# US 10,504,222 B2 · System, method and computer-accessible medium for obtaining and/or determining mesoscopic structure and orientation with fiber tracking > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,504,222 B2 - Document ID: US10504222B2 - Status: Granted - Kind: B2 - Application number: 14/782,390 - Filed: 2014-04-07 - Published: 2016-02-11 - Granted: 2019-12-10 - Earliest claimed: 2013-04-05 - Assignees: New York University; Albert-Ludwigs-Universität Freiburg - Applicants: New York University; Albert-Ludwigs-UniversitäT Freiburg - Inventors: Dmitry S. Novikov; Valerij Kiselev; Marco Reisert - Web page: https://patentgrep.com/patent/US10504222B2 ## CPC - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - A61B 5/0042: {for the brain} - G01R 33/4806: {Functional imaging of brain activation} - 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 7/0012: {Biomedical image inspection} - G06T 7/10: Segmentation; Edge detection (motion-based segmentation G06T7/215) - G06T 7/60: Analysis of geometric attributes - G06T 7/73: using feature-based methods - G01R 33/56341: {Diffusion imaging} - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/20112: Image segmentation details - G06T 2207/30016: Brain - 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) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Exemplary systems, methods, and computer-accessible mediums can be provided that can generate resultant data regarding fiber tract(s) and anatomical structure(s). For example, first information related to imaging data of the anatomical structure(s) can be received. Second information related to a predictive model of further fiber tract(s) can be received. The resultant data can be generated based on the first information, the second information and a fiber cost procedure. ## Claims ### Claim 1 A non-transitory computer-accessible medium having stored thereon computer-executable instructions for generating resultant data regarding a plurality of fiber tracts of at least one anatomical structure, wherein, when a computer hardware arrangement executes the instructions, the computer arrangement is configured to perform procedures comprising: receiving first information related to imaging data of the at least one anatomical structure; receiving second information related to a predictive model of at least one further fiber tract that is based on (i) biophysical mesoscopic model parameters of neuronal tracts, and (ii) geometric model parameters of the neuronal tracts, wherein the biophysical mesoscopic model parameters include a diffusion coefficient, and wherein the neuronal tracts comprise a plurality of segments each having a different set of diffusion parameters that are based on the first information; and generating the resultant data by simultaneously optimizing the biophysical mesoscopic model parameters and the geometric model parameters based on a fiber cost procedure that is based on (i) a polymerization of the segments into the fiber tracts, and (ii) a distance between a measured magnetic resonance imaging (MRI) signal for the segments and a predicted MRI signal for the segments. ### Claim 2 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the imaging data (i) includes MRI data or (ii) is of a region of interest of at least one portion of the at least one anatomical structure. ### Claim 3 (depends on 2) The non-transitory computer-accessible medium of claim 2, wherein the MRI data comprises further data associated with at least one diffusion-weighted signal. ### Claim 4 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the computer arrangement is further configured to generate the resultant data based on an iterative procedure until reaching a predetermined convergence level of both (i) a combination of the first information and the second information, and (ii) the fiber cost procedure. ### Claim 5 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the geometric model parameters include at least one of at least one position or at least one shape of at least one further segment of the fiber tracts. ### Claim 6 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the biophysical mesoscopic parameters vary spatially. ### Claim 7 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein each of the segments has a finite length. ### Claim 8 (depends on 7) The non-transitory computer-accessible medium of claim 7, wherein the computer arrangement is further configured to polymerize the segments to form the fiber tracts using a likelihood-maximization procedure. ### Claim 9 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the fiber cost procedure is further based on at least one of (i) a tendency for neuronal fibers of the predictive model to be straight, (ii) a tendency for the neuronal fibers to avoid sharp turns, (iii) a tendency of the neuronal fibers to be continuous, or (iv) a tendency for the neuronal fibers to have end portions outside white matter regions of the predictive model. ### Claim 10 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the predictive model is further based on at least one of (i) water fraction of neurites, (ii) water fraction of non-neurite compartments, or (iii) diffusion metrics of intra-neurite space, (iv) diffusion metrics of an extra-neurite space. ### Claim 11 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the at least one anatomical structure is a brain. ### Claim 12 (depends on 8) The non-transitory computer-accessible medium of claim 8, wherein the likelihood maximization procedure includes a global likelihood function of all or at least a subset of the parameters. ### Claim 13 (depends on 12) The non-transitory computer-accessible medium of claim 12, wherein the likelihood maximization procedure comprises at least one of (i) a smooth parameter variation, (ii) a gradual parameter variation, (iii) a slow parameter variation along the fiber tracts or (iv) subtracting a quantity proportional to at least one of a mean or weighted mean of the first information in order to decrease a bias in the resultant data. ### Claim 14 (depends on 12) The non-transitory computer-accessible medium of claim 12, wherein the likelihood maximization procedure includes information about a measurement noise. ### Claim 15 (depends on 14) The non-transitory computer-accessible medium of claim 14, wherein the measurement noise at least one of (i) is non-Gaussian or (ii) varies spatially. ### Claim 16 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the predictive model includes local information about at least one of (i) a packing geometry of at least one neurite within an elementary fiber segment, (ii) biophysical parameters of myelin sheaths surrounding axons, or (iii) effects of paramagnetic ions on diffusion metrics. ### Claim 17 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the predictive model includes local information about a fraction of at least one of (i) water, (ii) a size of building blocks of a nervous tissue or (ii) a geometry of the building blocks. ### Claim 18 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the predictive model is further based on axially-symmetric Gaussian diffusion signals. ### Claim 19 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the computer arrangement is further configured to: receive information related to at least one prior; and at least one of (a) prevent the predictive model from building non-fiber like tracts based on the at least one prior, (b) control a number of particles (segments) in the fiber tracts based on the at least one prior, or (c) control a number of connections in the fiber tracts based on the at least one prior. ### Claim 20 (depends on 1) The non-transitory computer-accessible medium of claim 1, wherein the computer arrangement is configured to optimize the biophysical mesoscopic model parameters and the geometric model parameters using an optimization procedure that converges towards a global likelihood maximum. ### Claim 21 A system for generating resultant data regarding a plurality of fiber tracts of at least one anatomical structure, comprising: a computer hardware arrangement configured to: receive first information related to imaging data of the at least one anatomical structure; receive second information related to a predictive model of at least one further fiber tract that is based on (i) biophysical mesoscopic model parameters of neuronal tracts, and (ii) geometric model parameters of the neuronal tracts, wherein the biophysical mesoscopic model parameters include a diffusion coefficient, and wherein the neuronal tracts comprise a plurality of segments each having a different set of diffusion parameters that are based on the first information; and generate the resultant data by simultaneously optimizing the biophysical mesoscopic model parameters and the geometric model parameters based on a fiber cost procedure, wherein the fiber cost procedure is based on (i) a polymerization of the segments into the fiber tracts and (ii) a distance between a measured magnetic resonance imaging (MRI) signal for the segments and a predicted MRI signal for the segments. ### Claim 22 (depends on 21) The system of claim 21, wherein the computer hardware arrangement is configured to optimize the biophysical mesoscopic model parameters and the geometric model parameters using an optimization procedure that converges towards a global likelihood maximum. ### Claim 23 A method for generating resultant data regarding a plurality of fiber tracts of at least one anatomical structure, comprising: receiving first information related to imaging data of the at least one anatomical structure; receiving second information related to a predictive model of at least one further fiber tract that is based on (i) biophysical mesoscopic model parameters of neuronal tracts, and (ii) geometric model parameters of the neuronal tracts, wherein the biophysical mesoscopic model parameters include a diffusion coefficient, and wherein the neuronal tracts comprise a plurality of segments each having a different set of diffusion parameters that are based on the first information; and generating the resultant data by simultaneously optimizing the biophysical mesoscopic model parameters and the geometric model parameters based on a fiber cost procedure, wherein the fiber cost procedure is based on (i) a polymerization of the segments into the fiber tracts and (ii) a distance between a measured magnetic resonance imaging (MRI) signal for the segments and a predicted MRI signal for the segments. ### Claim 24 (depends on 23) The method of claim 23, wherein the optimization of the biophysical mesoscopic model parameters and the geometric model parameters is performed using an optimization procedure that converges towards a global likelihood maximum.