# US 2021/0059623 A1 · 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 2021/0059623 A1 - Document ID: US20210059623A1 - Status: Published application - Kind: A1 - Application number: 17/083,089 - Filed: 2020-10-28 - Published: 2021-03-04 - Earliest claimed: 2019-05-03 - Assignees: iSchemaView, Inc. - Applicants: iSchemaView, Inc. - Inventors: Matus Straka; Shalini Ambika Amukotuwa; Roland Bammer - Web page: https://patentgrep.com/patent/US20210059623A1 ## CPC - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - A61B 5/02007: {Evaluating blood vessel condition, e.g. elasticity, compliance} - 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 2207/10072: Tomographic images - 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: accessing image data captured by an imaging device, the image data including information corresponding to one or more images of a brain of an individual and the information including voxels with individual voxels being associated with an intensity value; aligning, using one or more hardware processors, one or more templates that indicate at least one of bones or blood vessels of a human head to features related to the brain of the individual based on the image data; identifying, using the one or more hardware processors and based on the one or more templates, one or more portions of the image data that correspond to bone included in the head of the individual; removing, using the one or more hardware processors, the one or more portions of the image data to produce modified image data; identifying, using the one or more hardware processors, one or more regions of the modified image data that correspond to intracranial blood vessels of the individual; determining, using the one or more hardware processors, a first group of the intracranial blood vessels having first diameters included in a first range of values; determining, using the one or more hardware processors, a second group of intracranial blood vessels having second diameters included in a second range of values that is different from the first range of values; determining, using the one or more hardware processors, one or more first measures of blood vessel density for one or more first blood vessels that are included in the first group of intracranial blood vessels and that are located in a first region of a first hemisphere of the brain of the individual; determining, using the one or more hardware processors, one or more second measures of blood vessel density for one or more second blood vessels that are included in the first group of intracranial blood vessels and that are located in a second region of a second hemisphere of the brain of the individual, the one or more second blood vessels being substantially symmetrically located in the brain of the individual with respect to the one or more first blood vessels; determining, using the one or more hardware processors, one or more measures of difference between the one or more first measures of blood vessel density and the one or more second measures of blood vessel density; determining, using the one or more processors and based on the one or more measures of difference, that a probability of an abnormality being present with respect to a blood vessel included in the first region or the second region is greater than a threshold probability; and generating, using the one or more processors, user interface data that corresponds to an image that includes a user interface element that highlights the blood vessel. ### Claim 2 (depends on 1) The method of claim 1, further comprising: identifying, using the one or more processors and the image data, the 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; determining, using the one or more processors, a starting point with respect to the blood vessel, the starting point indicating a first voxel that is included in a path of the blood vessel within the brain of the individual; determining, using the one or more processors, 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 data related to the blood vessel and background portions of the image data; determining, using the one or more processors, an endpoint of the blood vessel based at least partly on the template; determining, using the one or more processors, that the blood vessel terminates before the endpoint; and determining, using the one or more processors and based on the blood vessel terminating before the endpoint, an additional probability that the abnormality is present with respect to the blood vessel. ### Claim 3 (depends on 2) The method of claim 2, wherein the path along the blood vessel corresponds to a shortest path that is determined based on vesselness values of voxels of the image data in locations that correspond to the location of the blood vessel, the vesselness values corresponding to eigenvalues of a Hessian filter. ### Claim 4 (depends on 2) The method of claim 2, 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 5 (depends on 2) The method of claim 2, wherein the path of the blood vessel terminates based on the cost of the shortest path exceeding a threshold cost. ### Claim 6 (depends on 2) The method of claim 2, 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 7 (depends on 1) The method of claim 1, wherein: the imaging device is a computed tomography angiography (CTA) imaging device; the image data is formatted according to a Digital Imaging and Communications in Medicine (DICOM) standard; and the image is based on a plurality of image slices obtained by the imaging device, individual image slices of the plurality of image slices having a thickness from at least about 0.5 mm to no greater than about 5 mm. ### Claim 8 (depends on 1) The method of claim 1, further comprising: generating, using the one or more hardware processors, a template of the one or more templates by aggregating a number of images obtained with respect to brains of a number of individuals and determining locations of at least one of bones or blood vessels associated with the brains of the number of individuals; and wherein aligning the one or more templates to features related to the brain of the individual includes a registration process that identifies voxels included in the image data that correspond with additional voxels included in the one or more templates. ### Claim 9 (depends on 1) The method of claim 1, wherein the one or more measures of difference between the one or more first measures of blood vessel density and the one or more second measures of blood vessel density correspond to a reduction in density between a first blood vessel located in the first region and a second blood vessel located in the second region. ### Claim 10 A system comprising: at least one hardware processor; one or more computer-readable medium storing computer-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising: accessing image data captured by an imaging device, the image data including information corresponding to one or more images of a brain of an individual and the information including voxels with individual voxels being associated with an intensity value; aligning one or more templates that indicate at least one of bones or blood vessels of a human head to features related to the brain of the individual based on the image data; identifying, based on the one or more templates, one or more portions of the image data that correspond to bone included in the head of the individual; removing the one or more portions of the image data to produce first modified image data; identifying one or more regions of the modified image data that correspond to intracranial blood vessels of the individual; determining a first group of the intracranial blood vessels having first diameters included in a first range of values; determining a second group of intracranial blood vessels having second diameters included in a second range of values that is different from the first range of values; determining one or more first measures of blood vessel density for one or more first blood vessels that are included in the first group of intracranial blood vessels and that are located in a first region of a first hemisphere of the brain of the individual; determining one or more second measures of blood vessel density for one or more second blood vessels that are included in the first group of intracranial blood vessels and that are located in a second region of a second hemisphere of the brain of the individual, the one or more second blood vessels being substantially symmetrically located in the brain of the individual with respect to the one or more first blood vessels; determining one or more measures of difference between the one or more first measures of blood vessel density and the one or more second measures of blood vessel density; determining, based on the one or more measures of difference, that a probability of an abnormality being present with respect to a blood vessel located in the brain of the individual is greater than a threshold probability; and generating user interface data that corresponds to an image that includes a user interface element that highlights the blood vessel. ### Claim 11 (depends on 10) The system of claim 10, wherein the one or more computer-readable medium store computer-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform additional operations comprising: analyzing the one or more measures of difference with respect to a schema that includes a plurality of tiers with each tier corresponding to a range of amounts of reduction in blood vessel density between blood vessels located in the first hemisphere and counterpart blood vessels located in the second hemisphere; and determining a tier of the schema that corresponds to the one or more measures of difference; and determining whether to evaluate one or more third measures of blood vessel density of one or more third blood vessels located in a third region located in the first hemisphere of the brain with respect to one or more fourth measures of blood vessel density of one or more fourth blood vessels located in a fourth region located in the second hemisphere of the brain, the one or more fourth blood vessels being substantially symmetrically located in the brain of the individual with respect to the one or more third blood vessels. ### Claim 12 (depends on 11) The system of claim 11, wherein the one or more computer-readable medium store computer-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform additional operations comprising: determining, based on the tier corresponding to a threshold criteria, the one or more third measures of blood vessel density for the one or more third blood and the one or more fourth measures of blood vessel density for the one or more fourth blood vessels; determining one or more additional measures of difference between the one or more third measures of blood vessel density and the one or more fourth measures of blood vessel density; and determining, based on the one or more additional measures of difference, that the probability of the abnormality being present with respect to the blood vessel located in the brain of the individual is greater than the threshold probability. ### Claim 13 (depends on 12) The system of claim 12, wherein one or more computer-readable medium store computer-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform additional operations comprising: analyzing the one or more additional measures of difference with respect to the schema; determining an additional tier of the schema that corresponds to the one or more additional measures of difference; and determining whether to evaluate one or more fifth measures of blood vessel density of one or more fifth blood vessels located in a fifth region located in the first hemisphere of the brain with respect to one or more sixth measures of blood vessel density of one or more sixth blood vessels located in a sixth region located in the second hemisphere of the brain, the one or more sixth blood vessels being substantially symmetrically located in the brain of the individual with respect to the one or more fifth blood vessels. ### Claim 14 (depends on 13) The system of claim 13, wherein the one or more computer-readable medium store computer-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform additional operations comprising: determining, based on the additional tier corresponding to an additional threshold criteria, the one or more fifth measures of blood vessel density for the one or more fifth blood vessels and the one or more sixth measures of blood vessel density for the one or more sixth blood; determining one or more further measures of difference between the one or more fifth measures of blood vessel density and the one or more sixth measures of blood vessel density; and determining, based on the one or more further measures of difference, that the probability of the abnormality being present with respect to the blood vessel located in the brain of the individual is greater than the threshold probability. ### Claim 15 (depends on 14) The system of claim 14, wherein the first region and the second region correspond to an intracranial segment of the carotid artery and a proximal portion of the M1 segment of the middle cerebral artery, the third region and the fourth region correspond to middle to distal portions of the M1 segment of the middle cerebral artery, and the fifth region and the sixth region correspond to an M2 segment of the middle cerebral artery. ### Claim 16 (depends on 10) The system of claim 10, wherein: the one or more computer-readable medium store computer-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform additional operations comprising: determining that the probability of the abnormality being present with respect to the blood vessel is within a range of probabilities of a plurality of ranges of probabilities, each range of the plurality of ranges of probabilities corresponding to a respective user interface element having a respective color; and the user interface element has the respective color corresponding to the range of probabilities. ### Claim 17 (depends on 16) The system of claim 16, wherein each range of probabilities of the plurality of ranges of probabilities corresponds to a respective amount of reduction in blood vessel density between a region located in the first hemisphere of the brain of the individual and a counterpart region located in the second hemisphere of the brain of the individual. ### Claim 18 A method comprising: accessing image data captured by an imaging device, the image data including information corresponding to one or more images of a brain of an individual and the information including voxels with individual voxels being associated with an intensity value; applying, using one or more hardware processors, a first template to the image data to determine first blood vessels located in a first region of a first hemisphere of the brain of the individual, the first blood vessels having first diameters included in a range of values; applying, using the one or more hardware processors, a second template to the image data to determine second blood vessels located in a first counterpart region of a second hemisphere of the brain of the individual, the second blood vessels having second diameters included in the range of values; determining, using the one or more hardware processors and based on the image data, first density values of the first blood vessels and second density values of the second blood vessels; determining, using the one or more hardware processors, a measure of difference between the first density values and the second density values; determining, using the one or more hardware processors, a tier of a schema that corresponds to the measure of difference, the schema including a plurality of tiers with each tier corresponding to a range of amounts of reduction in blood vessel density between blood vessels located in the first hemisphere and counterpart blood vessels located in the second hemisphere; determining, using the one or more hardware processors and based on the tier that corresponds to the measure of difference, whether to evaluate first additional blood vessels densities of first additional blood vessels located in a second region of the first hemisphere of the brain with respect to second additional blood vessel densities of second additional blood vessels location in a second counterpart region of the second hemisphere of the brain; and generating, using the one or more hardware processors, user interface data including one or more images of the brain of the individual and indicating differences between the first density values and the second density values. ### Claim 19 (depends on 18) The method of claim 18, further comprising: determining, using the one or more hardware processors, a first sum of the first density values and a second sum of the second density values; and determining, using the one or more hardware processors, a ratio between the first sum of the first density values and the second sum of the second density values; wherein the measure of difference between the first density values and the second density values is based on the ratio. ### Claim 20 (depends on 18) The method of claim 18, further comprising: applying, using the one or more hardware processors, one or more additional templates to the image data to determine one or more portions of the image data that correspond to one or more regions of bone associated with the head of the individual; and removing, using the one or more hardware processors, the one or more portions of the image data that correspond to the one or more regions of bone to produce modified image data; wherein the first density values and the second density values are determined based on the modified image data.