Systems and methods associated with neurovascular device selection and deployment

Abstract

An exemplary method includes receiving an image of a blood vessel having an aneurysm captured by an image capture device, reconstructing the blood vessel using the image to generate a model of the blood vessel, determining a final length of a stent when deployed within the model of the blood vessel to redirect blood flow away from the aneurysm, taking into account foreshortening effects, visually presenting a representation of the stent deployed within the model of the blood vessel, and providing parameters of the stent including stent apposition and pore density.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 480,385, filed January 18, 2023, the contents of which are incorporated herein by reference as if fully set forth herein. [Background technology]

[0002] An aneurysm occurs when a section of an artery's wall weakens, causing it to bulge or expand abnormally. Aneurysms often occur in the aorta, brain, behind the knee, intestine, or spleen. If an aneurysm ruptures, it can cause internal bleeding, which can lead to a stroke, which can sometimes be fatal.

[0003] Aneurysms often do not cause symptoms until they rupture. Treatment for aneurysms ranges from observation to emergency surgery. Treatment options depend on the location, size, and condition of the aneurysm.

[0004] Some aneurysms require surgery to reinforce the arterial wall with a stent or coil. If the aneurysm bulges out from the side of the blood vessel, the area may be blocked with a clip or coil embolization.

[0005] Currently, medical professionals (e.g., neurosurgeons or interventional radiologists) may obtain images of the blood vessel and aneurysm, attempt to estimate the dimensions (e.g., diameter) of the aneurysm, and then, based on their own experience, attempt to determine what type of neurovascular device (e.g., stent or coil) is appropriate for that aneurysm and the parameters (e.g., diameter, length, etc.) of such a device.

[0006] However, two-dimensional (2D) images of a three-dimensional (3D) environment can be misleading, and a medical professional's estimate of the size and characteristics of the aneurysm and the vessel in which the aneurysm formed may be inaccurate. Based on such inaccurate estimates, a medical professional may select a device with certain parameters and deploy it within the vessel. However, due to the inaccuracy of the estimate, the device may be too large, too small, or inappropriate for the aneurysm. The medical professional may then try a different device with different parameters.

[0007] Therefore, medical professionals may use a trial-and-error approach to determine the type of device to use and its key parameters, which may be suboptimal and may reduce the effectiveness of the device upon deployment and may increase the risk of aneurysm rupture.

[0008] It is with respect to these and other considerations that the disclosure made herein is presented. Summary of the Invention

[0009] In some embodiments, systems and methods relating to the selection and deployment of neurovascular devices are described herein.

[0010] In further embodiments described herein, systems and methods are disclosed relating to capturing images of an aneurysm within a patient's vessel, using the images to determine which type of neurovascular device is best suited for the aneurysm and key parameters of such a neurovascular device, and visually presenting the neurovascular device within the vessel and the key parameters to a medical professional.

[0011] The foregoing features, functions, and advantages may be achieved independently in various embodiments or may be combined in yet other embodiments. Further details of the embodiments can be found in the following description and by reference to the drawings.

[0012] The novel features believed characteristic of the illustrative embodiments are set forth in the appended claims. However, the illustrative embodiments, as well as the preferred mode of use, further objects and description thereof, will best be understood by reference to the following detailed description of illustrative embodiments of the present disclosure when read in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0013] [Figure 1] An example of a cerebral aneurysm is shown. [Figure 2] 1 shows dimensions and ratios for an example aneurysm. [Figure 3] FIG. 1 is a block diagram of a system including an image capture device, a neurovascular device, and a display device, according to an exemplary implementation. [Figure 4] 1 is a flowchart of a method for determining and providing key parameters of a stent, according to an exemplary embodiment. [Figure 5] 5 illustrates a reconstructed vessel generated by the neurovascular module of FIG. 4, according to an exemplary embodiment. [Figure 6] 1 is a flowchart of a method for determining the final length of a stent when deployed in a reconstructed vessel, taking into account foreshortening effects, according to an exemplary embodiment. [Figure 7] 6 illustrates a stent deployed within the reconfigured vessel of FIG. 5, according to an exemplary embodiment. [Figure 8] 1 illustrates a cross-sectional view of a stent deployed in a vessel having an aneurysm, according to an exemplary embodiment. [Figure 9] 1 shows a graph illustrating the variation of stent apposition along the length of a vessel, according to an exemplary embodiment. [Figure 10-1] 1 illustrates a graphical user interface according to an exemplary embodiment. [Figure 10-2] 1 illustrates a graphical user interface according to an exemplary embodiment. [Figure 11] 1 shows a micro-computed tomography scan of a vessel with an aneurysm and a stent showing ribboning. [Figure 12] 1 illustrates a reconstructed blood vessel with an aneurysm and an area most prone to ribbon-like deformation, according to an exemplary embodiment. [Figure 13] FIG. 1 shows a block diagram of a system for flow visualization and quantification, according to an exemplary embodiment. [Figure 14] 1 illustrates different geometric characteristics of an aneurysm bulging from a blood vessel, according to an exemplary embodiment. [Figure 15] 1 illustrates blood flow through a blood vessel and aneurysm before treatment, according to an exemplary embodiment. [Figure 16] FIG. 16A shows blood flow through the vessel and aneurysm of FIG. 15 after treatment, according to an exemplary embodiment, and FIG. 16B shows the pore region of the FD device of FIG. 16A, according to an exemplary embodiment. [Figure 17] FIG. 1 is a block diagram of a computing device according to an exemplary implementation. [Figure 18] 1 is a flowchart of a method for selecting a neurovascular device and providing key parameters of the neurovascular device, according to an exemplary embodiment. [Figure 19] 19 is a flowchart of additional operations that can be performed in conjunction with the method of FIG. 18, according to an example implementation. [Figure 20] 19 is a flowchart of additional operations that can be performed in conjunction with the method of FIG. 18, according to an example implementation. [Figure 21] 19 is a flowchart of additional operations that can be performed in conjunction with the method of FIG. 18, according to an example implementation. [Figure 22] 19 is a flowchart of additional operations that can be performed in conjunction with the method of FIG. 18, according to an example implementation. [Figure 23] 19 is a flowchart of additional operations that can be performed in conjunction with the method of FIG. 18, according to an example implementation. [Figure 24]19 is a flowchart of additional operations that can be performed in conjunction with the method of FIG. 18, according to an example implementation. [Figure 25] 19 is a flowchart of additional operations that can be performed in conjunction with the method of FIG. 18, according to an example implementation. [Figure 26] 19 is a flowchart of additional operations that can be performed in conjunction with the method of FIG. 18, according to an example implementation. DETAILED DESCRIPTION OF THE INVENTION

[0014]

[0003] Embodiments described herein relate to improved selection and deployment of neurovascular devices in a blood vessel having an aneurysm. An image capture device captures images of the blood vessel and aneurysm and provides the images to a neurovascular module. The neurovascular module reconstructs the shape of the blood vessel and aneurysm from the images (e.g., determines a three-dimensional model). The neurovascular module then determines which type of neurovascular device (e.g., a braided stent, a laser-cut stent, a coil, etc.) is suitable or optimal for the particular aneurysm.

[0015] The neurovascular module also determines key parameters of the selected device. For example, in the case of a braided stent, the neurovascular module may determine one or more key parameters selected from a group of parameters including stent size (e.g., length and diameter), apposition to the vessel wall, pore density variation, etc. In determining such key parameters, the neurovascular module considers effects such as foreshortening effects that may prevent proper stent selection using conventional approaches.

[0016] As an example, the neurovascular module can further predict where ribboning may occur within a vessel, which can provide medical personnel with information to help optimize stent deployment techniques to avoid ribboning.

[0017] The neurovascular module may also be in communication with a display device. The neurovascular module may visually display a visualization of the stent deployed within a vessel on the display device. The neurovascular module may also generate a display of key parameters of the stent. Thus, a medical professional may be able to reduce or avoid trial and error and may be able to pre-plan the deployment of the neurovascular device in a more advanced manner.

[0018] Figure 1 shows an example of a cerebral aneurysm, which is used herein as an illustrative example. The systems and methods described herein can be used for aneurysms in any blood vessel.

[0019] In the illustrated example, the brain 100 of the patient 102 has several blood vessels, such as arteries 104. The arteries 104 are muscular tubular blood vessels that are part of the circulatory system of the patient 102. The arteries 104 transport blood from the heart to the brain 100, delivering oxygen and nutrients to support the brain 100 and its functions.

[0020] A diagnosis of a cerebral aneurysm in an artery 104 indicates the presence of a bulging, weak area in the wall of one of the arteries that supply blood to the brain. As shown in FIG. 1, a cerebral aneurysm 106 may form in an artery 104. As mentioned above, a brain or cerebral aneurysm is used as an example herein. Other types of aneurysms include, for example, aortic aneurysms, popliteal artery aneurysms, mesenteric artery aneurysms, and splenic artery aneurysms.

[0021] In some cases, it may be desirable to insert or deploy a neurovascular device, such as a stent (e.g., a braided stent or a laser-cut stent) or a coil, into the artery 104 or cerebral aneurysm 106 to divert blood flow away from the cerebral aneurysm 106 and prevent it from rupturing. The type of device used will depend on the characteristics (e.g., size and morphology) of the cerebral aneurysm 106.

[0022] 2 illustrates dimensions and ratios associated with an example aneurysm 200. Aneurysm 200 may represent, for example, a cerebral aneurysm 106.

[0023] Aneurysm 200 has a neck 202 (the narrowed portion where aneurysm 200 protrudes from the blood vessel) and a dome 204. The size of aneurysm 200 can be characterized by several dimensions, such as neck width "W1," dome width "W2," and dome height "H." The size can also be characterized by the ratio of these dimensions, such as aspect ratio = H / W1 and dome / neck ratio = W2 / W1.

[0024] These dimensions and ratios can help determine which type of neurovascular device to use. The characteristics and dimensions of the blood vessel from which the aneurysm 200 is distending can also facilitate the determination of key parameters of the neurovascular device. The system disclosed herein is configured to (i) capture images of the blood vessel and aneurysm, (ii) determine characteristics of the aneurysm and blood vessel based on the captured images, (iii) determine the type of neurovascular device to use and key parameters of the neurovascular device based on the determined characteristics, and (iv) visually present to the medical professional the blood vessel and aneurysm, the neurovascular device deployed in the blood vessel, and the key parameters of the neurovascular device.

[0025] 3 is a block diagram of a system 300 including an image capture device 302, a neurovascular module 304, and a display device 306, according to an exemplary implementation. The components of system 300 may be configured to function in an interconnected manner with each other and / or with other components coupled to their respective systems. One or more of the described operations or components of system 300 may be divided into additional operational or physical components or combined into fewer operational or physical components. In some further examples, additional operational and / or physical components may be added to system 300. Still further, any of the components or modules of system 300 may include or be provided in the form of a processor (e.g., a microprocessor, a digital signal processor, etc.) configured to execute program code including one or more instructions to perform the logical operations described herein.

[0026] System 300 may further include any type of computer-readable medium (non-transitory medium) or memory, such as a storage device including, for example, a disk or hard drive, that stores program code that, when executed by one or more processors, causes system 300 to perform the operations described above. In one example, system 300 may be included in other systems.

[0027] Image capture device 302 is configured to capture images of a blood vessel having an aneurysm. For example, image capture device 302 may include a computed tomography (CT) scanning device that combines a series of X-ray images taken from different angles around the body of a patient having an aneurysm and uses computer processing to generate cross-sectional images (slices) of the blood vessel.

[0028] In particular, the image capture device 302 may include a micro-CT scanning device that uses X-ray-based 3D imaging techniques to view the inside of a patient's body slice by slice. Micro-CT scanning is similar to CT scan imaging, but on a smaller scale with improved resolution. For example, blood vessels and aneurysms can be imaged with pixel sizes as small as 100 nanometers, and objects as large as 200 millimeters in diameter can be scanned. Therefore, micro-CT imaging may be suitable for capturing images of aneurysms.

[0029] Thus, in one example, image capture device 302 can include an X-ray source that generates X-rays, which then pass through a region of the patient having an aneurysm. Image capture device 302 also includes an X-ray detector that records the X-rays as a two-dimensional projection image. The X-ray source can then be rotated a small angle on a rotating platform, and another X-ray projection image is acquired. This step is repeated through 180 degrees or 360 degrees, thereby capturing images of the aneurysm from different angles.

[0030] The neurovascular module 304 is configured to receive a series of such X-ray projection images and then generate cross-sectional images through a computational process that may be referred to as “reconstruction.” For example, the neurovascular module 304 can use these images to generate 3D models of blood vessels and aneurysms.

[0031] The neurovascular module 304 is then configured to analyze the cross-sectional images and / or "slices" of the model to extract characteristics of the aneurysm (e.g., aneurysm dimensions and ratios) and the vessel from which the aneurysm is distending. Based on such determinations, the neurovascular module 304 is configured to determine an appropriate neurovascular device (e.g., a coil, a braided stent, a laser-cut stent, etc.).

[0032] The neurovascular module 304 is further configured to provide key parameters of the neurovascular device. For example, if the neurovascular device is selected to be a braided stent, the neurovascular module 304 can provide parameters such as the size (e.g., diameter and length) of the stent, expected apposition, pore density, etc. In determining the key parameters of the stent, the neurovascular module 304 takes into account foreshortening effects, as described in more detail below.

[0033] The neurovascular module 304 communicates such information to the display device 306, which visually presents this information to the medical practitioner. The medical practitioner can then select a commercially available stent that matches the key parameters provided by the neurovascular module 304. The operations performed by the neurovascular module 304 to determine the key parameters of the stent are described below.

[0034] 4 is a flowchart of a method 400 for determining and providing key parameters of a stent, according to an exemplary embodiment. Method 400 can be performed, for example, by neurovascular module 304.

[0035] Method 400 may include one or more operations or actions represented by one or more of blocks 402-412 (and associated blocks 602-612 of method 600, described below). While the blocks are illustrated in sequential order, these blocks may in some cases be performed in parallel and / or in a different order than described herein. Additionally, various blocks may be combined into fewer blocks, divided into additional blocks, and / or eliminated, depending on the desired implementation.

[0036] Additionally, for method 400 and other processes and operations disclosed herein, a flowchart illustrates the operation of one possible implementation of this example. In this regard, each block may represent a module, segment, or portion of program code, including one or more instructions executable by a processor to perform a particular logical operation or step in the process. The program code may be stored in any type of computer-readable medium or memory, such as a storage device, including a disk or hard drive. Computer-readable media may include non-transitory computer-readable media or memory, such as, for example, register memory, processor cache, and computer-readable media that store short-term data, such as random access memory (RAM). Computer-readable media may also include non-transitory media or memory, such as, for example, secondary or persistent long-term storage, such as read-only memory (ROM), optical or magnetic disks, and compact disc read-only memory (CD-ROM). Computer-readable media may also be any other volatile or non-volatile storage system. Computer-readable media may be considered, for example, as a computer-readable storage medium, a tangible storage device, or other article of manufacture. Additionally, for method 400 and other processes and operations disclosed herein, one or more blocks in FIG. 4 may represent circuitry or digital logic configured to perform particular logical operations within the process.

[0037] At block 402, the method 400 includes reconstructing a blood vessel from the image to generate a reconstructed blood vessel (e.g., a model of the blood vessel, such as a 3D model). The image may be received at the neurovascular module 304 from the image capture device 302 as described above, and the neurovascular module 304 may generate a 3D mesh of at least a portion of the blood vessel, including, for example, an aneurysm.

[0038] 5 shows a reconstructed blood vessel 500 generated by the neurovascular module 304, according to an exemplary embodiment. The reconstructed blood vessel 500 is depicted as a yoke-shaped (e.g., U-shaped) blood vessel for simplicity. Actual blood vessels may have complex shapes, as shown, for example, in FIGS. 8, 10, and 11.

[0039] 4, at block 404, the method 400 includes determining a center point of the distal opening of the reconstructed blood vessel. As shown in FIG. 5, the neurovascular module 304 may determine a distal opening 502 of the reconstructed blood vessel 500. The neurovascular module 304 may then determine a center point 504 of the distal opening 502. For example, the neurovascular module 304 may determine a point on the plane of the distal opening 502 that is located a given distance from substantially all points on the periphery of the distal opening 502. The neurovascular module 304 then designates such point as the center point 504.

[0040] Referring to FIG. 4 , in block 406, the method 400 includes determining the final length of the stent when deployed within the reconstructed vessel, taking into account the foreshortening effect. Braided stents are advantageously maneuverable, allowing medical personnel (e.g., surgeons or interventional radiologists) to reach and deploy the stent in distal regions within the intracranial vessel. The stent functions as a flow diverter for aneurysm occlusion. Operating as a flow diverter, the stent is used to change the hemodynamic conditions near the aneurysm and redirect blood flow from the aneurysm to the parent vessel where the aneurysm is distending, thereby promoting controlled thrombus formation within the aneurysmal sac and restoring normal blood flow.

[0041] Braided stents may have a mesh of tightly woven wires (e.g., 24 or more strands). By way of example, such stents can be made from cobalt-chromium metal alloys, which are characterized by a high strength-to-weight ratio.

[0042] A technical problem with using braided stents in neurovascular procedures is that it is difficult to predict the final position of the stent after deployment within a vessel because the change in length of the braided stent depends on the patient's anatomy and the placement of the device therein. In other words, a problem with deploying a braided stent is the change in overall length (shortening) that the stent experiences when it is released from a catheter into a vessel. Therefore, method 400 includes taking foreshortening into account when determining the final length and position of the stent when deployed. Determining the final length may include several operations.

[0043] 6 is a flowchart of a method 600 for determining the final length of a stent, taking into account foreshortening effects, when deployed in a reconstructed blood vessel 500, according to an illustrative example. Method 600 can be executed by neurovascular module 304, for example, to perform the operations of block 406.

[0044] At block 602, the method 600 includes extracting a centerline of the reconstructed blood vessel 500. Referring to FIG. 5 , the neurovascular module 304 may extract a centerline 506 along the length of the reconstructed blood vessel 500. For example, the neurovascular module 304 may divide the reconstructed blood vessel 500 into multiple segments or cross-sections along the length of the reconstructed blood vessel 500. The neurovascular module 304 may then determine a center point of the reconstructed blood vessel 500 in each of the multiple segments, taking into account the curvature and tortuosity of the reconstructed blood vessel 500. The neurovascular module 304 may then determine or extract the centerline 506 of the reconstructed blood vessel 500 by extrapolating or connecting the center points of such segments.

[0045] At block 604, the method 600 includes dividing the centerline 506 into a number of segments along the length of the centerline 506. The number of segments may vary. As an example, the neurovascular module 304 may divide the centerline 506 into 5,000 or 10,000 segments, depending on the length of the reconstructed vessel 500.

[0046] At block 606, the method 600 includes determining, for each segment of the plurality of segments, the maximum radius of a sphere positioned within the reconstructed blood vessel 500, with the center of the sphere lying on the centerline 506 within such segment. As an illustrative example, the neurovascular module 304 may implement a k-nearest neighbor algorithm (KNN) to determine the radius of the sphere. KNN is a non-parametric supervised learning classifier that uses proximity to make classifications or predictions about groupings of individual data points. In particular, the KNN algorithm may determine a radius from a given point on the centerline 506 within one of the plurality of segments and form a sphere centered at the given point such that the sphere touches the inner wall of the reconstructed blood vessel 500.

[0047] In this manner, the neurovascular module 304 can estimate or determine the radius of the reconstructed blood vessel 500 at each of the multiple segments along the length of the centerline 506. As noted above, the reconstructed blood vessel 500 shown in FIG. 5 is a simplified configuration in which the reconstructed blood vessel 500 has a constant radius throughout its length. However, a patient's actual blood vessel will likely have varying radii along its length.

[0048] After the radii have been determined for all segments as described in block 606, a stent may be selected that has a radius equal to the largest radius of the spheres determined in block 606. As an illustrative example, the diameter of the reconstructed blood vessel 500 may vary between 3.75 millimeters (mm) and 4.5 mm, and a stent having a diameter of 4.5 mm may be selected.

[0049] At block 608, the method 600 includes determining the length of the portion of the stent that will cover each segment. While a particular stent length may appear sufficient on a 2D image, that particular length may be insufficient when the stent is deployed due to foreshortening. In particular, due to the 3D configuration of the reconstructed blood vessel 500, the reconstructed blood vessel 500 may be longer than it appears in the 2D image. For example, the reconstructed blood vessel 500 may be tortuous in 3D space and may have twists and bends, causing the actual length of the vessel to be longer than it appears from the perspective of the 2D image. Furthermore, the stent may compress or otherwise change length upon deployment due to interactions with the vessel. Thus, a longer stent may be required than it appears in the 2D image. As an illustrative example, the length of the portion of the stent may be 1.5 mm to cover what appears to be a 1 mm segment of the reconstructed blood vessel 500 when the stent is deployed. This difference may be referred to as “foreshortening.”

[0050] The neurovascular module 304 estimates such shortening in each segment of the plurality of segments determined in block 604. Thus, the neurovascular module 304 can determine the length of a stent that will cover each segment of the plurality of segments when the stent is deployed in the reconstructed blood vessel 500.

[0051] In block 610, once the length determination for each segment has been completed for all segments, the method 600 includes determining a final proximal point of the stent when deployed within the reconstructed vessel 500. In particular, starting from the center point 504 of the distal opening 502, the neurovascular module 304 incrementally adds the lengths of the stent portions covering each segment of the reconstructed vessel 500 determined in block 608, thereby arriving at the final proximal point of the stent when deployed within the reconstructed vessel 500.

[0052] At block 612, the method 600 includes determining a final length of the stent as the distance between the center point 504 of the distal opening 502 of the reconstructed blood vessel 500 and the final proximal point determined in block 610. In particular, because the neurovascular module 304 has already determined the final proximal point of the stent when deployed, as determined in block 404 described above, the neurovascular module 304 can determine the distance between such final proximal point and the center point 504 of the distal opening 502. Such distance is the estimated deployed length of the stent.

[0053] 4, the method 400 proceeds to block 408. In block 408, the method 400 includes estimating the radial expansion of the stent within the reconstructed blood vessel 500 once the final length of the stent has been determined. The stent will expand when deployed from the catheter into the blood vessel and released. The neurovascular module 304 is configured to determine the extent of such expansion based on the stent material and the length determined in block 612.

[0054] At block 410, the method 400 includes visually presenting the stent deployed within the reconstructed blood vessel 500. Because the neurovascular module 304 has already determined the final length of the stent and the expected amount of expansion of the stent when deployed, it can generate a representation of the stent as deployed within the reconstructed blood vessel 500 to help a medical professional visualize the location and coverage of the stent when deployed within the patient's vessel.

[0055] 7 illustrates a stent 700 deployed within a reconstructed blood vessel 500, according to an exemplary embodiment. As shown, the visualization of the stent 700 within the reconstructed blood vessel 500 shows a beginning end 702 and an ending end 704 of the stent 700, which takes into account the foreshortening effect as described above. As noted above, the reconstructed blood vessel 500 represents a simplification of an actual blood vessel for purposes of illustrating methods 400, 600. An actual patient's blood vessel may have a more complex shape.

[0056] 8 shows a cross-sectional view of a stent 800 deployed in a blood vessel 802 having an aneurysm 804, according to an exemplary embodiment. The blood vessel 802 has a complex shape and may represent an actual blood vessel in a patient.

[0057] Neurovascular module 304 can perform the operations described above with respect to methods 400 and 600 to generate the visualization in Figure 8 of stent 800 deployed within a patient's blood vessel 802 (e.g., a reconstructed blood vessel). The visualization in Figure 8 shows the beginning and ending ends of stent 800 when deployed within blood vessel 802, and therefore indicates whether stent 800 adequately covers neck 806 of aneurysm 804 and whether stent 800 is effective in diverting blood flow from aneurysm 804.

[0058] Further, returning to Figure 4, at block 412, the method 400 includes determining key parameters of the stent 800 of Figure 8. For example, the neurovascular module 304 can provide information indicative of the apposition and pore density variation of the stent 800 to the vessel wall.

[0059] The apposition of stent 800 may refer to how closely the outer peripheral surface of stent 800 contacts the inner wall of blood vessel 802. If the outer diameter of stent 800 is smaller than the inner diameter of blood vessel 802, stent 800 may be characterized as having a loose apposition relative to the wall of blood vessel 802. Such a loose apposition may be undesirable because it may lead to shifting or migration of stent 800 when deployed within blood vessel 802. Rather, it is desirable to have stent 800 with a tight apposition such that stent 800 fits as closely as possible against the wall of blood vessel 802, stabilizing its position within blood vessel 802 and providing effective flow diversion.

[0060] Apposition can be expressed by a coverage percentage at a particular cross section of the vessel 802. For example, at a given cross section of the vessel 802, the coverage percentage can be determined as follows:

[0061] Coverage % = (Cross-sectional area of ​​vessel - Cross-sectional area of ​​stent) / Cross-sectional area of ​​vessel

[0062] Thus, the neurovascular module 304 can determine the coverage rate at various cross sections along the length of the blood vessel 802. The neurovascular module 304 can then present the coverage rate information to a medical professional, thereby enabling the medical professional to assess the apposition of the stent 800 when deployed within the blood vessel 802.

[0063] 9 shows a graph 900 illustrating the variation in apposition of a stent 800 along the length of a blood vessel 802, according to an exemplary embodiment. Graph 900 shows percent coverage on the right Y-axis and cross-sectional areas of the stent 800 and blood vessel 802 in square millimeters (mm ). 2 ) and the X-axis shows the length of the blood vessel 802 in millimeters (mm).

[0064] Line 902 shows the variation in the cross-sectional area of ​​blood vessel 802 along the length of blood vessel 802. The cross-sectional area of ​​blood vessel 802 increases due to the bulge, as shown in region 904, which corresponds to the location of aneurysm 804.

[0065] Line 906 shows the variation in the cross-sectional area of ​​stent 800 along the length of vessel 802. As shown by line 906, the cross-sectional area of ​​stent 800 starts out relatively large (starting at the left end), then narrows slightly in region 904, before widening again towards the end of vessel 802.

[0066] Line 908 shows the variation (variability) in the coverage rate or apposition of the stent 800 as determined by the above equation. As shown by line 908, the coverage rate begins (from the far left) at a relatively high value (e.g., close to 90%) indicating close contact between the stent 800 and the vessel 802, then drops off in region 904, and then rises again past region 904 to a value of approximately 90%. A medical professional can evaluate this apposition information and determine whether the performance of the stent 800 is acceptable or whether minor adjustments to parameters (e.g., stent diameter) can be made to improve performance. In an example, rather than providing a graph showing the variation in apposition, the neurovascular module 304 can provide an average apposition value to the medical professional.

[0067] The neurovascular module 304 can further provide information indicative of the porosity (e.g., pore density) of the stent 800. Porosity refers to the amount of space within the stent 800. As discussed above, the braided stent 800 has a mesh of tightly woven wires that form diamond-shaped pores between the interwoven wires throughout the stent 800. Porosity can be defined as the ratio of the volume of the voids or pores divided by the total volume of the stent 800.

[0068] Another way to indicate porosity is the density of such pores (e.g., how many pores are present) in a particular region. Such density can indicate the blood flow redirection ability of the stent 800 in such region. Too many pores may indicate poor blood flow redirection because blood will diffuse through the stent 800 into the aneurysm 804, while fewer pores and / or smaller pores may indicate increased blood flow redirection ability.

[0069] In an example, the porosity of the stent 800 may vary along the length of the stent 800 upon deployment within the blood vessel 802. Such variation may be based on the diameter and curvature of the blood vessel 802. When the stent 800 is deployed within the blood vessel 802, the stent 800 may be compressed in some regions, thereby reducing its porosity. The neurovascular module 304 is configured to estimate the porosity in various regions of the stent 800 using geometric rules based on the expected compression and curvature. The neurovascular module 304 can present such porosity information to a medical professional to assess the effectiveness of the stent 800 in diverting blood flow away from the aneurysm 804.

[0070] In one example, the neurovascular module 304 is configured to visually present key parameters and a visualization of the stent within the vessel via a graphical user interface (GUI) on the display device 306. Such a GUI can have a variety of configurations. An exemplary GUI is now described with respect to FIG. 10.

[0071] 10 illustrates a GUI 1000 according to an exemplary embodiment. The neurovascular module 304 is configured to generate a display of or visually present the GUI 1000 on the display device 306 to assist a medical professional in visualizing a selected neurovascular device for a particular patient with an aneurysm and to provide key parameters of such a neurovascular device.

[0072] The GUI 1000 may have user-selectable on-screen graphical items (e.g., buttons, menus, widgets, scroll bars, graphical objects, audio indicators, icons, etc.) to facilitate user interaction. In particular, the neurovascular module 304 generates a display of the GUI 1000 on the display device 306, and the healthcare professional may then interact with the GUI 1000 and select user-selectable user interface items, for example, by pressing or selecting areas on the touchscreen of the display device 306.

[0073] The GUI 1000 may have a device visualization display area 1002 that shows the type of device that is appropriate for the particular vessel and aneurysm as determined by the neurovascular module 304. For example, for a curved vessel 1004 having an aneurysm 1006 at its bend point, the neurovascular module 304 may determine that a stent 1008 (e.g., a braided stent) is appropriate. As shown, the neurovascular module 304 provides a visual representation of the stent 1008 deployed within the vessel 1004 after the operations of methods 400 and 600 are completed.

[0074] On the other hand, in the case of another T-shaped blood vessel 1010, for example, the blood vessel 1010 has a straight blood vessel portion 1012 and a branch blood vessel 1014 that is approximately perpendicular to the straight blood vessel portion 1012. The blood vessel 1010 has an aneurysm 1016 formed at a junction 1018 between the straight blood vessel portion 1012 and the branch blood vessel 1014. In this case, the neurovascular module 304 may determine that a coil embolization device 1020 is appropriate.

[0075] In one example, if both visualizations (e.g., one for vessel 1004 and one for vessel 1010) belong to the same patient and both vessels are selected by a medical professional (e.g., via vessel accordion 1038, described below), they can be presented simultaneously. In another example, one vessel is presented at a time.

[0076] In addition to the device visualization display area 1002, the GUI 1000 can have a deployment summary display area 1022 that provides key parameters of the neurovascular device (e.g., a stent or coil) as determined by the neurovascular module 304. By way of example, in the case of a braided stent such as stent 1008, the deployment summary display area 1022 can include several GUI items (e.g., message boxes or information widgets), such as GUI item 1024 providing the final deployed length of the stent, GUI item 1026 providing the braid angle after deployment (e.g., half the angle created by crossing filaments in the braided portion of a braided stent), GUI item 1028 providing the average apposition of the stent, and GUI item 1030 providing the pore density of the stent. More or fewer parameters may be provided.

[0077] In one example, the GUI 1000 can have a sidebar or side menu 1032 that provides several menu items to facilitate interaction between a healthcare professional and the GUI 1000. For example, the side menu 1032 can have a menu item 1034 that indicates the type of neurovascular device selected and what the information in the expanded summary display area 1022 pertains to.

[0078] Additionally, the side menu 1032 can have several accordions (e.g., vertically stacked lists of items that utilize a show / hide feature). For example, for braided stents, the side menu 1032 can have a braided stent accordion 1036 that lists options for stent configurations or brands / types. When clicked, the "braided stents" label expands the section and displays its contents, which include several stent brands / types to choose from. Selecting a different brand can cause the display device 306 to change the information and images displayed, for example, in the deployment summary display area 1022 and the device visualization display area 1002.

[0079] The side menu 1032 may have a vessel accordion 1038 that can be clicked to present the patient's various vessels. One or more vessels (e.g., vessel 1004 and / or vessel 1010) may then be selected by the medical professional to show information for each neurovascular device associated with the selected vessel. The side menu 1032 may have an add-on section 1040 that allows the medical professional to select information to display in chart or table format on the GUI 1000.

[0080] In some cases, when a stent is deployed in a tortuous vessel, "ribboning" of the stent can occur. A tortuous vessel can be defined as a complex vessel having repeated bends or bends, windings or kinks, etc. Due to such tortuosity, there can be sections of the deployed stent in such a vessel that do not expand properly to fill the vessel. In particular, the stent may become tangled on itself, or the stent may lose its ability to expand due to multiple expansions and compressions during deployment. Such poor expansion can be referred to as ribboning and reduces the effectiveness of the stent as a flow diverter device.

[0081] 11 shows a micro-CT image 1100 of a blood vessel 1102 having an aneurysm 1104 and a braided stent 1106 exhibiting ribbon-like deformation. As shown, the blood vessel 1102 is a tortuous vessel having at least one bent region 1108.

[0082] The braided stent 1106 has ribboned sections 1110 in bend regions 1108 where the braided stent 1106 has not expanded to fill the vessel 1102 (malapposition). It may be desirable to provide a medical professional with information indicating the areas of the vessel 1102 where ribboning is most likely to occur. Such advance information may prompt the medical professional to use a particular technique or exercise caution in deploying the stent in bend regions 1108 to avoid ribboning where it is most likely to occur.

[0083] 12 shows a reconstructed blood vessel 1200 having an aneurysm 1202 and a region 1204 where ribboning is most likely to occur, according to an exemplary embodiment. The neurovascular module 304 can generate the reconstructed blood vessel 1200 based on a micro-CT scan image, as described above in connection with FIG. 3. The neurovascular module 304 can then evaluate the shape and tortuosity of the reconstructed blood vessel 1200 to identify regions, such as region 1204, where ribboning may occur during deployment of a stent 1206.

[0084] For example, the neurovascular module 304 may identify sections of the reconstructed vessel 1200 where multiple bends are adjacent to one another, or regions of the reconstructed vessel 1200 where the diameter narrows below a threshold compared to the diameters of the adjacent regions. Using such criteria, the neurovascular module 304 may identify regions, such as region 1204, that are most susceptible to ribbon deformation.

[0085] The neurovascular module 304 can then display or visually present (e.g., on the GUI 1000) the reconstructed vessel 1200 labeled as the region most susceptible to ribboning, with or without displaying a stent 1206 within the region 1204. A medical professional can then take such information into account and adjust the deployment technique to prevent ribboning from occurring in the region 1204.

[0086] In some examples, the neurovascular module 304 can further estimate a fatigue safety factor for a neurovascular device (e.g., a stent) taking into account apposition, stent material, and shortening effects. Understanding the fatigue safety factor can increase the confidence of medical personnel after device deployment. In one example, the neurovascular module 304 can estimate the fatigue safety factor by utilizing the spatial distribution of RGB / grayscale values ​​of a stent deployed within a vessel to quantify mechanical stresses in a heat map, which can be further used to highlight the distribution of fatigue safety factors on each wire of the stent in a given configuration.

[0087] Because aneurysm progression and rupture are governed by the gradual deterioration and weakening of the aneurysm wall in response to abnormal hemodynamics, it may be desirable to have tools that help investigate the relationship between hemodynamic conditions within an aneurysm and the mechanical properties of the aneurysm wall, thereby improving aneurysm evaluation and patient management.

[0088] Characterization of qualitative and quantitative parameters of the aneurysm sac can include, by way of example, maximum ostium diameter, sac area, and sac volume. Such geometric characteristics, along with blood flow patterns, can be used to predict aneurysm rupture risk.

[0089] Thus, in some examples, the neurovascular module 304 can determine blood flow characteristics within blood vessels and aneurysms, generate visualizations of blood flow within blood vessels and aneurysms after deployment of a neurovascular flow diverter device, and provide quantitative parameters associated with the blood flow characteristics and the performance of the flow diverter (e.g., a stent or coil).

[0090] 13 shows a block diagram of a system 2300 for flow visualization and quantification, according to an exemplary embodiment. The system 2300 can be implemented by, for example, the neurovascular module 304.

[0091] In block 2302, system 2300 receives variables or parameters associated with a flow diverter (FD) device selected for aneurysm treatment, geometric characteristics of the aneurysm and the vessel in which the aneurysm is distended, patient information, etc. For example, system 2300 may receive or have access to the pore size and pore density of the FD device as deployed (e.g., as described above with respect to item 1030 of GUI 1000). System 2300 also determines or has access to the geometric characteristics / size of the aneurysm.

[0092] 14 illustrates different geometric characteristics of an aneurysm 2400 bulging from a blood vessel 2402, according to an exemplary embodiment. Based on the images captured by the image capture device 302, the system 2300 can determine geometric characteristics of the aneurysm, such as maximum opening diameter, surface area Aa, volume Va, neck ratio, aspect ratio, etc., which can be extracted from the CT images captured by the image capture device 302.

[0093] The system 2300 also determines or receives information indicative of the shape and size of the blood vessel 2402 from which the aneurysm 2400 is distending. The system 2300 also has access to patient information such as blood pressure.

[0094] 13 , in block 2304, the system 2300 collects data from simulations performed by the neurovascular module 304. For example, as described above in connection with FIG. 10 , the results of such simulations include visualization of the FD device deployed within the vessel, the starting point and landing zone of the FD device (e.g., a stent), the deployment length, the porosity, etc. The porosity of the FD device can be determined in 3D across the aneurysm.

[0095] System 2300 can further clean, scale, annotate, order, and organize the data to provide it to a machine learning (ML) model or algorithm in block 2306. The ML model can be trained with previous experience, datasets from other patients, etc. Based on such training, the ML model is trained to find relationships between output performance parameters, described below, and characteristics (input variables) of the aneurysm, vessel, and selected FD device. In particular, the ML model is configured to use the data provided from block 2304 as input data to be processed through the ML model to generate quantitative predictions regarding the performance of the FD device, as well as visualizations of blood flow within the vessel and aneurysm, for example, in block 2308.

[0096] In this manner, the system 2300 (neurovascular module 304) can provide medical personnel with insight into assessing the blood flow redirection capabilities and effectiveness of devices. By way of example, in block 2308, the neurovascular module 304 can provide parameters such as aneurysm inflow velocity, aneurysm occlusion (turnover time), and aneurysm impact zone, which in turn assist medical personnel in device selection and deployment.

[0097] Aneurysmal inflow can be defined as the average flow rate of blood Q(t) entering the aneurysm (aneurysmal sac) through the neck plane 2404 shown in Figure 14 over the duration of one cardiac cycle T. The neck plane 2404 can be defined as the plane where the aneurysmal sac (aneurysm 2400) intersects with the parent vessel (vessel 2402). The average blood flow rate Q avg can be determined as follows:

[0098]

number

[0099] Turnover time Tt can be defined as the duration required to fill the aneurysm sac (e.g., fill the aneurysm volume Va). The turnover time can be determined as follows:

[0100] T t =V a / Q avg where V a is the volume of the aneurysm 2400.

[0101] The impact zone (IZ) is the surface area of ​​the aneurysmal sac where the blood flow impinges. a It can be defined as a fraction or percentage of the total surface area of ​​the aneurysm sac at peak systole: IZ=I a / A a where I a is the impact area, A a is the total surface area.

[0102] Thus, system 2300 can provide medical personnel with visualization of blood flow before and after FD device deployment, allowing medical personnel to evaluate the performance of FD devices and select an appropriate FD device.

[0103] FIG. 15 shows blood flow through blood vessel 2500 and aneurysm 2502 before treatment (before deployment of FD device 2504), FIG. 16A shows blood flow through blood vessel 2500 and aneurysm 2502 after treatment (after deployment of FD device 2504), and FIG. 16B shows the pore region of FD device 2504 according to an exemplary embodiment. FIG. 15 and FIG. 16A show exemplary blood flow visualization that system 2300 (neurovascular module 304) can provide to a medical professional. FD device 2504 can be, for example, a braided stent.

[0104] As shown in FIG. 15, blood flows from blood vessel 2500 into aneurysm 2502, circulates within it, and strikes the interior wall. In FIG. 16A, FD device 2504 significantly inhibits or reduces blood flow to aneurysm 2502. Thus, FD device 2504 slows down blood flow within aneurysm 2502 (Q avg and T t IZ) which can increase the likelihood of thrombus or embolism formation to prevent rupture of the aneurysm 2502. The FD device 2504 also reduces the impact of blood flow on the inner wall of the aneurysm 2502 (e.g., reduces the IZ), thereby reducing the likelihood of rupture.

[0105] Thus, for FD device 2504, system 2300 can provide a visual representation of the predicted performance of FD device 2504 to assist medical personnel in assessing its performance and determining the suitability of FD device 2504 in treating a patient. In addition, system 2300 can further provide a numerical value indicative of performance.

[0106] In this way, the system 230 can assist in risk mitigation, treatment (neurovascular device) selection and sizing based on these predicted mechanical / geometric properties of the vessel / aneurysm. The mechanical properties of the aneurysm wall can be evaluated for burst strength, stiffness, and modulus. The geometric properties include the aneurysm's volumetric details, dome-to-neck ratio, aspect ratio, wall thickness, and other surface and volumetric details shown in FIG. 14.

[0107] For example, based on such parameters and characteristics, the system 2300 may determine the aneurysm inflow rate (e.g., Q(t) or Q avg ), can provide an indication of aneurysm occlusion and changes in the aneurysm impact zone, thereby providing an assessment of rupture risk.

[0108] Table 1 below provides, by way of example, output parameters that the system 2300 can provide.

[0109] [Table 1]

[0110] As shown by the information in Table 1, the FD device 2504 reduced the aneurysm inflow rate from 0.6 milliliters per second (ml / s) to 0.07 ml / s, representing an 89% reduction in flow rate, which increases the likelihood of thrombus formation within the aneurysm 2502 and protects the aneurysm 2502 from rupture. Aneurysm occlusion, as measured by turnover time in seconds, shows an increase in turnover time from 0.3 seconds to 4.32 seconds. Therefore, the turnover time increased by approximately 93%. Thus, upon deployment of the FD device 2504, blood flow fills the aneurysm 2502 more slowly and remains there for a longer period of time (e.g., blood flow slows and blood does not flow out of the aneurysm 2502 as quickly). Additionally, the impact zone decreased by approximately 96.3%, indicating a significant reduction in the surface impact area within the aneurysm 2502, thereby improving protection against rupture.

[0111] Therefore, blood flow visualization and performance evaluation of a specific FD device can guide medical professionals to select an appropriate device and adjust treatment options interventionally.

[0112] Additionally, in some examples, the neurovascular module 304 can estimate the strength characteristics of a blood vessel based on its radiodensity, which is the opacity to the radio wave and x-ray portions of the electromagnetic spectrum, i.e., the relative difficulty of radio wave and x-ray electromagnetic radiation passing through a particular material (e.g., a blood vessel). In this manner, the neurovascular module 304 can rank the mechanical strength coefficient of a blood vessel, taking into account the patient's radiopacity and age parameters. This functionality can assist medical professionals in selecting appropriate devices, such as stents with various stiffness profiles, hardnesses, braid angles, or device combinations such as stent-coil combinations and similar hybrid configurations.

[0113] Operations performed by the neurovascular module 304 (e.g., system 2300), such as image processing, training ML models, processing information through ML models, visualization, etc., can be computationally intensive and may involve large amounts of data processing. Therefore, it may be desirable to use a cloud system for data storage and processing. Cloud systems can also be useful, for example, for aggregating data from multiple patients within a medical facility or from other facilities to enhance the training of ML models.

[0114] However, when it is desirable for medical personnel to make real-time decisions when treating patients, edge computing and high-performance computing (H-computing) techniques can be employed to reduce latency. High-performance computing generally refers to the aggregation of computing power to provide performance much higher than that available from a typical desktop computer or workstation for processing the large amounts of data and images involved in performing the operations described above.

[0115] Edge computing involves having a local cloud system where the data is being generated (e.g., a hospital). Rather than sending raw data to a central data center for processing and analysis, the processing and analysis can be performed where the data is generated (e.g., a hospital or medical facility). This reduces latency and can aid in real-time decision-making where data is processed in milliseconds.

[0116] 17 is a block diagram of a computing device 1300 according to an example implementation. The computing device 1300 may represent or be included within any of the devices described above (e.g., the image capture device 302, the neurovascular module 304, the display device 306, etc.).

[0117] Computing device 1300 may have a processor 1302, a communication interface 1304, and data storage 1306, each connected to a communication bus 1312. Computing device 1300 may also include hardware that enables communications within computing device 1300 and between computing device 1300 and other devices. The hardware may include, for example, transmitters, receivers, and antennas.

[0118] The communication interface 1304 may be a wireless interface and / or one or more wired interfaces that enable both short-range and long-range communication to one or more networks or one or more remote devices (e.g., to enable communication with the communication bus 1312). Such wireless interfaces may provide communication via one or more wireless communication protocols, such as Bluetooth, Wi-Fi (e.g., the Institute of Electrical and Electronics Engineers (IEEE) 802.11 protocol), Long Term Evolution (LTE), cellular communication, Near Field Communication (NFC), and / or other wireless communication protocols. The wired interfaces may include an Ethernet interface, a CAN network interface, a USB interface, or a similar interface for communicating via a wire, twisted pair, coaxial cable, optical link, fiber optic link, or other physical connection to a wired network.

[0119] Data storage 1306 may include or take the form of one or more computer-readable storage media that can be read or accessed by processor 1302. The computer-readable storage media may include volatile and / or non-volatile storage components, such as optical, magnetic, organic, or other memory or disk storage, which may be integrated in whole or in part with processor 1302. Data storage 1306 is considered a non-transitory computer-readable medium. In some examples, data storage 1306 may be implemented using a single physical device (e.g., one optical, magnetic, organic, or other memory or disk storage unit), while in other examples, data storage 1306 may be implemented using two or more physical devices.

[0120] Thus, the data storage 1306 is a non-transitory computer-readable storage medium that stores executable instructions 1314. The executable instructions 1314 include computer-executable code. When executed by the processor 1302, the executable instructions 1314 cause the processor 1302 to perform operations of the computing device 1300 (e.g., operations performed by the image capture device 302, the neurovascular module 304, or the display device 306).

[0121] Processor 1302 may be a general-purpose processor or an application-specific processor (e.g., a digital signal processor, an application-specific integrated circuit (ASIC), etc.). Processor 1302 may receive input from communication interface 1304 and process the input to generate output that is stored in data storage 1306. Processor 1302 may be configured to execute executable instructions 1314 (e.g., computer-readable program instructions) stored in data storage 1306, and is executable to provide the functionality of computing device 1300 as described herein.

[0122] If the computing device 1300 represents a display device 306, the computing device 1300 may further include an output interface 1308 and a display 1310. The output interface 1308 may also output information to the display 1310 or other components. As such, the output interface 1308 may be a wireless interface (e.g., a transmitter) or a wired interface. The processor 1302 may receive input from the communication interface 1304 and process the input to generate output for the display 1310.

[0123] 18 is a flowchart of a method 1400 for selecting a neurovascular device and providing key parameters of the neurovascular device, according to an exemplary embodiment. Method 1400 can be performed, for example, by the neurovascular module 304.

[0124] Method 1400 may include one or more operations or actions represented by one or more of blocks 1402-1410, 1500, 1600-1608, 1700-1702, 1800, 1900, 2000, 2100-2102, and 2200-2202. While the blocks are illustrated in sequential order, these blocks may in some cases be performed in parallel and / or in an order different from that described herein. Additionally, various blocks may be combined into fewer blocks, divided into additional blocks, and / or eliminated, depending on the desired implementation.

[0125] Additionally, for method 1400 and other processes and operations disclosed herein, a flowchart illustrates the operation of one possible implementation of this example. In this regard, each block may represent a module, segment, or portion of program code, including one or more instructions executable by a processor to implement a particular logical operation or step within the process. The program code may be stored in any type of computer-readable medium or memory, such as a storage device, including a disk or hard drive. The computer-readable medium may include non-transitory computer-readable media or memory, such as computer-readable media that store short-term data, such as register memory, processor cache, and random access memory (RAM). The computer-readable medium may also include non-transitory media or memory, such as secondary or persistent long-term storage, such as read-only memory (ROM), optical or magnetic disks, compact disc read-only memory (CD-ROM), etc. The computer-readable medium may also be any other volatile or non-volatile storage system. The computer-readable medium may be considered, for example, a computer-readable storage medium, a tangible storage device, or other article of manufacture. Additionally, for method 1400 and other processes and operations disclosed herein, one or more blocks in FIG. 18 may represent circuitry or digital logic configured to perform specific logical operations within the process.

[0126] At block 1402, the method 1400 includes receiving, at a processor (eg, processor 1302 of the neurovascular module 304), an image of a blood vessel having an aneurysm captured by the image capture device 302.

[0127] At block 1404, method 1400 includes the processor reconstructing the vessel using the image to generate a model of the vessel (e.g., a 3D model of the vessel, such as reconstructed vessel 500, vessel 802, or vessel 1004).

[0128] At block 1406, method 1400 includes determining, by the processor, a final length of the stent when deployed within the model of the blood vessel to divert blood flow away from the aneurysm, taking into account the foreshortening effect. Determining the final length of the stent is described above in connection with block 404 and method 600.

[0129] At block 1408, the method 1400 includes visually presenting, by the processor (e.g., on the display device 306), a representation of the stent deployed within the model of the blood vessel. Such a visual presentation is shown, for example, in FIG.

[0130] At block 1410, the method 1400 includes providing, by the processor, parameters of the stent, including stent apposition and pore density. For example, the parameters may be displayed as shown in FIG.

[0131] 19 is a flowchart of additional operations that can be performed in conjunction with method 1400, according to an example embodiment. At block 1500, the operations further include estimating a radial expansion of the stent within the model of the vessel once the final length of the stent has been determined, and visually presenting a representation of the stent includes visually presenting the stent in an expanded state within the model of the vessel, as described above, for example, in connection with block 408 of method 400.

[0132] 20 is a flowchart of additional operations that can be performed in conjunction with method 1400, according to an example embodiment. Determining the final length of the stent, taking into account the foreshortening effect, involves several operations. In block 1600, the operations include determining a center point (e.g., center point 504) of a distal opening (e.g., distal opening 502) of a model of a vessel (e.g., a model of reconstructed vessel 500). In block 1602, the operations include extracting a centerline (e.g., centerline 506) of the model of the vessel.

[0133] At block 1604, the operations include dividing the centerline into a plurality of segments along its length. At block 1606, the operations include determining a length of a portion of the stent covering each segment, taking into account foreshortening effects upon deployment of the stent. At block 1608, the operations include, upon determining the length of each of the portions covering the plurality of segments, determining a final proximal point of the stent when deployed within the vessel model, the final length of the stent being the distance between the center point of the distal opening and the final proximal point.

[0134] 21 is a flowchart of additional operations that can be performed in conjunction with method 1400, according to an example embodiment. At block 1700, the operations include determining, for each segment of the plurality of segments, a maximum radius of a sphere positioned within the model of the vessel, with the center of the sphere lying on a centerline within such segment. At block 1702, the operations include selecting a stent having a particular radius based on the determined respective maximum radii of the spheres of the plurality of segments.

[0135] 22 is a flowchart of additional operations that can be performed in conjunction with method 1400, according to an exemplary embodiment. At block 1800, the operations include generating a representation of the variation in apposition of the stent along the length of the vessel (see FIG. 9).

[0136] 23 is a flowchart of additional operations that can be performed in conjunction with method 1400, according to an exemplary embodiment. At block 1900, an operation includes generating a display of the mean apposition of the stent (see GUI item 1028 in FIG. 10).

[0137] 24 is a flowchart of additional operations that can be performed in conjunction with method 1400, according to an exemplary embodiment. At block 2000, an operation includes generating a display of the deployed braid angle of the braided stent (see GUI item 1026 in FIG. 10).

[0138] 25 is a flowchart of additional operations that can be performed in conjunction with method 1400, according to an exemplary embodiment. At block 2100, the operations include estimating, by a processor, the dimensions of the aneurysm (e.g., H, W1, W2, and ratios described in connection with FIG. 2) using a model of the blood vessel. At block 2102, the operations include determining, based on the dimensions, that a stent is an optimal neurovascular device for the aneurysm. For example, neurovascular module 304 determines that the blood vessel is similar to blood vessel 1004 rather than blood vessel 1010, and therefore determines that a stent is more suitable for redirecting blood flow.

[0139] 26 is a flowchart of additional operations that can be performed in conjunction with method 1400, according to an exemplary embodiment. At block 2200, the operations include, by a processor, using a model of a vessel to determine at least one region where ribbon-like deformation of the stent is most likely to occur upon deployment (see FIGS. 11-12). At block 2202, the operations include, by the processor, providing information to a medical professional indicative of the at least one region (e.g., region 1204).

[0140] The above detailed description, with reference to the accompanying drawings, describes various features and operations of the disclosed system. The exemplary embodiments described herein are not intended to be limiting. Certain aspects of the disclosed system can be arranged and combined in a wide variety of different configurations, all of which are contemplated herein.

[0141] Furthermore, unless the context suggests otherwise, features shown in each figure can be used in combination with one another. Thus, the figures should be viewed generally as component aspects of one or more overall embodiments, with the understanding that not all illustrated features are required for each embodiment.

[0142] Additionally, any recitation of elements, blocks, or steps in the specification or claims is for the purpose of clarity, and therefore, such recitation should not be construed as requiring or implying that these elements, blocks, or steps follow a particular sequence or be performed in a particular order.

[0143] Furthermore, a device or system may be used or configured to perform the functions presented in the figures. In some cases, device and / or system components may be configured to perform the functions such that the components are actually configured and structured (using hardware and / or software) to enable such functions. In other examples, device and / or system components may be adapted, executable, or arranged to perform the functions, such as when operated in a particular manner.

[0144] The term "substantially" or "about" means that the referenced characteristic, parameter, or value need not be exactly realized, but rather that deviations or variations, including, for example, tolerances, measurement errors, limitations in measurement accuracy, and other factors known to those skilled in the art, may occur to an extent that does not preclude the effect that the characteristic is intended to bring about.

[0145] The configurations described herein are for illustrative purposes only. As such, those skilled in the art will recognize that other configurations and other elements (e.g., machines, interfaces, operations, order and grouping of operations, etc.) can be substituted, and that some elements may be omitted entirely, depending on the desired results. Furthermore, many of the elements described herein are functional entities and can be implemented as discrete or distributed components, or in combination with other components, in any suitable combination and location.

[0146] While various aspects and embodiments are disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and not limitation, with the true scope being indicated by the following claims, along with the full scope of equivalents to which such claims are entitled. Additionally, the terminology used herein is used only for the purpose of describing particular embodiments and is not intended to be limiting.

[0147] To this end, embodiments of the present disclosure may relate to one of the following listed exemplary embodiments (EEE):

[0148] EEE1 is a method including: receiving, in a processor, an image of a blood vessel having an aneurysm captured by an image capture device; reconstructing, by the processor, the blood vessel using the image to generate a model of the blood vessel; determining, by the processor, a final length of a stent when deployed in the model of the blood vessel to divert blood flow from the aneurysm, taking into account foreshortening effects; visually presenting, by the processor, a representation of the stent deployed in the model of the blood vessel; and providing, by the processor, parameters of the stent including stent apposition and pore density.

[0149] EEE2 is the method of EEE1, further comprising estimating a radial expansion of the stent within the model of the blood vessel once the final length of the stent has been determined, and visually presenting a representation of the stent includes visually presenting the stent in an expanded state within the model of the blood vessel.

[0150] EEE3 is any of the methods EEE1-2, wherein determining the final length of the stent taking into account the shortening effect includes determining a center point of a distal opening of a blood vessel model, extracting a centerline of the blood vessel model, dividing the centerline into a plurality of segments along the length of the centerline, determining a length of a portion of the stent covering each segment taking into account the shortening effect when the stent is deployed, and when determining the length of each of the portions covering the plurality of segments, determining a final proximal point of the stent when deployed in the blood vessel model, wherein the final length of the stent is the distance between the center point of the distal opening and the final proximal point.

[0151] EEE4 is the method of EEE3, further comprising: for each segment of the plurality of segments, determining a maximum radius of a sphere positioned within the model of the blood vessel with a center of the sphere on a centerline within such segment; and selecting a stent having a particular radius based on the determined respective maximum radii of the spheres of the plurality of segments.

[0152] EEE5 is any of the methods EEE1-EEE4, wherein providing parameters of the stent includes generating an indication of variation in apposition of the stent along the length of the vessel.

[0153] EEE6 is any of the methods EEE1-EEE5, wherein providing the parameters of the stent includes generating an indication of a mean apposition of the stent.

[0154] EEE7 is any of the methods of EEE1-6, wherein the stent is a braided stent, and providing the parameters of the stent includes generating an indication of a braid angle after deployment of the braided stent.

[0155] EEE8 is any of the methods EEE1-7, further comprising estimating, by a processor, the dimensions of the aneurysm using a model of the blood vessel, and determining, based on the dimensions, that a stent is an optimal neurovascular device for the aneurysm.

[0156] EEE9 is any of the methods EEE1 to EEE8, further comprising: determining, by the processor, using a model of the vessel, at least one region in which ribbon-like deformation of the stent is most likely to occur upon deployment; and providing, by the processor, information indicative of the at least one region to a medical professional.

[0157] The method of any of EEE1-EEE9 may further include any of the operations performed by the neurovascular module of any of EEE18-EEE25 below.

[0158] EEE10 is a non-transitory computer-readable medium having stored thereon a plurality of executable instructions that, when executed by a processor of the neurovascular module, cause the neurovascular module to perform operations including receiving an image of a blood vessel having an aneurysm captured by an image capture device, generating a model of the blood vessel using the image, determining a final length of a stent when deployed within the model of the blood vessel to redirect blood flow away from the aneurysm, taking into account foreshortening effects, visually presenting a representation of the stent deployed within the model of the blood vessel, and providing parameters of the stent including stent apposition and pore density.

[0159] EEE11 is the non-transitory computer readable medium of EEE10, wherein the operations further include estimating a radial expansion of the stent within the model of the blood vessel once the final length of the stent has been determined, and wherein visually presenting a representation of the stent includes visually presenting the stent in an expanded state within the model of the blood vessel.

[0160] EEE12 is the non-transitory computer-readable medium of any of EEE10-11, wherein determining the final length of the stent taking into account foreshortening effects includes determining a center point of a distal opening of a blood vessel model, extracting a centerline of the blood vessel model, dividing the centerline into a plurality of segments along the length of the centerline, determining a length of a portion of the stent covering each segment taking into account foreshortening effects upon deployment of the stent, and when determining the length of each of the portions covering the plurality of segments, determining a final proximal point of the stent when deployed in the blood vessel model, wherein the final length of the stent is the distance between the center point of the distal opening and the final proximal point.

[0161] EEE13 is the non-transitory computer readable medium of EEE12, wherein providing parameters of the stent includes generating an indication of the variation in apposition of the stent along the length of the vessel.

[0162] EEE14 is the non-transitory computer readable medium of any of EEE10-13, wherein providing parameters of the stent includes generating an indication of a mean apposition of the stent.

[0163] EEE15 is the non-transitory computer readable medium of any of EEE10-14, wherein the stent is a braided stent, and providing parameters of the stent includes generating an indication of a braid angle after deployment of the braided stent.

[0164] EEE16 is the non-transitory computer readable medium of any of EEE10-15, wherein the operations further include estimating the size of the aneurysm using a model of the blood vessel, and determining, based on the size, that a stent is an optimal neurovascular device for the aneurysm.

[0165] EEE17 is the non-transitory computer readable medium of any of EEE10-16, wherein the operations further include using a model of the blood vessel to determine at least one region where ribbon-like deformation of the stent is most likely to occur upon deployment, and providing information indicative of the at least one region to a medical professional.

[0166] The non-transitory computer readable medium of any of EEE10-16 further performs any of the operations performed by the neurovascular modules of EEE18-25 below.

[0167] EEE18 is a system comprising: an image capture device configured to capture micro-computed tomography (micro-CT) images of a blood vessel having an aneurysm; a neurovascular module in communication with the image capture device, the neurovascular module configured to perform operations including: (i) reconstructing the blood vessel using the micro-CT images and generating a model of the blood vessel; and (ii) determining a final length of a stent when deployed in the model of the blood vessel to redirect blood flow away from the aneurysm, taking into account foreshortening effects; and a display device in communication with the neurovascular module, the display device configured to perform operations including: (i) visually presenting a representation of the stent deployed in the model of the blood vessel; and (ii) generating a display of stent parameters including stent apposition and pore density.

[0168] EEE19 is the system of EEE18, wherein determining the final length of the stent by the neurovascular module includes determining a center point of a distal opening of the blood vessel model, extracting a centerline of the blood vessel model, dividing the centerline into a plurality of segments along the length of the centerline, determining a length of a portion of the stent covering each segment taking into account a shortening effect upon deployment of the stent, and when determining the length of each of the portions covering the plurality of segments, determining a final proximal point of the stent when deployed in the blood vessel model, wherein the final length of the stent is the distance between the center point of the distal opening and the final proximal point.

[0169] EEE20 is any of the systems EEE18-19, wherein the neurovascular module is further configured to perform operations including: using a model of the blood vessel to determine at least one region in which ribbon-like deformation of the stent is most likely to occur upon deployment; and providing information indicative of the at least one region to a medical professional.

[0170] EEE21 is the system of any of EEE18-20, wherein the neurovascular module is further configured to perform operations including generating visualizations of blood flow through blood vessels and aneurysms.

[0171] EEE22 is the system of EEE21, wherein the neurovascular module is further configured to perform operations including generating visualizations of blood flow through the vessel and aneurysm before and after stent deployment.

[0172] EEE23 is any of the systems EEE18-22, wherein the neurovascular module is further configured to perform operations including providing information indicative of intra-aneurysmal inflow velocity after stent deployment, aneurysm occlusion or turnover time, and aneurysm impact zone.

[0173] EEE24 is the system of any of EEE18-23, wherein the neurovascular module is further configured to perform operations including estimating a fatigue safety factor of the stent, taking into account apposition, stent material, and shortening effects.

[0174] EEE25 is the system of any of EEE18 to 24, wherein the neurovascular module is further configured to perform operations including estimating one or more vessel integrity characteristics based on the radiodensity and one or more age parameters of the patient.

Claims

1. receiving, at a processor, an image of a blood vessel having an aneurysm captured by an image capture device; reconstructing, by the processor, the blood vessel using the images to generate a model of the blood vessel; determining, by the processor, a final length of the stent when deployed within the model of the blood vessel to redirect blood flow away from the aneurysm, taking into account foreshortening effects; visually presenting, by the processor, a representation of the stent deployed within the model of the blood vessel; providing, by the processor, parameters of the stent including apposition and pore density of the stent; A method comprising:

2. once the final length of the stent is determined, estimating a radial expansion of the stent within the model of the blood vessel; The method of claim 1 , wherein visually presenting the representation of the stent comprises visually presenting the stent in an expanded state within the model of the blood vessel.

3. Determining the final length of the stent taking into account shortening effects may include: determining a center point of a distal opening of the model of the blood vessel; extracting a centerline of the model of the blood vessel; Dividing the centerline into a plurality of segments along the length of the centerline; determining the length of the stent covering each segment, taking into account the shortening effect when the stent is deployed; determining a final proximal point of the stent when deployed within the model of the blood vessel when determining the length of each of the portions covering the plurality of segments, the final length of the stent being the distance between the center point of the distal opening and the final proximal point; The method of claim 1 , comprising:

4. determining, for each segment of the plurality of segments, the largest radius of a sphere positioned within the model of the vessel with a center of the sphere on the centerline within such segment; selecting the stent having a particular radius based on the determined respective maximum radii of the spheres of the plurality of segments; The method of claim 3 further comprising:

5. Providing the parameters of the stent includes: The method of claim 1 , further comprising generating a representation of the variation in apposition of the stent along the length of the vessel.

6. Providing the parameters of the stent includes: The method of claim 1 , further comprising generating a representation of the mean apposition of the stent.

7. the stent is a braided stent; Providing the parameters of the stent includes: The method of claim 1 , comprising generating a representation of a braid angle after deployment of the braided stent.

8. estimating, by the processor, a size of the aneurysm using the model of the blood vessel; determining, based on the dimensions, that the stent is an optimal neurovascular device for the aneurysm; The method of claim 1 further comprising:

9. determining, by the processor, using the model of the vessel, at least one region where ribbon-like deformation of the stent is most likely to occur upon deployment; providing, by the processor, information indicative of the at least one region to a medical professional; The method of claim 1 further comprising:

10. A non-transitory computer-readable medium having stored thereon a plurality of executable instructions, the plurality of executable instructions, when executed by a processor of a neurovascular module, causing the neurovascular module to: receiving an image of a blood vessel having an aneurysm captured by an image capture device; generating a model of the blood vessel using the image; determining a final length of the stent when deployed within the model of the blood vessel to redirect blood flow away from the aneurysm, taking into account foreshortening effects; visually presenting a representation of the stent deployed within the model of the blood vessel; providing parameters of the stent including apposition and pore density of the stent; A non-transitory computer-readable medium for causing a computer to perform operations including:

11. The operation is once the final length of the stent is determined, estimating a radial expansion of the stent within the model of the blood vessel; 11. The non-transitory computer-readable medium of claim 10, wherein visually presenting the representation of the stent comprises visually presenting the stent in an expanded state within the model of the blood vessel.

12. Determining the final length of the stent taking into account shortening effects may include: determining a center point of a distal opening of the model of the blood vessel; extracting a centerline of the model of the blood vessel; Dividing the centerline into a plurality of segments along the length of the centerline; determining the length of the stent covering each segment, taking into account the shortening effect when the stent is deployed; determining a final proximal point of the stent when deployed within the model of the blood vessel when determining the length of each of the portions covering the plurality of segments, the final length of the stent being the distance between the center point of the distal opening and the final proximal point; 11. The non-transitory computer-readable medium of claim 10, comprising:

13. Providing the parameters of the stent includes: The non-transitory computer-readable medium of claim 12 , further comprising generating a representation of the variation in the apposition of the stent along the length of the blood vessel.

14. Providing the parameters of the stent includes: The non-transitory computer-readable medium of claim 10 , further comprising generating a representation of the average apposition of the stent.

15. the stent is a braided stent; Providing the parameters of the stent includes: The non-transitory computer-readable medium of claim 10 , comprising generating a representation of a braid angle after deployment of the braided stent.

16. The operation is estimating a size of the aneurysm using the model of the blood vessel; determining, based on the dimensions, that the stent is an optimal neurovascular device for the aneurysm; The non-transitory computer-readable medium of claim 10 further comprising:

17. The operation is using the model of the vessel to determine at least one region where ribbon-like deformation of the stent is most likely to occur upon deployment; providing information indicative of the at least one region to a medical professional; The non-transitory computer-readable medium of claim 10 further comprising:

18. an image capture device configured to capture a micro-computed tomography (micro-CT) image of a blood vessel having an aneurysm; a neurovascular module in communication with the image capture device, the neurovascular module configured to perform operations including: (i) reconstructing the blood vessel using the microCT images to generate a model of the blood vessel; and (ii) determining a final length of a stent when deployed within the model of the blood vessel to redirect blood flow away from the aneurysm, taking into account foreshortening effects; a display device in communication with the neurovascular module, the display device configured to perform operations including: (i) visually presenting a representation of the stent deployed within the model of the blood vessel; and (ii) generating a display of parameters of the stent, including the apposition and pore density of the stent; A system comprising:

19. Determining the final length of the stent by the neurovascular module comprises: determining a center point of a distal opening of the model of the blood vessel; extracting a centerline of the model of the blood vessel; Dividing the centerline into a plurality of segments along the length of the centerline; determining the length of the stent covering each segment, taking into account the shortening effect when the stent is deployed; determining a final proximal point of the stent when deployed within the model of the blood vessel when determining the length of each of the portions covering the plurality of segments, the final length of the stent being the distance between the center point of the distal opening and the final proximal point; 20. The system of claim 18, comprising:

20. The neurovascular module comprises: using the model of the vessel to determine at least one region where ribbon-like deformation of the stent is most likely to occur upon deployment; providing information indicative of the at least one region to a medical professional; 20. The system of claim 18, further configured to perform operations including:

21. The neurovascular module comprises: generating a visualization of blood flow through the blood vessel and the aneurysm; 20. The system of claim 18, further configured to perform operations including:

22. The neurovascular module comprises: generating visualizations of blood flow through the vessel and the aneurysm before and after deployment of the stent; 22. The system of claim 21, further configured to perform operations including:

23. The neurovascular module comprises: providing information indicative of intraaneurysmal inflow velocity, aneurysm occlusion or turnover time, and aneurysm impact zone after deployment of the stent; 20. The system of claim 18, further configured to perform operations including:

24. The neurovascular module comprises: estimating a fatigue safety factor of the stent taking into account the apposition, the material of the stent, and the shortening effect; 20. The system of claim 18, further configured to perform operations including:

25. The neurovascular module comprises: estimating one or more vascular integrity characteristics based on radiodensity and one or more age parameters of the patient; 20. The system of claim 18, further configured to perform operations including:

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