# US 11,443,429 B2 · Atlas registration for resting state network mapping in patients with brain tumors > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,429 B2 - Document ID: US11443429B2 - Status: Granted - Kind: B2 - Application number: 16/889,192 - Filed: 2020-06-01 - Published: 2020-12-03 - Granted: 2022-09-13 - Earliest claimed: 2019-05-30 - Assignees: Washington University - Applicants: Ki Yun Park; Abraham Snyder; Eric Leuthardt - Inventors: Ki Yun Park; Abraham Snyder; Eric Leuthardt - Web page: https://patentgrep.com/patent/US11443429B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06T 7/0014: {using an image reference approach} - A61B 5/0042: {for the brain} - A61B 5/0263: {using NMR} - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - G01R 33/5608: {Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T)} - G06T 3/18: Image warping, e.g. rearranging pixels individually - G06T 7/11: Region-based segmentation - G06T 7/149: involving deformable models, e.g. active contour models - G06T 7/30: Determination of transform parameters for the alignment of images, i.e. image registration - A61B 2576/026: for the brain - G01R 33/4806: {Functional imaging of brain activation} - G01R 33/5616: {using gradient refocusing, e.g. EPI} - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/20128: Atlas-based segmentation - G06T 2207/30016: Brain - G06T 2207/30096: Tumor; Lesion - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) ## Abstract A method for mapping brain function of a subject includes generating a lesion mask using a search light algorithm based on a plurality of anatomical images of the subject. The plurality of anatomical images are registered with atlas images by nonlinear atlas registration using the generated lesion mask to generate a warping map. A plurality of functional images of the subject are resampled using the warping map to generate a functional map, functional connectivity is computed using the functional map and a multi-layer perceptron. ## Claims ### Claim 1 A method for mapping brain function of a subject, the method comprising: receiving a plurality of functional images of a brain of the subject ascertained during a resting state; receiving a plurality of anatomical images of the brain of the subject; defining one or more regions in the plurality of anatomical images to be excluded from atlas registration of the plurality of anatomical images; registering the plurality of anatomical images with atlas images by nonlinear atlas registration with the defined one or more regions excluded to generate a warping map; registering the plurality of functional images with atlas images using the warping map to guide the registration; and computing functional connectivity of the brain of the subject using the registered functional images. ### Claim 2 (depends on 1) The method of claim 1, further generating a resting state network map of the brain of the subject based on the computed functional connectivity of the brain of the subject. ### Claim 3 (depends on 2) The method of claim 2, wherein generating a resting state network map of the brain of the subject comprises inputting the computed functional connectivity into a neural network trained to estimate class membership for voxels of functional images of the brain to a plurality of resting state networks. ### Claim 4 (depends on 3) The method of claim 3, wherein the neural network comprises a multilayer perceptron. ### Claim 5 (depends on 1) The method of claim 1, wherein the plurality of functional images of a brain of the subject comprises resting state functional magnetic resonance imaging (rs-fMRI) images. ### Claim 6 (depends on 1) The method of claim 1, wherein defining one or more regions to be excluded from atlas registration comprises executing a searchlight algorithm programmed to compare voxels of the plurality of anatomical images to corresponding voxels of a plurality of atlas images. ### Claim 7 (depends on 6) The method of claim 6, wherein the searchlight algorithm is programmed to identify one or more lesions of the brain of the subject based on the comparison of voxels of the plurality of anatomical images to corresponding voxels of the plurality of atlas images and to use the identified lesions as the one or more regions in the plurality of anatomical images to be excluded from atlas registration of the plurality of anatomical images. ### Claim 8 A computing device for use in a system for mapping brain function of a subject, the computing device comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: receive a plurality of functional images of a brain of the subject ascertained during a resting state; receive a plurality of anatomical images of the brain of the subject; define one or more regions in the plurality of anatomical images to be excluded from atlas registration of the plurality of anatomical images; register the plurality of anatomical images with atlas images by nonlinear atlas registration with the defined one or more regions excluded to generate a warping map; register the plurality of functional images with atlas images using the warping map to guide the registration; and compute functional connectivity of the brain of the subject using the registered functional images. ### Claim 9 (depends on 8) The computing device of claim 8, wherein the memory includes instructions that cause the processor to generate a resting state network map of the brain of the subject based on the computed functional connectivity of the brain of the subject. ### Claim 10 (depends on 9) The computing device of claim 9, wherein the memory includes instructions that cause the processor to generate the resting state network map of the brain of the subject by inputting the computed functional connectivity into a neural network trained to estimate class membership for voxels of functional images of the brain to a plurality of resting state networks. ### Claim 11 (depends on 10) The computing device of claim 10, wherein the neural network comprises a multilayer perceptron. ### Claim 12 (depends on 8) The computing device of claim 8, wherein the plurality of functional images of a brain of the subject comprises resting state functional magnetic resonance imaging (rs-fMRI) images. ### Claim 13 (depends on 8) The computing device of claim 8, wherein the memory includes instructions that cause the processor to define one or more regions to be excluded from atlas registration by executing a searchlight algorithm programmed to compare voxels of the plurality of anatomical images to corresponding voxels of a plurality of atlas images. ### Claim 14 (depends on 13) The computing device of claim 13, wherein the searchlight algorithm is programmed to identify one or more lesions or other abnormalities of the brain of the subject based on the comparison of voxels of the plurality of anatomical images to corresponding voxels of the plurality of atlas images and to use the identified lesions or other abnormalities as the one or more regions in the plurality of anatomical images to be excluded from atlas registration of the plurality of anatomical images. ### Claim 15 A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the processor to: receive a plurality of functional images of a brain of the subject; receive a plurality of anatomical images of the brain of the subject; define one or more regions in the plurality of anatomical images to be excluded from atlas registration of the plurality of anatomical images; register the plurality of anatomical images with atlas images by nonlinear atlas registration with the defined one or more regions excluded to generate a warping map; register the plurality of functional images with atlas images using the warping map to guide the registration; and compute functional connectivity of the brain of the subject using the registered functional images. ### Claim 16 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the instructions further cause the processor to generate a resting state network map of the brain of the subject based on the computed functional connectivity of the brain of the subject. ### Claim 17 (depends on 16) The non-transitory computer readable medium of claim 16, wherein the instructions cause the processor to generate the resting state network map of the brain of the subject by inputting the computed functional connectivity into a neural network trained to estimate class membership for voxels of functional images of the brain to a plurality of resting state networks. ### Claim 18 (depends on 17) The non-transitory computer readable medium of claim 17, wherein the neural network comprises a multilayer perceptron. ### Claim 19 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the instructions cause the processor to define one or more regions to be excluded from atlas registration by executing a searchlight algorithm programmed to compare voxels of the plurality of anatomical images to corresponding voxels of a plurality of atlas images. ### Claim 20 (depends on 19) The non-transitory computer readable medium of claim 19, wherein the searchlight algorithm is programmed to identify one or more lesions or other abnormalities of the brain of the subject based on the comparison of voxels of the plurality of anatomical images to corresponding voxels of the plurality of atlas images and to use the identified lesions or other abnormalities as the one or more regions in the plurality of anatomical images to be excluded from atlas registration of the plurality of anatomical images.