# US 12,182,969 B2 · System, method and computer-accessible medium for detecting functional disorder(s) or aging progression in magnetic resonance imaging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,182,969 B2 - Document ID: US12182969B2 - Status: Granted - Kind: B2 - Application number: 17/347,917 - Filed: 2021-06-15 - Published: 2022-02-10 - Granted: 2024-12-31 - Earliest claimed: 2019-08-23 - Assignees: The Trustees of Columbia University in the City of New York - Applicants: The Trustees of Columbia University In the City of New York - Inventors: Jia Guo; Scott A. Small - Web page: https://patentgrep.com/patent/US12182969B2 ## CPC - G16H 30/40: for processing medical images, e.g. editing - G06T 5/50: using two or more images, e.g. averaging or subtraction - G06T 5/60: using machine learning, e.g. neural networks - G06T 5/92: based on global image properties - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - 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 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/10096: Dynamic contrast-enhanced magnetic resonance imaging [DCE-MRI] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30016: Brain - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract An exemplary system, method, and computer-accessible medium for detection of functional disorder(s) or aging progression of patient(s) can be provided which can include, for example, receiving magnetic resonance imaging (MRI) information of the portion(s), generating gadolinium (“Gd”) enhanced map(s) based on the MRI information using a machine learning procedure(s), and detecting the functional disorder(s) or aging progression of the patient(s) based on the Gd enhanced map(s). The Gd enhanced map(s) can be a full dosage Gd enhanced map which can be a full dosage Gd enhanced cerebral blood volume map(s). The machine learning procedure can be a convolutional neural network. The MRI information can include (i) a low-dosage Gd MRI scan(s), and/or (ii) a Gd-free MRI scan(s). Functional disorder(s) or age progression can include a neurodegenerative disease, a neuropsychiatric disease, a neurodevelopment disorder or aging. ## Claims ### Claim 1 A non-transitory computer-accessible medium having stored thereon computer executable instructions for detecting at least one functional neurological disorder or cognitive aging progression of at least one patient, wherein, when a computing arrangement executes the instructions, the computing arrangement is configured to perform procedures comprising: receiving magnetic resonance imaging (MRI) information of: a. only one gadolinium (Gd)-free pre-contrast T1W structural MRI scan of at least one portion of the at least one patient, or b. only gadolinium (Gd)-free pre-contrast T1W structural MRI scans of at least one portion of the at least one patient; generating at least one Gd enhanced cerebral blood volume map of the at least one portion based on the MRI information using at least one machine learning procedure, wherein the at least one machine learning procedure includes at least one attention unit; and detecting the at least one functional neurological disorder or cognitive aging progression of the at least one patient based on the at least one Gd enhanced cerebral blood volume map. ### Claim 2 (depends on 1) The computer-accessible medium of claim 1, wherein the at least one Gd enhanced cerebral blood volume map is a full dosage Gd enhanced cerebral blood volume map. ### Claim 3 (depends on 1) The computer-accessible medium of claim 1, wherein the at least one machine learning procedure is a convolutional neural network. ### Claim 4 (depends on 1) The computer-accessible medium of claim 1, wherein the at least one functional neurological disorder or cognitive aging progression includes at least one of (i) a neurodegenerative disorder, (ii) a neuropsychiatric disease, (iii) cognitive aging. ### Claim 5 (depends on 4) The computer-accessible medium of claim 4, wherein the neurodegenerative disorder includes Alzheimer's disease. ### Claim 6 (depends on 4) The computer-accessible medium of claim 4, wherein the neuropsychiatric disease includes Schizophrenia. ### Claim 7 (depends on 1) The computer-accessible medium of claim 1, wherein the at least one machine learning procedure includes at least one residual unit. ### Claim 8 (depends on 7) The computer-accessible medium of claim 7, wherein the at least one machine learning procedure includes at least five layers. ### Claim 9 (depends on 8) The computer-accessible medium of claim 8, wherein the at least one machine learning procedure includes at least one contraction path configured to encode at least one high resolution image into at least one low resolution representation. ### Claim 10 (depends on 9) The computer-accessible medium of claim 9, wherein the at least one machine learning procedure includes at least one expansion path configured to decode the at least one low resolution representation into at least one further high-resolution image. ### Claim 11 (depends on 1) The computer-accessible medium of claim 1, wherein the at least one machine learning procedure includes max-pooling and upsampling. ### Claim 12 (depends on 11) The computer-accessible medium of claim 11, wherein the computer arrangement is further configured to perform the max-pooling and the upsampling using a factor of 2. ### Claim 13 (depends on 12) The computer-accessible medium of claim 12, wherein the at least one machine learning procedure includes at least one batch normalization layer and at least one rectified linear unit layer. ### Claim 14 (depends on 1) The computer-accessible medium of claim 1, wherein the at least one portion is at least one section of a brain of the at least one patient. ### Claim 15 (depends on 1) The computer-accessible medium of claim 1, wherein the MRI information excludes data for a contrast-agent scan. ### Claim 16 (depends on 1) The computer-accessible medium of claim 1, wherein the at least one Gd enhanced map is generated without information associated with data for a contrast-agent scan. ### Claim 17 A non-transitory computer-accessible medium having stored thereon computer executable instructions for detecting at least one functional neurological disorder or cognitive aging progression of at least one patient, wherein, when a computing arrangement executes the instructions, the computing arrangement is configured to perform procedures comprising: receiving magnetic resonance imaging (MRI) information of: a. only one gadolinium (Gd)-free pre-contrast T1W structural MRI scan of at least one portion of the at least one patient, or b. only gadolinium (Gd)-free pre-contrast T1W structural MRI scans of at least one portion of the at least one patient; generating at least one Gd enhanced cerebral blood volume map of the at least one portion based on the MRI information using at least one machine learning procedure; and detecting the at least one functional neurological disorder or cognitive aging progression of the at least one patient based on the at least one Gd enhanced cerebral blood volume map, wherein the at least one machine learning procedure includes at least five encoding layers and at least five decoding layers. ### Claim 18 (depends on 17) The computer-accessible medium of claim 17, wherein each of the at least five encoding layers and each of the at least five decoding layers includes a residual connection. ### Claim 19 (depends on 17) The computer-accessible medium of claim 17, wherein each of the at least five encoding layers and each of the at least five decoding layers include two series of 3×3 two dimensional convolutions. ### Claim 20 (depends on 17) The computer-accessible medium of claim 17, wherein (i) each of the at least five encoding layers is followed by a 2×2 max-pooling layer, and (ii) each of the at least five decoding layers is followed by at least one 2×2 upsampling layers. ### Claim 21 A method for detecting at least one functional neurological disorder or cognitive aging progression of at least one patient, comprising: receiving magnetic resonance imaging (MRI) information of: a. only one gadolinium (Gd)-free pre-contrast T1W structural MRI scan of at least one portion of the at least one patient, or b. only gadolinium (Gd)-free pre-contrast T1W structural MRI scans of at least one portion of the at least one patient; using a computer hardware arrangement, generating at least one Gd enhanced cerebral blood volume map of the at least one portion based on the MRI information using at least one machine learning procedure, wherein the at least one machine learning procedure includes at least one attention unit; and using the computer hardware arrangement, detecting the at least one functional neurological disorder or cognitive aging progression of the at least one patient based on the at least one Gd enhanced cerebral blood volume map. ### Claim 22 A system for detecting at least one functional neurological disorder or cognitive aging progression of at least one patient, comprising: a computer hardware arrangement configured to: receive magnetic resonance imaging (MRI) information of: a. only one gadolinium (Gd)-free pre-contrast T1W structural MRI scan of at least one portion of the at least one patient, or b. only gadolinium (Gd)-free pre-contrast T1W structural MRI scans of at least one portion of the at least one patient, generate at least one Gd enhanced cerebral blood volume map of the at least one portion based on the MRI information using at least one machine learning procedure, wherein the at least one machine learning procedure includes at least one attention unit, and detect the at least one functional neurological disorder or cognitive aging progression of the at least one patient based on the at least one Gd enhanced cerebral blood volume map.