Accelerated time domain magnetic resonance spin tomography

Patent metadata

Field	Value
Publication number	US-2023044166-A1
Application number	US-202117791527-A
Country	US
Kind code	A1
Filing date	Jan 8, 2021
Priority date	Jan 8, 2020
Publication date	Feb 9, 2023
Grant date	—

Abstract

The present patent disclosure relates to a method and a device 700 for determining a spatial distribution of at least one tissue parameter within a sample on a time domain magnetic resonance, TDMR, signal emitted from the sample after excitation of the sample according to an applied pulse sequence, a method of obtaining at least one time dependent parameter relating to a magnetic resonance, MR, signal emitted from a sample after excitation of the sample according to an applied spin echo pulse sequence, and a computer program product for performing the methods. A TDMR signal model is used to approximate the emitted time domain magnetic resonance signal. The model is factorized into one or more first matrix operators that have a non-linear dependence on the at least one tissue parameter and a remainder of the TDMR signal model.

First claim

What is claimed is:

1 . A method for determining a spatial distribution of at least one tissue parameter within a sample based on a measured time domain magnetic resonance, TDMR, signal emitted from the sample after excitation of the sample according to an applied pulse sequence, the method comprising:
i) determining a TDMR signal model to approximate the emitted time domain magnetic resonance signal, wherein the TDMR signal model is dependent on TDMR signal model parameters comprising the at least one tissue parameter within the sample, wherein the model is factorized into one or more first matrix operators that have a non-linear dependence on the at least one tissue parameter and a remainder of the TDMR signal model; ii) performing optimization with an objective function and constraints based on the first matrix operators and the remainder of the TDMR signal model until a difference between the TDMR signal model and the TDMR signal emitted from the sample is below a predefined threshold or until a predetermined number of repetitions is completed, in order to obtain an optimized or final set of TDMR signal model parameters; and iii) obtaining from the optimized or final set of TDMR signal model parameters the spatial distribution of the at least one tissue parameter.

2 . The method according to claim 1 , wherein one of the one or more first matrix operators represents the TDMR signal at echo time.

3 . The method according to claim 2 , wherein the model is factorized into at least two first matrix operators that have a non-linear dependence on the at least one tissue parameter and the remainder of the TDMR signal model, wherein a first of the at least two first matrix operators represents the TDMR signal at echo time, and wherein a second of the at least two first matrix operators represents a readout encoding matrix operator of the TDMR signal.

4 . The method according to claim 1 , wherein the remainder of the TDMR signal model comprises a readout encoding matrix operator of the TDMR signal.

5 . The method according to claim 1 , wherein the performing the optimization comprises using a surrogate predictive model wherein a TDMR signal is computed at echo time only based on the one or more first matrix operators, wherein the surrogate predictive model outputs the TDMR signal at echo time and one or more TDMR signal derivatives at echo time with respect to each of the at least one tissue parameter within the sample.

6 . The method according to claim 1 , wherein the TDMR signal model is a volumetric signal model and comprises a plurality of voxels, wherein preferably the step of performing optimization is done iteratively for each line in a phase encoding direction of the voxels of the TDMR signal model.

7 . The method according to claim 6 , wherein the TDMR signal at echo time is a compressed TDMR signal at echo time for each line of voxels, wherein the TDMR signal at echo time is compressed for each voxel, and/or
wherein the remainder of the TDMR signal model is factorized into a diagonal phase encoding matrix, preferably for each of the lines of voxels, and a compression matrix for the TDMR signal at echo time.

8 . The method according to claim 7 , wherein the optimization with an objective function and constraints is representable by:

min
α

1
2

⁢



D
-

∑

i
=
1

N
y

C
i
p

⁢

UY
⁡
(

α
i

)

⁢

C
r

(

α
i

)



F
2

(

Eq
.

1

)

wherein;
α i denotes the at least one tissue parameter for the ith line of voxels in the phase encoding direction;
C i p ∈ N Tr ×N Tr is the diagonal phase encoding matrix for the ith line of voxels in the phase encoding direction;
U∈ N Tr ×N Eig is the compression matrix for the TDMR signal at echo time, N Tr being a number of RF pulses and N Eig being a length of the compressed TDMR signal at echo time;
Y(α i )∈ N Eig ×N x is the compressed echo time TDMR signal for the ith line in the phase encoding direction of voxels, wherein each column of Y(α i ) is the compressed TDMR signal for one voxel in the ith line;
C r (α i )∈ N Tr ×N Read is the readout encoding matrix for the ith line in the phase encoding direction of voxels;
D∈ N x ×N Read is the TDMR signal emitted from the sample in a matrix format, N Read being a number of readout points every TR;
N y represents the number of voxels or rows of voxels in the phase encoding direction.

9 . The method according to claim 1 , wherein the optimization with an objective function and constraints is representable by:

min

α
,
Z
,
W

ℒ
λ

(

α
,
Z
,
W

)

Assignees

• Umc Utrecht Holding Bv

Inventors

• Liu Hongyan
• Sbrizzi Alessandro
• Van Den Berg Cornelis Antonius Theodorus

Classifications

• G01R33/448

  Relaxometry, i.e. quantification of relaxation times or spin density (G01R33/50 takes precedence) · CPC title

• G01R33/5608 Primary

  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) · CPC title

• G01R33/561

  by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences · CPC title

• G01R33/50 Primary

  based on the determination of relaxation times {, e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences} · CPC title