# US 11,308,661 B2 · Motion robust reconstruction of multi-shot diffusion-weighted images without phase estimation via locally low-rank regularization

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- Number: US 11,308,661 B2
- Document ID: US11308661B2
- Status: Granted
- Kind: B2
- Application number: 16/416,700
- Filed: 2019-05-20
- Published: 2019-11-21
- Granted: 2022-04-19
- Earliest claimed: 2018-05-21
- Assignees: The Board of Trustees of the Leland Stanford Junior University
- Applicants: The Board of Trustees of the Leland Stanford Junior University
- Inventors: Bruce L. Daniel; Brian A. Hargreaves; Yuxin Hu; Evan G. Levine
- Web page: https://patentgrep.com/patent/US11308661B2

## CPC

- G06T 12/20: Inverse problem, i.e. transformations from projection space into object space
- G01R 33/443: {Assessment of an electric or a magnetic field, e.g. spatial mapping, determination of a B0 drift or dosimetry}
- G01R 33/5602: {by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse}
- 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 33/5611: {Parallel magnetic resonance imaging, e.g. sensitivity encoding [SENSE], simultaneous acquisition of spatial harmonics [SMASH], unaliasing by Fourier encoding of the overlaps using the temporal dimension [UNFOLD], k-t-broad-use linear acquisition speed-up technique [k-t-BLAST], k-t-SENSE (structural details of arrays of sub-coils G01R33/3415)}
- G01R 33/5616: {using gradient refocusing, e.g. EPI}
- G01R 33/56341: {Diffusion imaging}
- G01R 33/56509: {due to motion, displacement or flow, e.g. gradient moment nulling (G01R33/567 takes precedence)}
- G06T 7/262: using transform domain methods, e.g. Fourier domain methods
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/20216: Image averaging
- G06T 2210/41: Medical
- G06T 2211/424: Iterative
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)

## Abstract

Multi-shot diffusion-weighted magnetic resonance imaging acquires multiple k-space segments of diffusion-weighted MRI data, estimates reconstructed multi-shot diffusion weighted images, and combines the estimated images to obtain a final reconstructed MRI image. The estimation of images iteratively calculates updated multi-shot images from the multiple k-space segments and current multi-shot images using a convex model without estimating motion-induced phase, constructs multiple locally low-rank spatial-shot matrices from the updated multi-shot images, and calculates current multi-shot images from spatial-shot matrices.

## Claims

### Claim 1

A method for multi-shot diffusion-weighted magnetic resonance imaging, the method comprising:
a) acquiring with a magnetic resonance imaging system multiple k-space segments of diffusion-weighted MRI data;
b) estimating reconstructed multi-shot diffusion weighted images by iteratively performing steps comprising:
i) calculating updated multi-shot images from the multiple k-space segments and current multi-shot images using a convex model without estimating motion-induced phase;
ii) constructing multiple locally low-rank spatial-shot matrices from the updated multi-shot images;
iii) calculating the current multi-shot images from the spatial-shot matrices;
c) combining the reconstructed multi-shot diffusion weighted images to obtain a reconstructed MRI image.

### Claim 2 (depends on 1)

The method of claim 1 wherein the diffusion-weighted MRI data is acquired in response to an echo planar imaging sequence after applying diffusion encoding gradients.

### Claim 3 (depends on 2)

The method of claim 2 wherein the echo planar imaging sequence is a 2D single-refocused Stejskal-Tanner spin-echo planar imaging sequence.

### Claim 4 (depends on 1)

The method of claim 1 wherein constructing the spatial-shot matrices from the updated multi-shot image comprises calculating a low-rank approximation by doing singular value decomposition (SVD) and soft-thresholding.

### Claim 5 (depends on 1)

The method of claim 1 where each of the spatial-shot matrices corresponds to a spatial block in an imaging plane, where each column of the matrix corresponds to a different shot of the multiple k-space segments.

### Claim 6 (depends on 1)

The method of claim 1 where calculating updated multi-shot images uses a sampling operator, a Fourier transform, and an encoding operator constructed from sensitivity maps, and their conjugate operators, and the acquired data.

### Claim 7 (depends on 6)

The method of claim 6 where the sensitivity maps are estimated from T2 images acquired without applying diffusion encoding gradients.

### Claim 8 (depends on 1)

The method of claim 1 further comprising flipping and conjugating the multiple k-space segments of diffusion-weighted MRI data and treating them as virtual shots.

### Claim 9 (depends on 8)

The method of claim 8 further comprising estimating sensitivity maps for the virtual shots from conjugated T2 images.