Systems and methods for determining field map

Patent metadata

Field	Value
Publication number	US-11067654-B2
Application number	US-202016750012-A
Country	US
Kind code	B2
Filing date	Jan 23, 2020
Priority date	Apr 29, 2019
Publication date	Jul 20, 2021
Grant date	Jul 20, 2021

Abstract

The present disclosure is related to systems and methods for determining a field map in magnetic resonance imaging (MRI). The method includes obtaining at least three images. Each may be acquired at one of at least three echo times by an MRI device via scanning a subject. The at least three echo times may define multiple pairs of adjacent echo times. Each of the multiple pairs of adjacent echo times may have a time difference between the adjacent echo times. At least two of the time differences may be different. The method includes determining a target function with an off-resonance frequency as an independent variable. The target function includes a phase deviation term and a sparsity constraint term.

First claim

We claim:

1. A system for determining a field map in magnetic resonance imaging (MRI), comprising:
at least one storage device storing a set of instructions; and
at least one processor in communication with the at least one storage device, when executing the stored set of instructions, the at least one processor causes the system to perform operations including:
obtaining at least three images, each being acquired at one of at least three echo times by an MRI device via scanning a subject, wherein the at least three echo times define multiple pairs of adjacent echo times, each of the multiple pairs of adjacent echo times has a time difference between the adjacent echo times, and at least two of the time differences are different; and
determining a target function with an off-resonance frequency as an independent variable,
wherein the target function includes a phase deviation term and a sparsity constraint term, the phase deviation term is constructed based on multiple phase deviations, each phase deviation corresponds to two images acquired at each pair of the multiple pairs of the adjacent echo times, and the sparsity constraint term is constructed based on at least one sparsity parameter of the off-resonance frequency in at least one transform domain.

2. The system of claim 1 , the at least one processor causes the system to perform further operations including:
determining a field map by determining a target off-resonance frequency based on the target function.

3. The system of claim 2 , wherein to determine a target off-resonance frequency based on the target function, the at least one processor causes the system to perform the operations including:
determining, based on an initial off-resonance frequency, a minimum value of the target function; and
determining an off-resonance frequency corresponding to the minimum value of the target function as the target off-resonance frequency.

4. The system of claim 3 , the at least one processor causes the system to perform further operations including:
obtaining at least three initial images, each being acquired at one of at least three initial echo times, wherein the at least three initial echo times define multiple pairs of adjacent initial echo times, each pair of the multiple pairs of adjacent initial echo time has a time difference between the adjacent initial echo times; and
determining the initial off-resonance frequency based on a phase difference between two initial images acquired at the each pair of the multiple pairs of adjacent initial echo times and the time difference corresponding to the each pair of the multiple pairs of adjacent initial echo times.

5. The system of claim 1 , wherein to determine a target function with an off-resonance frequency as an independent variable, the at least one processor causes the system to perform operations including:
determining the phase deviation corresponding to two images acquired at each pair of adjacent echo times based on the off-resonance frequency, the two images acquired at the each pair of adjacent echo times, and the time difference corresponding to the each pair of adjacent echo times;
determining the phase deviation term base on the phase deviation;
determining the at least one sparsity parameter of the off-resonance frequency in the at least one transform domain;
determining the sparsity constraint term based on the at least one sparsity parameter; and
determining the target function with the off-resonance frequency as the independent variable based on the phase deviation term and the sparsity constraint term.

6. The system of claim 5 , wherein to determine a phase deviation corresponding to two images acquired at each pair of adjacent echo times based on the off-resonance frequency, the two image acquired at the each pair of adjacent echo times, and the time difference corresponding to the each pair of adjacent echo times, the at least one processor causes the system to perform operations including:
determining an estimated phase difference with an off-resonance frequency as an independent variable based on the time difference corresponding to the each pair of adjacent echo times;
determining an actual phase difference based on values of corresponding pixels in the two images acquired at the each pair of adjacent echo times; and
determining the phase deviation corresponding to the each pair of adjacent echo times based on the estimated phase difference and the actual phase difference.

7. The system of claim 6 , wherein to determine the phase deviation corresponding to the each pair of adjacent echo times based on the estimated phase difference and the actual phase difference, the at least one processor causes the system to perform operations including:
determining a distance between the estimated phase difference and the actual phase difference; and
determining a Euclidean norm of the distance as the phase deviation.

8. The system of claim 5 , wherein to determine the phase deviation term base on the phase deviation corresponding to the each pair of adjacent echo times, the at least one processor causes the system to perform operations including:
determining a weight corresponding to the each pair of adjacent echo times based on the two images acquired at the each pair of adjacent echo times; and
determining the phase deviation term by performing a weighted summation operation on the phase deviation corresponding to the each pair of adjacent echo times based on the weight corresponding to the each pair of adjacent echo times.

9. The system of claim 5 , wherein to determine the at least one sparsity parameter of the off-resonance frequency in the at least one transform domain, the at least one processor causes the system to perform operations including:
processing the off-resonance frequency according to at least one of a variational method, a wavelet transform, a discrete Fourier transform, a discrete cosine transform, or a finite difference transform, to generate at least one processing result; and
determining the at least one sparsity parameter of the off-resonance frequency in the at least one transform domain based on the at least one processing result.

10. The system of claim 9 , wherein to determine the at least one sparsity parameter of the off-resonance frequency in the at least one transform domain based on the at least one processing result, the at least one processor causes the system to perform operations including:
determining the at least one sparsity parameter of the off-resonance frequency in the at least one transform domain based on a sum of absolute values of multiple elements in the at least one processing result.

11. The system of claim 5 , wherein to determine the sparsity constraint term based on the at least one sparsity parameter of the off-resonance frequency in at least one transform domain, the at least one processor causes the system to perform operations including:
determining the sparsity constraint term by performing a weighted summation operation on the at least one sparsity parameter of the off-resonance frequency in the at least one transform domain.

12. The system of claim 5 , wherein to determine the target function with the off-resonance frequency as the independent variable based on the phase deviation term and the sparsity constraint term, the at least one processor causes the system to perform operations including:
determining the target function by performing a weighted summation operation on the phase deviation term and the sparsity constraint term.

13. The system of claim 1 , wherein to obtain at least three images, the at least one processor causes the system to perf

Assignees

• Shanghai United Imaging Healthcare Co Ltd

Inventors

• Zheng Yuan
• Ye Yongquan
• Ding Yu

Classifications

• G01R33/5602

  by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse · CPC title

• G01R33/243 Primary

  Spatial mapping of the polarizing magnetic field · 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

• G01R33/56536

  due to magnetic susceptibility variations · CPC title