# US 12,181,554 B2 · System and method for quantitative parameter mapping using magnetic resonance images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,181,554 B2 - Document ID: US12181554B2 - Status: Granted - Kind: B2 - Application number: 18/067,489 - Filed: 2022-12-16 - Published: 2023-06-22 - Granted: 2024-12-31 - Earliest claimed: 2021-12-20 - Assignees: Beth Israel Deaconess Medical Center, Inc. - Applicants: Beth Israel Deaconess Medical Center, Inc. - Inventors: Reza Nezafat; Rui Guo - Web page: https://patentgrep.com/patent/US12181554B2 ## CPC - 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)} - G01R 33/50: based on the determination of relaxation times {, e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences} - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/08: Learning methods - G01R 33/5602: {by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse} - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system for quantitative parameter mapping using magnetic resonance (MR) image includes an input for receiving a plurality of weighted MR images of a subject and a corresponding at least one imaging parameter for the plurality of weighted MR images, and a quantitative parameter mapping neural network coupled to the input and configured to estimate at least one tissue parameter and generate at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter. The quantitative parameter mapping neural network can be trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter. The system can further include a display coupled to the quantitative parameter mapping neural network to display the at least one quantitative map. ## Claims ### Claim 1 A system for quantitative parameter mapping using magnetic resonance (MR) images, the system comprising: an input for receiving a plurality of weighted MR images of a subject and a corresponding at least one imaging parameter for the plurality of weighted MR images; a quantitative parameter mapping neural network coupled to the input and configured to estimate at least one tissue parameter and generate at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; and a display coupled to the quantitative parameter mapping neural network and configured to display the at least one quantitative map. ### Claim 2 (depends on 1) The system according to claim 1, wherein the plurality of weighed MR images are one of multi-contrast weighted images, T1-weighted images, T2-weighted images, T2*-weighted images and T1p-weighted images. ### Claim 3 (depends on 1) The system according to claim 1, wherein the at least one tissue parameter is one of T1, T2, T2*, or T1p. ### Claim 4 (depends on 1) The system according to claim 1, wherein the quantitative parameter mapping neural network is a fully compensated (FC) neural network. ### Claim 5 (depends on 1) The system according to claim 1, wherein the quantitative parameter mapping neural network is an encoder-decoder neural network with skip connections. ### Claim 6 (depends on 1) The system according to claim 1, wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of native and post-contrast MR data. ### Claim 7 (depends on 1) The system according to claim 1, wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of numerically simulated MR data and MR phantom data. ### Claim 8 (depends on 1) The system according to claim 1, wherein the at least one confounder is one of angle, heart rate, B0, B1, or off-resonance. ### Claim 9 A method for quantitative parameter mapping using magnetic resonance (MR) images, the method comprising acquiring, using a magnetic resonance imaging (MRI) system, MR data from a subject for a plurality of weighted images using a pulse sequence; generating a plurality of weighted MR images of a subject from the acquired MR data, each of the plurality of weighted MR images having a corresponding at least one imaging parameter; providing, using a processor, the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images to a quantitative parameter mapping neural network, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; generating, using the quantitative parameter mapping neural network, an estimate of at least one tissue parameter and at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images; and displaying, using a display, the at least one quantitative map. ### Claim 10 (depends on 9) The method according to claim 9, wherein the plurality of weighed MR images are one of multi-contrast weighted images, T1-weighted images, T2-weighted images, T2*-weighted images, and T1p-weighted images. ### Claim 11 (depends on 9) The method according to claim 9, wherein the at least one tissue parameter is one of T1, T2, T2*, or T1p. ### Claim 12 (depends on 9) The method according to claim 9, wherein the quantitative parameter mapping neural network is a fully connected (FC) neural network. ### Claim 13 (depends on 9) The method according to claim 9, wherein the quantitative parameter mapping neural network is an encoder-decoder neural network with skip connections. ### Claim 14 (depends on 9) The method according to claim 9, wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of native and post-contrast MR data. ### Claim 15 (depends on 7) The method according to claim 7, wherein the quantification parameter mapping neural network is trained using a set of training data comprising a combination of numerically simulated MR data and MR phantom data. ### Claim 16 (depends on 9) The method according to claim 9, wherein the at least one confounder is one of angle, heart rate, B0, B1, or off-resonance. ### Claim 17 A magnetic resonance imaging (MRI) system comprising: a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject; a magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field; a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject using a coil array; and a computer system programmed to acquire MR data from a subject for a plurality of weighted images using a pulse sequence; generate a plurality of weighted MR images of a subject from the acquired MR data, each of the plurality of weighted MR images having a corresponding at least one imaging parameter; provide the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images to a quantitative parameter mapping neural network, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; and generate, using the quantitative parameter mapping neural network, an estimate of at least one tissue parameter and at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images. ### Claim 18 (depends on 17) The MRI system according to claim 17, wherein the quantitative parameter mapping neural network is a fully connected (FC) neural network. ### Claim 19 (depends on 17) The MRI system according to claim 17, wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of native and post-contrast MR data. ### Claim 20 (depends on 17) The MRI system according to claim 17, wherein the quantification parameter mapping neural network is trained using a set of training data comprising a combination of numerically simulated MR data and MR phantom data.