JPWO2021062154A5

JPWO2021062154A5 -

Info

Publication number: JPWO2021062154A5
Application number: JP2022515106A
Authority: JP
Publication date: 2023-09-22

Claims

A method for determining coefficients of a coil sensitivity expression and MR information associated with a sample, the method comprising:
by computer,
obtaining a magnetic resonance (MR) signal associated with the sample from a measurement device or memory;
accessing a predetermined set of coil magnetic field basis vectors, wherein the coil sensitivity of a coil in the measuring device is represented by a weighted superposition of the predetermined set of coil magnetic field basis vectors using the coefficients; accessing a given coil magnetic field basis vector, which is a solution of Maxwell's equations;
a forward model that uses the MR information as input and simulates the response physics of the sample to output a calculated MR signal corresponding to a predetermined set of the MR signal , the coefficients, and the coil magnetic field basis vectors; solving a nonlinear optimization problem of the coefficients of the representation of the MR information and the coil sensitivity associated with the sample, based at least in part on
The method , wherein the MR information includes quantitative values of one or more MR parameters within voxels associated with the sample specified by the MR signal.

2. The method of claim 1, wherein a given coil sensitivity is represented by a linear superposition of the products of said coefficients and a predetermined coil field basis vector in a predetermined set of said coil field basis vectors.

the nonlinear optimization problem includes a term corresponding to the square of the absolute value of the difference between the MR signal and the estimated MR signal corresponding to the MR information;
2. The method of claim 1, wherein the term includes a contribution from the coil sensitivity of the coil within the measurement device.

the nonlinear optimization problem includes one or more constraints on reduction or minimization of the term;
2. The method of claim 1, wherein the one or more constraints include a regularizer corresponding to a spatial distribution of the MR information.

2. The method of claim 1, wherein the MR information comprises an image having a spatial distribution of one or more MR parameters within voxels associated with the sample as specified by the MR signal.

2. The method of claim 1, wherein the MR signal corresponds to magnetic resonance imaging (MRI) or another MR measurement technique.

The MR parameters include nuclear density, spin-lattice relaxation time along the direction of the external magnetic field, spin-spin relaxation time perpendicular to the direction of the external magnetic field, tuned spin-spin relaxation time, components of the diffusion tensor, velocity, 2. The method of claim 1, comprising one or more of temperature, non-resonant frequency, electrical conductivity, dielectric constant, magnetic susceptibility, or dielectric constant .

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

2. The method of claim 1, wherein the nonlinear optimization problem is iteratively solved until a convergence criterion is achieved.

The nonlinear optimization problem is solved using a pre-trained neural network or a pre-trained machine learning model that maps the MR signal and the set of coil field basis vectors to the MR information and the coefficients. 2. The method according to claim 1, wherein:

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

2. The method of claim 1, wherein the MR scanning time of measurements performed by the measurement device is reduced compared to magnetic resonance imaging (MRI) parallel imaging techniques.

A computer,
an interface circuit configured to communicate with the measurement device;
a memory configured to store program instructions;
a processor configured to execute the program instructions, the program instructions, when executed by the processor, causing the computer to:
obtaining a magnetic resonance (MR) signal associated with the sample from the measurement device or the memory;
accessing a predetermined set of coil magnetic field basis vectors, wherein the coil sensitivity of a coil in the measuring device is represented by a weighted superposition of the predetermined set of coil magnetic field basis vectors using the coefficients; accessing a given coil magnetic field basis vector, which is a solution of Maxwell's equations;
a forward model that uses the MR information as input and simulates the response physics of the sample to output a calculated MR signal corresponding to a predetermined set of the MR signal , the coefficients, and the coil magnetic field basis vectors; a processor for performing operations comprising: solving a nonlinear optimization problem of the coefficients of the representation of the MR information and the coil sensitivity associated with the sample based at least in part on ;
The MR information includes quantitative values of one or more MR parameters within voxels associated with the sample specified by the MR signal.

the nonlinear optimization problem includes a term corresponding to the square of the absolute value of the difference between the MR signal and the estimated MR signal corresponding to the MR information;
14. The computer of claim 13, wherein the term includes a contribution from the coil sensitivity of the coil within the measurement device.

14. The computer of claim 13, wherein the MR information includes an image having a spatial distribution of one or more MR parameters within voxels associated with the sample as specified by the MR signal.

14. The computer of claim 13, wherein the MR signal corresponds to magnetic resonance imaging (MRI) or another MR measurement technique.

The MR parameters include nuclear density, spin-lattice relaxation time along the direction of the external magnetic field, spin-spin relaxation time perpendicular to the direction of the external magnetic field, tuned spin-spin relaxation time, components of the diffusion tensor, velocity, 14. The computer of claim 13, comprising one or more of temperature, non-resonant frequency, electrical conductivity, dielectric constant, magnetic susceptibility, or dielectric constant .

14. The computer of claim 13, wherein the MR signal corresponds to a tensor field mapping or an MR fingerprint.

14. The computer of claim 13, wherein the nonlinear optimization problem is iteratively solved until a convergence criterion is achieved.

The nonlinear optimization problem is solved using a pre-trained neural network or a pre-trained machine learning model that maps the MR signal and the set of coil field basis vectors to the MR information and the coefficients. 14. The computer according to claim 13, wherein:

14. The computer of claim 13, wherein solving the nonlinear optimization problem reconstructs MR scan lines skipped during measurements performed by the measurement device.

14. The computer of claim 13, wherein the MR scan time of measurements performed by the measurement device is reduced compared to magnetic resonance imaging (MRI) parallel imaging techniques.

a non-transitory computer-readable storage medium for use in conjunction with a computer, the computer-readable storage medium configured to store program instructions, the program instructions, when executed by the computer; , to the computer,
obtaining a magnetic resonance (MR) signal associated with the sample from a measurement device or memory;
accessing a predetermined set of coil magnetic field basis vectors, wherein the coil sensitivity of a coil in the measuring device is represented by a weighted superposition of the predetermined set of coil magnetic field basis vectors using the coefficients; accessing a given coil magnetic field basis vector, which is a solution of Maxwell's equations;
a forward model that uses the MR information as input and simulates the response physics of the sample to output a calculated MR signal corresponding to a predetermined set of the MR signal , the coefficients, and the coil magnetic field basis vectors; solving a nonlinear optimization problem of the coefficients of the representation of the coil sensitivity and the MR information associated with the sample based at least in part on the MR information associated with the sample ;
A non-transitory computer-readable storage medium, wherein the MR information includes quantitative values of one or more MR parameters within voxels associated with the sample specified by the MR signal.

the nonlinear optimization problem includes a term corresponding to the square of the absolute value of the difference between the MR signal and the estimated MR signal corresponding to the MR information;
24. The non-transitory computer-readable storage medium of claim 23, wherein the term includes a contribution from the coil sensitivity of the coil within the measurement device.

24. The non-transitory computer readable storage of claim 23, wherein the MR information comprises an image having a spatial distribution of one or more MR parameters within voxels associated with the sample as specified by the MR signal. Medium.

24. The non-transitory computer-readable storage medium of claim 23, wherein the MR signal corresponds to magnetic resonance imaging (MRI) or another MR measurement technique.

The MR parameters include nuclear density, spin-lattice relaxation time along the direction of the external magnetic field, spin-spin relaxation time perpendicular to the direction of the external magnetic field, tuned spin-spin relaxation time, components of the diffusion tensor, velocity, 24. The non-transitory computer readable storage medium of claim 23, comprising one or more of temperature, non-resonant frequency, electrical conductivity, dielectric constant, magnetic susceptibility, or dielectric constant .

24. The non-transitory computer-readable storage medium of claim 23, wherein the MR signal corresponds to a tensor field mapping or an MR fingerprint.

24. The non-transitory computer-readable storage medium of claim 23, wherein the non-linear optimization problem is iteratively solved until a convergence criterion is achieved.

The nonlinear optimization problem is solved using a pre-trained neural network or a pre-trained machine learning model that maps the MR signal and the set of coil field basis vectors to the MR information and the coefficients. 24. The non-transitory computer-readable storage medium of claim 23, wherein the non-transitory computer-readable storage medium is

24. The non-transitory computer-readable storage medium of claim 23, wherein solving the non-linear optimization problem reconstructs MR scan lines skipped during measurements performed by the measurement device.

24. The non-transitory computer-readable storage medium of claim 23, wherein the MR scan time of measurements performed by the measurement device is reduced compared to magnetic resonance imaging (MRI) parallel imaging techniques.