IP Library › Granted Patent US 12,320,880
Granted Patent B2
US 12,320,880 · App. 18/568,056 · Granted Jun 3, 2025

Maxwell parallel imaging

Inventors: Matteo Alessandro Francavilla (San Mateo, CA); Jorge Fernandez Villena (Lisbon, PT); Stamatios Lefkimmiatis (Attiki, GR); Athanasios Polymeridis (Salonika, GR); Doruk Tayli (Mountain View, CA)
Assignee: Q Bio, Inc.
G01R33/5611G01R33/50G01R33/56581
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Quick Facts
Patent No.
US 12,320,880
App. No.
18/568,056
Filed
Dec 7, 2023
Granted
Jun 3, 2025
Kind
B2
Art Unit
2858
USPC
324/309
Abstract

During operation, a computer system may acquire magnetic resonance (MR) signals associated with a sample from a measurement device or memory. Then, the computer system may access a predetermined set of coil magnetic field basis vectors associated with a surface surrounding the sample, where coil sensitivities of coils in the measurement device are represented by weighted superpositions of the predetermined set of coil magnetic field basis vectors using coefficients, and where the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations. Next, the computer system may solve, on a voxel-by-voxel basis for voxels associated with the sample, a nonlinear optimization problem for MR information associated with the sample and the coefficients using: a forward model that uses the MR information as inputs and simulates response physics of the sample, the MR signals and the predetermined set of coil magnetic field basis vectors.

Claims (45)

1. A method for determining coefficients in a representation of coil sensitivities and

magnetic-resonance (MR) information associated with a sample, comprising:

by a computer system:

acquiring, from a measurement device or memory, MR signals associated with the sample;

accessing a predetermined set of coil magnetic field basis vectors associated with a surface surrounding the sample, wherein the coil sensitivities of coils in the measurement device are represented by weighted superpositions of the predetermined set of coil magnetic field basis vectors using the coefficients, and wherein the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations; and

solving, on a voxel-by-voxel basis for voxels associated with the sample, a nonlinear optimization problem for the MR information associated with the sample and the coefficients in the representation of coil sensitivities based at least in part on: a forward model that uses the MR information as inputs and simulates response physics of the sample to output computed MR signals corresponding to the MR signals, the coefficients and the predetermined set of coil magnetic field basis vectors, wherein the MR information comprises quantitative values of one or more MR parameters in the voxels;

wherein a given coil sensitivity for a given voxel is represented by a linear superposition of products of the coefficients and predetermined coil magnetic field basis vectors on the surface in the predetermined set of coil magnetic field basis vectors; and

wherein the MR parameters comprise one or more of: a nuclei density, a spin-lattice relaxation time along a direction of an external magnetic field, a spin-spin relaxation time perpendicular to the direction of the external magnetic field, an adjusted spin-spin relaxation time, components in a diffusion tensor, a velocity, a temperature, an off-resonance frequency, an electrical conductivity, a dielectric constant, a magnetic susceptibility, or a permittivity.

2. The method of claim 1 , wherein the nonlinear optimization problem comprises a term corresponding to a squared absolute value of a difference between the MR signals and the computed MR signals that are based at least in part on the MR information; and

wherein the term comprises a contribution from the coil sensitivities of the coils in the measurement device.

3. The method of claim 2 , wherein the nonlinear optimization problem comprises one or more constraints on a reduction or minimization of the term; and

wherein the one or more constraints comprise a regularizer corresponding to a spatial distribution of the MR information.

4. The method of claim 1 , wherein the MR information comprises an image with a spatial distribution of the one or more MR parameters in the voxels.

5. The method of claim 1 , wherein the MR signals correspond to magnetic resonance imaging (MRI) or another MR measurement technique.

6. The method of claim 1 , wherein the measurement device performs tensor field mapping or MR fingerprinting.

7. The method of claim 1 , wherein the nonlinear optimization problem is solved using a pretrained neural network or a pretrained machine-learning model that maps the MR signals and the predetermined set of coil magnetic field basis vectors to the MR information and the coefficients.

8. The method of claim 1 , wherein solving the nonlinear optimization problem reconstructs MR scan lines that were skipped during measurements performed by the measurement device.

9. The method of claim 1 , wherein an MR scan time of measurements performed by the measurement device is reduced relative to a magnetic resonance imaging (MRI) parallel imaging technique.

10. The method of claim 1 , wherein the surface comprises a closed surface.

11. A computer system, comprising:

an interface circuit configured to communicate with an electronic device;

memory configured to store program instructions; and

a processor configured to execute the program instructions, wherein, when executed by the processor, the program instructions cause the computer system to determine coefficients in a representation of coil sensitivities and magnetic-resonance 5 (MR) information associated with a sample by performing operations comprising:

acquiring, from a measurement device or memory, MR signals associated with the sample;

accessing a predetermined set of coil magnetic field basis vectors associated with a surface surrounding the sample, wherein the coil sensitivities of coils in the measurement device are represented by weighted superpositions of the predetermined set of coil magnetic field basis vectors using the coefficients, and wherein the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations; and

solving, on a voxel-by-voxel basis for voxels associated with the sample, a nonlinear optimization problem for MR information associated with the sample and the coefficients in the representation of coil sensitivities based at least in part on: a forward model that uses the MR information as inputs and simulates response physics of the sample to output computed MR signals corresponding to the MR signals, the coefficients and the predetermined set of coil magnetic field basis vectors, wherein the MR information comprises quantitative values of one or more MR parameters in the voxels;

wherein a given coil sensitivity for a given voxel is represented by a linear superposition of products of the coefficients and predetermined coil magnetic field basis vectors on the surface in the predetermined set of coil magnetic field basis vectors; and

wherein the MR parameters comprise one or more of: a nuclei density, a spin-lattice relaxation time along a direction of an external magnetic field, a spin-spin relaxation time perpendicular to the direction of the external magnetic field, an adjusted spin-spin relaxation time, components in a diffusion tensor, a velocity, a temperature, an off-resonance frequency, an electrical conductivity, a dielectric constant, a magnetic susceptibility, or a permittivity.

12. The computer system of claim 11 , wherein the nonlinear optimization problem comprises a term corresponding to a squared absolute value of a difference between the MR signals and the computed MR signals that are based at least in part on the MR information; and

wherein the term comprises a contribution from the coil sensitivities of the coils in the measurement device.

13. The computer system of claim 12 , wherein the nonlinear optimization problem comprises one or more constraints on a reduction or minimization of the term; and

wherein the one or more constraints comprise a regularizer corresponding to a spatial distribution of the MR information.

14. The computer system of claim 11 , wherein the MR signals correspond to magnetic resonance imaging (MRI) or another MR measurement technique.

15. The computer system of claim 11 , wherein the nonlinear optimization problem is solved using a pretrained neural network or a pretrained machine-learning model that maps the MR signals and the predetermined set of coil magnetic field basis vectors to the MR information and the coefficients.

16. The computer system of claim 11 , wherein the surface comprises a closed surface.

17. A non-transitory computer-readable storage medium for use in conjunction with a computer system, the computer-readable storage medium configured to store program instructions that, when executed by the computer system, cause the computer system to determine coefficients in a representation of coil sensitivities and magnetic-resonance (MR) information associated with a sample by performing operations comprising:

acquiring, from a measurement device or memory, MR signals associated with the sample;

accessing a predetermined set of coil magnetic field basis vectors associated with a surface surrounding the sample, wherein the coil sensitivities of coils in the measurement device are represented by weighted superpositions of the predetermined set of coil magnetic field basis vectors using the coefficients, and wherein the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations; and

solving, on a voxel-by-voxel basis for voxels associated with the sample, a nonlinear optimization problem for MR information associated with the sample and the coefficients in the representation of coil sensitivities based at least in part on: a forward model that uses the MR information as inputs and simulates response physics of the sample to output computed MR signals corresponding to the MR signals, the coefficients and the predetermined set of coil magnetic field basis vectors, wherein the MR information comprises quantitative values of one or more MR parameters in the voxels;

wherein a given coil sensitivity for a given voxel is represented by a linear superposition of products of the coefficients and predetermined coil magnetic field basis vectors on the surface in the predetermined set of coil magnetic field basis vectors; and

wherein the MR parameters comprise one or more of: a nuclei density, a spin-lattice relaxation time along a direction of an external magnetic field, a spin-spin relaxation time perpendicular to the direction of the external magnetic field, an adjusted spin-spin relaxation time, components in a diffusion tensor, a velocity, a temperature, an off-resonance frequency, an electrical conductivity, a dielectric constant, a magnetic susceptibility, or a permittivity.

18. The non-transitory computer-readable storage medium of claim 17 , wherein the nonlinear optimization problem comprises a term corresponding to a squared absolute value of a difference between the MR signals and the computed MR signals that are based at least in part on the MR information; and

wherein the term comprises a contribution from the coil sensitivities of the coils in the measurement device.

19. The non-transitory computer-readable storage medium of claim 17 , wherein the MR signals correspond to magnetic resonance imaging (MRI) or another MR measurement technique.

20. The non-transitory computer-readable storage medium of claim 17 , wherein the surface comprises a closed surface.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 11, 2023
From: FRANCAVILLA, MATTEO ALESSANDRO; FERNANDEZ VILLENA, JORGE; LEFKIMMIATIS, STAMATIOS; POLYMERIDIS, ATHANASIOS; TAYLI, DORUK
To: Q BIO, INC.
Reel/Frame 065833/0775 →
Continuity (4)
Continuation 17352868 · Jun 21, 2021
Continuation In Part 17032717 · Sep 25, 2020
Provisional Application 62907516 · Sep 27, 2019
Related Publication 20240369662A1 · Nov 7, 2024
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