# US 12,758,174 B2 · Analysis of intracranial blood vessels > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,758,174 B2 - Document ID: US12758174B2 - Status: Granted - Kind: B2 - Application number: 18/959,502 - Filed: 2024-11-25 - Published: 2025-05-22 - Granted: 2026-10-13 - Earliest claimed: 2019-05-03 - Assignees: iSchemaView, Inc. - Applicants: iSchemaView, Inc. - Inventors: Matúš Straka; Shalini Ambika Amukotuwa; Roland Bammer - Web page: https://patentgrep.com/patent/US12758174B2 ## CPC - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - A61B 5/7246: {using correlation, e.g. template matching or determination of similarity} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/463: {characterised by displaying multiple images or images and diagnostic data on one display} - A61B 6/501: {for diagnosis of the head, e.g. neuroimaging or craniography} - A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data} - G06T 7/0012: {Biomedical image inspection} - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 50/50: for simulation or modelling of medical disorders - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G06T 2200/24: involving graphical user interfaces [GUIs] - G06T 2207/30016: Brain - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Abnormalities of blood vessels associated with brains of individuals can be detected by analyzing images that include the brains of the individuals. Blood vessels included in the images can be identified and densities of blood vessels across hemispheres can be determined. Differences between densities of blood vessels in different hemispheres of the brains of the individuals can be used to determine a probability of an abnormality with respect to one or more blood vessels associated with the brains of the individuals. User interfaces can be generated indicating possible abnormalities associated with the brains of the individuals. ## Claims ### Claim 1 A method comprising: obtaining an image of a brain of an individual; identifying, using the image, a blood vessel located in the brain of an individual based on a template that indicates locations of a number of blood vessels located in a human brain, the template including a composite image generated based on a number of reference images with the number of reference images being obtained from a number of individuals and including brains and other features associated with the brains of the number of individuals; determining a starting point with respect to the blood vessel, the starting point indicating a first voxel of the image that is included in a path of the blood vessel within the brain of the individual; determining a path along the blood vessel based on density values corresponding to the blood vessel, the density values indicating an amount of contrast between portions of the image related to the blood vessel and background portions of the image; determining an endpoint of the blood vessel based at least partly on the template; determining that the blood vessel terminates before the endpoint; and determining, based on the blood vessel terminating before the endpoint, a probability that an abnormality is present with respect to the blood vessel. ### Claim 2 (depends on 1) The method of claim 1, wherein the path along the blood vessel corresponds to a shortest path that is determined based on vesselness values of voxels of the image in locations that correspond to the location of the blood vessel, the vesselness values corresponding to eigenvalues of a Hessian filter. ### Claim 3 (depends on 1) The method of claim 1, wherein the starting point of the blood vessel corresponds to a voxel having a highest vesselness value, the voxel being located in a proximal portion of the blood vessel. ### Claim 4 (depends on 1) The method of claim 1, wherein the path of the blood vessel terminates based on the cost of the shortest path exceeding a threshold cost. ### Claim 5 (depends on 1) The method of claim 1, wherein the path of the blood vessel terminates in response to determining that no voxels remain along the path of the blood vessel that have a finite cost to pass through. ### Claim 6 (depends on 1) The method of claim 1, wherein the starting point is located in the proximal end of the middle cerebral artery and the endpoint is located in a distal portion of the middle cerebral artery. ### Claim 7 (depends on 1) The method of claim 1, comprising: identifying, based on the image, one or more first additional blood vessels located in a first hemisphere of the brain; identifying, based on the image, one or more second additional blood vessels located in a second hemisphere of the brain; and determining a measure corresponding to an amount of difference between one or more first values of one or more first densities of the one or more first additional blood vessels and one or more second values of one or more second densities of the one or more second additional blood vessels. ### Claim 8 (depends on 7) The method of claim 7, wherein the probability that the abnormality is present with respect to the blood vessel is based on the blood vessel terminating before the endpoint and the measure corresponding to the amount of difference between the one or more first values of the one or more first densities and the one or more second values of the one or more second densities. ### Claim 9 A system comprising: at least one hardware processor, a non-transitory computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising: obtaining an image of a brain of an individual; identifying, using the image, a blood vessel located in the brain of an individual based on a template that indicates locations of a number of blood vessels located in a human brain, the template including a composite image generated based on a number of reference images with the number of reference images being obtained from a number of individuals and including brains and other features associated with the brains of the number of individuals; determining a starting point with respect to the blood vessel, the starting point indicating a first voxel of the image that is included in a path of the blood vessel within the brain of the individual; determining a path along the blood vessel based on density values corresponding to the blood vessel, the density values indicating an amount of contrast between portions of the image related to the blood vessel and background portions of the image; determining an endpoint of the blood vessel based at least partly on the template; determining that the blood vessel terminates before the endpoint; and determining, based on the blood vessel terminating before the endpoint, a probability that an abnormality is present with respect to the blood vessel. ### Claim 10 (depends on 9) The system of claim 9, wherein the path along the blood vessel corresponds to a shortest path that is determined based on vesselness values of voxels of the image in locations that correspond to the location of the blood vessel, the vesselness values corresponding to eigenvalues of a Hessian filter. ### Claim 11 (depends on 9) The system of claim 9, wherein the starting point of the blood vessel corresponds to a voxel having a highest vesselness value, the voxel being located in a proximal portion of the blood vessel. ### Claim 12 (depends on 9) The system of claim 9, wherein the non-transitory computer-readable medium stores additional instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform additional operations comprising: aligning the image of the brain of the individual with the template by performing a registration process that includes analyzing the image of the brain of the individual to determine features included in the image of the brain of the individual that correspond to additional features included in the template. ### Claim 13 (depends on 9) The system of claim 9, wherein the path of the blood vessel terminates in response to determining that no voxels remain along the path of the blood vessel that have a finite cost to pass through. ### Claim 14 (depends on 9) The system of claim 9, wherein the starting point is located in the proximal end of the middle cerebral artery and the endpoint is located in a distal portion of the middle cerebral artery. ### Claim 15 One or more computer-readable storage media storing computer-readable instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to perform operations comprising: obtaining an image of a brain of an individual; identifying, using the image, a blood vessel located in the brain of an individual based on a template that indicates locations of a number of blood vessels located in a human brain, the template including a composite image generated based on a number of reference images with the number of reference images being obtained from a number of individuals and including brains and other features associated with the brains of the number of individuals; determining a starting point with respect to the blood vessel, the starting point indicating a first voxel of the image that is included in a path of the blood vessel within the brain of the individual; determining a path along the blood vessel based on density values corresponding to the blood vessel, the density values indicating an amount of contrast between portions of the image related to the blood vessel and background portions of the image; determining an endpoint of the blood vessel based at least partly on the template; determining that the blood vessel terminates before the endpoint; and determining, based on the blood vessel terminating before the endpoint, a probability that an abnormality is present with respect to the blood vessel. ### Claim 16 (depends on 15) The one or more computer-readable storage media of claim 15, wherein the path along the blood vessel corresponds to a shortest path that is determined based on vesselness values of voxels of the image in locations that correspond to the location of the blood vessel, the vesselness values corresponding to eigenvalues of a Hessian filter. ### Claim 17 (depends on 15) The one or more computer-readable storage media of claim 15, wherein the starting point of the blood vessel corresponds to a voxel having a highest vesselness value, the voxel being located in a proximal portion of the blood vessel. ### Claim 18 (depends on 15) The one or more computer-readable storage media of claim 15, wherein the path of the blood vessel terminates based on the cost of the shortest path exceeding a threshold cost. ### Claim 19 (depends on 15) The one or more computer-readable storage media of claim 15, wherein the path of the blood vessel terminates in response to determining that no voxels remain along the path of the blood vessel that have a finite cost to pass through. ### Claim 20 (depends on 15) The one or more computer-readable storage media of claim 15, wherein the starting point is located in the proximal end of the middle cerebral artery and the endpoint is located in a distal portion of the middle cerebral artery.