System and method for scan time reduction for propeller magnetic resonance imaging acquisition using deep learning reconstruction

Patent metadata

Field	Value
Publication number	US-2025157098-A1
Application number	US-202318506457-A
Country	US
Kind code	A1
Filing date	Nov 10, 2023
Priority date	Nov 10, 2023
Publication date	May 15, 2025
Grant date	—

Abstract

A system and method for reducing scan time of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging include acquiring a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data includes a plurality of parallel phase encoding lines sampled in a phase encoding order. Each blade of the plurality of blades of k-space data is undersampled. The system and method include utilizing a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades. The system and method include utilizing a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.

First claim

1 . A computer-implemented method for reducing scan time of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging, comprising:
acquiring, via a processor, in an accelerated manner a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data comprises a plurality of parallel phase encoding lines sampled in a phase encoding order, and wherein each blade of the plurality of blades of k-space data is undersampled; utilizing, via the processor, a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades; and utilizing, via the processor, a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.

2 . The computer-implemented method of claim 1 , wherein the deep learning-based Cartesian-like reconstruction network is configured to perform with an arbitrary number of blades of k-space data.

3 . The computer-implemented method of claim 1 , wherein the plurality of blades of k-space data is acquired from a single receiver coil.

4 . The computer-implemented method of claim 1 , wherein the plurality of blades of k-space data is acquired from a plurality of receiver coils.

5 . The computer-implemented method of claim 1 , wherein the deep learning-based Cartesian-like reconstruction network comprises an unrolled algorithm-based deep learning-based network.

6 . The computer-implemented method of claim 5 , further comprising training, via the processor, the deep learning-based Cartesian-like reconstruction network on input-output data pairs utilizing supervised learning, wherein the input-output data pairs comprise undersampled k-space blade images and corresponding fully sampled k-space images acquired utilizing the PROPELLER sequence, and the undersampled k-space blade images were generated from the corresponding fully sampled k-space images.

7 . The computer-implemented method of claim 1 , wherein the plurality of blades of k-space data comprises skewed aspect ratios.

8 . A system for reducing scan time of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging, comprising:
a memory encoding processor-executable routines; and a processor configured to access the memory and to execute the processor-executable routines, wherein the processor-executable routines, when executed by the processor, cause the processor to:
acquire, in accelerated manner, a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data comprises a plurality of parallel phase encoding lines sampled in a phase encoding order, and wherein each blade of the plurality of blades of k-space data is undersampled;
utilize a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades; and
utilize a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.

9 . The system of claim 8 , wherein the deep learning-based Cartesian-like reconstruction network is configured to perform with an arbitrary number of blades of k-space data.

10 . The system of claim 8 , wherein the plurality of blades of k-space data is acquired from a single receiver coil.

11 . The system of claim 8 , wherein the plurality of blades of k-space data is acquired from a plurality of receiver coils.

12 . The system of claim 8 , wherein the deep learning-based Cartesian-like reconstruction network comprises an unrolled algorithm-based deep learning-based network.

13 . The system of claim 12 , wherein the processor-executable routines, when executed by the processor, further cause the processor to train the deep learning-based Cartesian-like reconstruction network on input-output data pairs utilizing supervised learning, wherein the input-output data pairs comprise undersampled k-space blade images and corresponding fully sampled k-space images acquired utilizing the PROPELLER sequence, and the undersampled k-space blade images were generated from the corresponding fully sampled k-space images.

14 . The system of claim 8 , wherein the plurality of blades of k-space data comprises skewed aspect ratios.

15 . A non-transitory computer-readable medium, the non-transitory computer-readable medium comprising processor-executable code that when executed by a processor, causes the processor to:
acquire, in an accelerated manner, a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner during a periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) sequence, wherein each blade of the plurality of blades of k-space data comprises a plurality of parallel phase encoding lines sampled in a phase encoding order, and wherein each blade of the plurality of blades of k-space data is undersampled; utilize a deep learning-based Cartesian-like reconstruction network to individually and separately reconstruct each blade of the plurality of blades of k-space data to generate a plurality of fully sampled blades; and utilize a PROPELLER reconstruction algorithm to generate a complex image from the plurality of fully sampled blades.

16 . The non-transitory computer-readable medium of claim 15 , wherein the deep learning-based Cartesian-like reconstruction network is configured to perform with an arbitrary number of blades of k-space data.

17 . The non-transitory computer-readable medium of claim 15 , wherein the plurality of blades of k-space data is acquired from a single receiver coil.

18 . The non-transitory computer-readable medium of claim 15 , wherein the plurality of blades of k-space data is acquired from a plurality of receiver coils.

19 . The non-transitory computer-readable medium of claim 15 , wherein the deep learning-based Cartesian-like reconstruction network comprises an unrolled algorithm-based deep learning-based network.

20 . The non-transitory computer-readable medium of claim 15 , wherein the processor-executable code, when executed by the processor, further causes the processor to train the deep learning-based Cartesian-like reconstruction network on input-output data pairs utilizing supervised learning, wherein the input-output data pairs comprise undersampled k-space blade images and corresponding fully sampled k-space images acquired utilizing the PROPELLER sequence, and the undersampled k-space blade images were generated from the corresponding fully sampled k-space images.

Assignees

• Ge Prec Healthcare Llc

Inventors

• C Florintina
• Chatterjee Sudhanya
• Patil Rohan
• Joel Suresh Emmanuel Devadoss

Classifications

• G06T12/20 Primary

  Inverse problem, i.e. transformations from projection space into object space · CPC title

• G01R33/5608

  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) · CPC title

• G01R33/4824

  using a non-Cartesian trajectory · CPC title

• G01R33/56509

  due to motion, displacement or flow, e.g. gradient moment nulling (G01R33/567 takes precedence) · CPC title

• G01R33/5611

  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) · CPC title