vessel, the distance between the centerline of the blood vessel and the outer wall of the blood vessel, etc. In some embodiments, the diameter refers to the diameter of the blood vessel at at least one predefined location along the length of the blood vessel. In this case, the diameter may refer to the diameter at a predefined location along the length of the blood vessel along a given radial direction. In another example, the diameter of the blood vessel may be specified for different radial directions, and the diameter may refer to an average diameter, a maximum diameter, a minimum diameter, etc.

[0161] In other embodiments, the diameter may relate to a longitudinal section of a blood vessel, in which case the diameter may refer to the average diameter of the longitudinal section of the blood vessel, the smallest diameter of the longitudinal section of the blood vessel, the largest diameter of the longitudinal section of the blood vessel, etc.

[0162] The surface area may correspond to the surface area of ​​the lumen of the blood vessel, the surface area of ​​the inner wall of the blood vessel, the surface area of ​​the outer wall of the blood vessel, etc. In some embodiments, the surface area refers to the surface area of ​​the entire blood vessel. In other embodiments, the surface area refers to the surface area of ​​a predefined longitudinal section of the blood vessel, or the surface area of ​​at least two predefined longitudinal sections of the blood vessel.

[0163] The length may correspond to the length along the lumen of the blood vessel (i.e., the length between two predefined points or landmarks on the luminal wall of the blood vessel), the length along the inner wall of the blood vessel (i.e., the length between two predefined points or landmarks on the inner wall of the blood vessel), the length along the outer wall of the blood vessel (i.e., the length between two predefined points or landmarks on the outer wall of the blood vessel), etc. In some embodiments, the length refers to the length of the entire blood vessel. In other embodiments, the length refers to the length of a predefined longitudinal segment of the blood vessel, or the length of at least two predefined longitudinal segments of the blood vessel.

[0164] The asymmetry corresponds to asymmetry in the change in one of the above-mentioned deformation parameters, e.g., diameter, volume, surface area, or length, between the initial 3D model and the subsequent 3D model. In this case, the blood vessel is radially divided into multiple radial portions. For example, the blood vessel may be radially divided into eight 45-degree octants. A value of the deformation parameter is determined for each radial portion of the initial 3D model and the subsequent 3D model, and the difference between the two values ​​represents the change in the deformation parameter for each radial portion. Variation in the change in the deformation parameter indicates asymmetry in the deformation of the blood vessel. For example, the value of the deformation parameter may remain the same between the initial 3D model and the subsequent 3D model for all radial portions except one, indicating asymmetry in the deformation of the blood vessel. In some embodiments, the blood vessel may be further divided longitudinally so that the blood vessel is divided into multiple subsections, each extending longitudinally along a given distance along the length of the blood vessel and radially along a given radial length, and positioned at a respective longitudinal position and a respective radial position. The value of the deformation parameter is then calculated for each subsection for the initial 3D model and the subsequent 3D model, and the difference between the two calculated values ​​represents the change in the deformation parameter for each subsection of the vessel.

[0165] In at least some embodiments, the deformation identification procedure 308 further includes outputting information indicative of the identified deformation of the blood vessel, such as information regarding blood vessel growth. For example, the information indicative of the identified deformation may be stored in a memory. In the same or another example, the information indicative of the identified deformation may be provided for display. In this case, the information indicative of the identified deformation is transmitted to a display unit for display thereon.

[0166] In some embodiments, determining the deformation of the blood vessel includes determining a first deformation parameter value for the initial 3D model and a second deformation parameter value for the subsequent 3D model, i.e., a first value of a given deformation parameter for the initial 3D model and a second value of the same given deformation parameter for the subsequent 3D model. For example, determining the deformation of the blood vessel may include determining a first diameter for the initial 3D model and a second diameter for the subsequent 3D model. In this case, it should be understood that the first and second deformation parameter values ​​are determined for the same point (or landmark), section, portion, or subsection on the initial 3D model and the subsequent 3D model.

[0167] In some embodiments, the deformation identification procedure 308 further includes comparing the identified deformation parameter to a predefined threshold. In some embodiments, the identified deformation parameter is compared to a maximum threshold, and an alert is generated if the identified deformation parameter is equal to or greater than the maximum threshold. The alert may be a visual alert, such as a written message that may be provided for display.

[0168] In at least some embodiments, determining the deformation of the blood vessel includes determining a variation in at least one deformation parameter of the blood vessel. For example, determining the deformation of the blood vessel includes determining a variation in the diameter of the blood vessel or a variation in at least one predefined segment, section, or subsection of the blood vessel. In another example, determining the deformation of the blood vessel includes determining a variation in the diameter of the blood vessel or a variation in the diameter of at least one predefined segment, section, or subsection of the blood vessel. In a further example, determining the deformation of the blood vessel includes determining a variation in the surface area of ​​the blood vessel or a variation in the surface area of ​​at least one predefined segment, section, or subsection of the blood vessel. In yet another example, determining the deformation of the blood vessel includes determining a variation in the length of the blood vessel or a variation in the length of at least one predefined segment, section, or subsection of the blood vessel.

[0169] In some embodiments, the deformation identification procedure 308 further includes comparing the identified variation in the identified deformation parameter to a predefined variation threshold. In some embodiments, the variation in the identified deformation parameter is compared to a threshold maximum variation, and an alert is generated if the variation in the identified deformation parameter is equal to or greater than the threshold maximum variation. The alert may be a visual alert, such as a written message, which may be provided for display.

[0170] In some embodiments, the deformation identification procedure 308 further includes generating a graphical user interface (GUI) and providing the GUI for display. The GUI may include at least one identified deformation parameter and / or at least one identified variation in the deformation parameter. If a visual alert is generated, the GUI may include the visual alert.

[0171] In some embodiments, the GUI further includes a visual representation of the initial 3D model and the subsequent 3D model, such as a visual representation of the initial 3D model aligned with respect to the subsequent 3D model, where at least one of the initial and subsequent 3D models may be see-through.

[0172] 10 shows a display of a 3D model of a blood vessel that may be incorporated into a GUI. The display shown shows the variation of a given deformation parameter between an initial model and subsequent models, with the length of each white line indicating the amplitude of the variation of the given deformation parameter.

[0173] In some embodiments, the GUI is interactive. In some embodiments, the GUI is designed to allow a user to select at least one deformation parameter, the value and / or variation of which is desired, from a list of predefined deformation parameters. The list of predefined deformation parameters may include diameter, maximum diameter, minimum diameter, average diameter, volume, surface area, length, asymmetry, etc. In some embodiments, the GUI further allows a user to select points(es) and / or sections, portions, or subsections of the initial 3D model and / or subsequent 3D model. In some embodiments, the GUI includes predefined points (or landmarks) on the initial 3D model and / or subsequent 3D model from which the user may select at least one point and / or predefined sections, portions, or subsections of the initial 3D model and / or subsequent 3D model from which the user may select at least one section, portion, or subsection. In the same or other embodiments, the user may select at least one point and / or at least one section, portion, or subsection directly on the displayed initial and / or subsequent 3D model using an input device such as a mouse. For example, a user may select a given point on the initial 3D model and further select to obtain a diameter at the selected point, in which case the processor receives the selected deformation parameter, i.e., diameter, and confirmation of the selected point on the initial 3D model, determines the value of the diameter of the initial 3D model at the selected point and the value of the diameter of the subsequent 3D model at a point on the subsequent 3D model that corresponds to the selected point, and incorporates the determined diameters into a GUI for display to the user.

[0174] In some embodiments, the GUI includes a display of a blood vessel divided into multiple segments, portions, or subsegments, with each segment, portion, or subsegment being assigned a color based on the respective variation in deformation parameters between the initial 3D model and the subsequent 3D model determined for the segment. For example, an average diameter may be determined for each segment of the initial 3D model, and an average diameter may be determined for each segment of the subsequent 3D model (each segment of the subsequent 3D model corresponds to a respective segment of the initial 3D model). The variation in average diameter is determined for each segment based on the previously determined average diameter. A number of predefined ranges of possible values ​​for the variation in average diameter are established, and each range is assigned a corresponding color. Each segment of the vessel representation is assigned a color within the range within which the determined variation in average diameter falls, as shown, for example, in FIG. 7A . It should be understood that instead of the average diameter of the segment, other deformation parameters, such as volume or surface area, may be displayed to the user by assigning a color to each segment of the vessel representation.

[0175] Figure 11 shows eight different views of the 3D vascular model acquired at different locations around the 3D model, where the color of each point on the 3D model indicates the diameter growth over time, i.e., the diameter growth between an initial model acquired at the first time point and a subsequent model acquired at a second time point.

[0176] In some embodiments, vascular growth can be described through measurements such as vascular volume, vascular surface area (e.g., the surface area of ​​the inner surface of the vessel or the surface area of ​​the outer surface of the vessel), vascular diameter or maximum diameter, and / or vascular length, and / or these measurements applied to at least one predefined segment of the vessel, e.g., the volume of at least one segment of the vessel, the surface area of ​​at least one segment of the vessel (e.g., the surface area of ​​the inner surface of at least one segment of the vessel or the surface area of ​​the outer surface of at least one segment of the vessel), vascular diameter or maximum diameter, and / or vascular length. In addition to segmental or overall measurements, localized measurements can be performed, such as an increase in surface area, an increase in volume element, or a localized dilation of the vessel measured from a predefined point in the model to the centerline of the model.

[0177] In some embodiments, measurements can be taken on the surface of the mesh of the model to obtain deformations of polygon elements on the model, local areas of deformation on the model, and / or volumetric measurements of growth on the model.

[0178] In some embodiments, measurements using the centerline can include measurements of diametric growth of the model and volumetric changes in sections of the model.

[0179] In some embodiments, measurements can include mapping data from one time point to another, along with changes in the model's shape characteristics and centerline, including model length growth, model growth asymmetry, etc. All measurements can be plotted as a function of time to indicate the rate and trajectory of growth in the model.

[0180] In one embodiment where blood vessel growth is characterized, diameter, length, and / or volume measurements are performed by the deformation identification procedure 308. For both the initial 3D model and the subsequent 3D model, a diameter taken at the same given point along the centerline of the 3D model is computed. The difference between the calculated diameter for the subsequent 3D model and the calculated diameter for the initial 3D model indicates an increase in the diameter of the blood vessel at the given point. The calculated diameter may be the diameter of the vessel's lumen, the diameter of the vessel's outer wall, etc.

[0181] In one or more embodiments, the calculated diameter corresponds to the largest diameter within a cross section taken at a given point along the centerline. In another embodiment, the calculated diameter is the smallest diameter. In a further embodiment, the calculated diameter is the average diameter.

[0182] In one or more embodiments, the deformation identification step includes calculating a volume. The volume of the same section of the blood vessel is calculated for the initial 3D model and the subsequent 3D model. The growth of the blood vessel may be identified by comparing the calculated volume for the subsequent 3D model with the calculated volume for the initial 3D model. In one or more embodiments, the calculated volume corresponds to the volume between two landmarks. In one or more embodiments, the calculated volume corresponds to the volume of at least a section of the lumen of the blood vessel. In another embodiment, the calculated volume corresponds to the volume of at least a section of the blood vessel.

[0183] In one or more embodiments, the deformation identification procedure includes calculating a length. A length between two landmarks or two points on the 3D model is calculated for the initial 3D model and the subsequent 3D model. The deformation of the vessel may be identified by comparing the calculated length for the subsequent 3D model with the calculated length for the initial 3D model.

[0184] Figure 5 shows the longitudinal section of a 3D model obtained using a common shape space defined by the centerline of the vessel. The centerline of the vessel is used to calculate and define the perpendicular and parallel directions in the vessel. The directions are then used to obtain the longitudinal section of the vessel to understand a measure of the vessel asymmetry.

[0185] FIG. 6 shows the distribution of diameter measurements at a single level of a vessel shape taken at a given point along the centerline. The diameter measurements are taken perpendicular to the vessel centerline, and each data point along the centerline generates a unique measurement at the given data point for the given centerline. The vessel measurements may be taken from a common centerline aligned for both the initial and subsequent 3D models. In another example, the vessel measurements may be taken from the centerlines of both the initial and subsequent 3D models. Measurements of deformation between the initial and subsequent 3D models using segmentation are obtained via the process described above. Additionally, FIG. 6 shows measures related to the direction and rotation of vessel deformation. These measurements provide information about the occurrence of vessel tortuosity during growth.

[0186] Figures 7A-7C show distributions of changes in growth measurements generated for a vessel measured at a given set number of longitudinal segments positioned perpendicular to the vessel centerline. At each longitudinal segment, a segment of the vessel shape is obtained from the complete vessel shape dataset. The longitudinal segment is then analyzed to identify changes in deformation parameters (or geometric measurements) at that segment, such as maximum diameter, minimum diameter, equivalent circle diameter, and / or volume. The changes in deformation parameters can then be calculated by comparing measurements from the initial 3D model with subsequent 3D models. It should be understood that the number of longitudinal segments shown in Figures 7A-7C is for illustrative purposes only. In Figures 7A-7C, the color scale represents diameter growth rate, with orange representing a high diameter growth rate and blue representing little or no change.

[0187] Figure 8 illustrates a model, connections, polygons, and deformation or growth measures applied to multiple data points on a region of the model. A point on a model is a point in the three-dimensional space where the model is constructed. A connection is the connectivity or tangent of a polygon formed through the connections between points. A polygon refers to a shape created through the connections between points. A region is defined as a group of polygons or points with a clear geometric definition, such as a volume or shape feature in a model, such as an iliac artery or an aneurysm body. The connections between data points can be used to determine the deformation between a given data point from one time point to the next. The direction and magnitude of the deformation are obtained through the line connecting the two data points. The connectivity information between data points can be used to create a geometric plane or polygon. The polygon defined by the connections between points in a dataset is used to calculate deformation in terms of the polygon's shape properties, such as area deformation. The connections between polygons may also be used to define a volume within the dataset.

[0188] In one or more embodiments, for area transformation, the change in area from the initial 3D model to the subsequent 3D model is calculated by using the bounding area of ​​each polygon element in the 3D model, and the volume calculation is performed via bounded volumes contained within multiple polygon elements.

[0189] For deformations related to changes in the configuration of the model's surface, the points that make up the model, and the lines connecting those points, any volume bounded by an element can be calculated as deformation. Deformation is defined by identifying equivalent data points on the initial 3D model and calculating the new positions of those points in the subsequent 3D model. These are used to calculate growth as the deformation between data points using the continuum mechanics definition of deformation gradient and strain. The deformation gradient may be expressed as:

number

[0190] where x is the subsequent structure and X is the initial structure.

[0191] From the deformation gradients, the left and right Cauchy-Green strain tensors can be calculated as follows: C=F T F (Formula 2) B=FF T (Formula 3)

[0192] where C is the right Cauchy-Green strain tensor and B is the left Cauchy-Green strain tensor.

[0193] From the Cauchy-Green strain tensor on the right, the Green-Lagrange strain can be calculated as follows:

number

[0194] where E is the Green-Lagrange strain tensor.

[0195] The principal components of the Green-Lagrange strain tensor are obtained by calculating the eigenvectors and eigenvalues ​​of the tensor as follows: (E-λI)v=0 (Equation 5)

[0196] The eigenvalues ​​are the principal values ​​of the strain, and the eigenvectors are the main directions of the strain. The largest eigenvalue is obtained as the largest principal component of the strain.

[0197] To obtain the volumetric deformation of a volume element in a mesh, the Jacobian is defined as follows: J=detF (Formula 6)

[0198] The Jacobian quantifies the change in volume of each volume element in a model. Volume elements can be generated from a 3D surface mesh to identify volume. The volume growth of a model can be used in the same way as the surface deformation described herein.

[0199] In some embodiments, each point on the 3D model may have information associated with it. For example, functional data may include hemodynamic information, intraluminal thrombus information, and strain information obtained at time 1 when a first medical image is acquired and mapped to time 2 when a second medical image is acquired. This can be used to calculate changes in vascular function.

[0200] It should be expressly understood that not all technical advantages referred to herein need be enjoyed in each and every embodiment of the present technology. For example, embodiments of the present technology may be implemented without users enjoying some of these technical advantages, while other non-limiting embodiments may be implemented without users enjoying other technical advantages or none at all.

[0201] Some of these steps and signal transmission / reception are well known in the art and, therefore, have been omitted in certain portions of this description for the sake of simplicity. Signals can be transmitted and received using optical means (such as fiber optic connections), electronic means (such as wired or wireless connections), and mechanical means (such as pressure-based, temperature-based, or other suitable physical parameter-based).

[0202] Modifications and improvements to the above-described embodiments of the technology may become apparent to those skilled in the art. The foregoing description is intended to be illustrative, not limiting.

Claims

1. 1. A method for identifying deformations in a blood vessel of a subject, the method being executed by a processor, the method comprising: receiving an initial 3D model of the blood vessel of the subject, the initial 3D model being generated from an initial medical image of the blood vessel acquired at an initial time; receiving a subsequent 3D model of the blood vessel of the subject, the subsequent 3D model being generated from a subsequent medical image of the blood vessel acquired at a subsequent time; registering the initial 3D model to the subsequent 3D model, thereby obtaining a registered 3D model; determining at least one deformation parameter of the blood vessel based on the registered 3D model; and outputting the at least one transformation parameter.

2. said receiving said subsequent 3D model receiving the subsequent medical image of the blood vessel; segmenting the subsequent medical image, thereby obtaining a subsequent segmented image; and generating the subsequent 3D model of the blood vessel based on the subsequent segmented image.

3. said receiving said subsequent 3D model receiving the initial medical image of the blood vessel; segmenting the initial medical image, thereby obtaining an initial segmented image; and generating the initial 3D model of the blood vessel based on the initial segmented image.

4. The method of any one of claims 1 to 3, wherein the initial 3D model and the subsequent 3D model each comprise a 3D surface model.

5. The method of claim 4 , wherein the 3D surface model comprises a polygonal model.

6. The method of any one of claims 1 to 5, wherein the registering of the initial 3D model to the subsequent 3D model is performed using a fixed registration method and a deformable registration method.

7. The method of any one of claims 1 to 5, wherein the registering of the initial 3D model to the subsequent 3D model is performed using a fixed registration method, an affine registration method and a deformable registration method.

8. The method of any one of claims 1 to 7, further comprising identifying landmarks in the 3D initial model and the 3D subsequent model.

9. The method of claim 8 , wherein the registering of the initial 3D model to the subsequent 3D model is performed based on the identified landmarks.

10. The method of claim 8 or 9, further comprising: scaling one of the initial 3D model and the subsequent 3D model based on the identified landmarks.

11. said registering said initial 3D model to said subsequent 3D model; identifying a first centerline of the initial 3D model; identifying a second centerline of the subsequent 3D model; and aligning the first centerline with the second centerline.

12. The method of any one of claims 1 to 11, wherein said determining at least one deformation comprises determining a deformation gradient based on the registered 3D models.

13. 12. The method of claim 1, wherein the identifying at least one deformation comprises identifying given deformation parameters for the initial 3D model and identifying given deformation parameters for the subsequent 3D model.

14. The method of claim 13 , wherein the given deformation parameter comprises at least one of diameter, volume, length, surface area, equivalent circle diameter, and asymmetry.

15. 1. A system for identifying deformation of a blood vessel of a subject, the system comprising: a processor; a non-transitory storage medium operably connected to said processor and including computer-readable instructions stored therein; The processor, in executing the computer-readable instructions, receiving an initial 3D model of the blood vessel of the subject, the initial 3D model being generated from an initial medical image of the blood vessel acquired at an initial time; receiving a subsequent 3D model of the blood vessel of the subject, the subsequent 3D model being generated from a subsequent medical image of the blood vessel acquired at a subsequent time; registering the initial 3D model to the subsequent 3D model, thereby obtaining a registered 3D model; determining at least one deformation of the blood vessel based on the registered 3D model; and outputting said at least one deformation parameter.

16. the processor further comprising: receiving the subsequent medical image of the blood vessel; segmenting the subsequent medical image, thereby obtaining a subsequent segmented image; and generating the subsequent 3D model of the blood vessel based on the subsequent segmented image.

17. the processor further comprising: receiving the initial medical image of the blood vessel; segmenting the initial medical image, thereby obtaining an initial segmented image; and generating the initial 3D model of the blood vessel based on the initial segmented image.

18. The system of any one of claims 15 to 17, wherein the initial 3D model and the subsequent 3D model each include a 3D surface model.

19. The system of claim 18 , wherein the 3D surface model comprises a polygonal model.

20. 20. The system of any one of claims 15 to 19, wherein the processor is configured for registering the initial 3D model to the subsequent 3D model using a fixed registration method and a deformable registration method.

21. 20. The system of any one of claims 15 to 19, wherein the processor is configured for registering the initial 3D model to the subsequent 3D model using a fixed registration method, an affine registration method, and a deformable registration method.

22. The system of any one of claims 15 to 21, wherein the processor is further configured for identifying landmarks in the 3D initial model and the 3D subsequent model.

23. 23. The system of claim 22, wherein the processor is configured for aligning the initial 3D model to the subsequent 3D model based on the identified landmarks.

24. 24. The system of claim 22 or 23, wherein the processor is further configured for scaling one of the initial 3D model and the subsequent 3D model based on the identified landmarks.

25. The registering of the initial 3D model to the subsequent 3D model comprises: identifying a first centerline of the initial 3D model; identifying a second centerline of the subsequent 3D model; and aligning the first centerline with respect to the second centerline.

26. The system of any one of claims 15 to 24, wherein the processor is configured for determining deformation gradients based on the registered 3D models.

27. 25. The system of claim 15, wherein the processor is configured to identify given deformation parameters for the initial 3D model and to identify given deformation parameters for the subsequent 3D model.

28. 28. The system of claim 27, wherein the given deformation parameter comprises at least one of diameter, volume, length, surface area, equivalent circle diameter, and asymmetry.

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