Time compressed dynamic mr deep learning reconstruction

Patent metadata

Field	Value
Publication number	US-2025004085-A1
Application number	US-202318343777-A
Country	US
Kind code	A1
Filing date	Jun 29, 2023
Priority date	Jun 29, 2023
Publication date	Jan 2, 2025
Grant date	—

Abstract

Systems and methods for reconstruction for a medical imaging system. Non-Cartesian k-space data is acquired using a dynamic MR sequence. A time compression network compresses the non-Cartesian data. The compressed data is used for reconstruction of an image. The time compression network is configured to reduce the (time and memory) complexity of the reconstruction process.

First claim

1 . A method of reconstruction for a medical imaging system, the method comprising:
scanning a patient by the medical imaging system, the scanning acquiring k-space scan data using a dynamic MR sequence that includes at least a time component; compressing the k-space scan data using a time compression network configured to input kspace data and generate a time compression matrix of the k-space scan data as output; reconstructing an image from the time compression matrix using an unrolled iterative reconstruction that includes at least a data-consistency step; and outputting the image.

2 . The method of claim 1 , wherein the dynamic MR sequence comprises a GRASP (Golden-angle RAdial Sparse Parallel imaging) sequence.

3 . The method of claim 1 , further comprising:
applying an orthogonalization procedure at an end of the time compression network.

4 . The method of claim 3 , wherein the orthogonalization procedure comprises a Gram-Schmidt orthonormalization procedure, a Cayley transformation, or a Householder transformation.

5 . The method of claim 1 , further comprising:
applying a decompression matrix at an end of the unrolled iterative reconstruction.

6 . The method of claim 1 , wherein the time compression network is first trained offline using supervised learning and ground truth images to generate target compression matrices; wherein the unrolled iterative reconstruction is then trained end to end with the trained time compression network.

7 . The method of claim 1 , wherein an input of the time compression network is the k-space scan data comprising a three-dimensional matrix of size: readout size x number of coils x number of time points, or as a two-dimensional matrix of size: number of coils x number of time points.

8 . The method of claim 1 , wherein an output of the time compression network is a matrix of size: number of compressed time components x number of time points, wherein the number of compressed time components is predefined by an operator.

9 . The method of claim 8 , wherein the number of compressed time components is between five and ten and wherein the number of time points is greater than one hundred.

10 . The method of claim 1 , wherein the time compression network comprises multiple fully connected layers with nonlinear activation functions of transformer encoder layers.

11 . The method of claim 1 , wherein the time compression network is trained with sequence of variable time points.

12 . A system for time compressed dynamic magnetic resonance deep learning reconstruction, the system comprising:
a medical imaging system configured to acquire k-space scan data using a non-Cartesian dynamic sequence; a time compression network configured to compress the k-space scan data; and a reconstruction network configured to reconstruct an image from the compressed k-space scan data.

13 . The system of claim 12 , further comprising:
a display configured to display the image.

14 . The system of claim 12 , wherein the non-Cartesian dynamic sequence comprises a GRASP (Golden-angle RAdial Sparse Parallel imaging) sequence.

15 . The system of claim 12 , wherein the time compression network is first trained offline using supervised learning and ground truth images to generate target compression matrices; wherein the reconstruction network is then trained end to end with the trained time compression network.

16 . The system of claim 12 , wherein an input of the time compression network is the k-space scan data comprising a three-dimensional matrix of size: readout size x number of coils x number of time points, or as a two-dimensional matrix of size: number of coils x number of time points and wherein an output of the time compression network is a matrix of size: number of compressed time components x number of time points, wherein the number of compressed time components is predefined by an operator.

17 . The system of claim 16 , wherein the number of compressed time components is between five and ten and wherein the number of time points is greater than one hundred.

18 . A non-transitory computer readable storage medium comprising a set of computer-readable instructions stored thereon which, when executed by at least one processor cause the processor to:
acquire non-Cartesian k-space scan data that includes at least a time component; compress the non-Cartesian k-space scan data using a time compression network configured to input non-Cartesian kspace data and generate a time compression matrix of the non-Cartesian k-space scan data as output; reconstruct an image from the time compression matrix using an unrolled iterative reconstruction that includes at least a data-consistency step; and output the image.

19 . The non-transitory computer readable storage medium of claim 18 , wherein the non-Cartesian k-space scan is acquired using a GRASP (Golden-angle RAdial Sparse Parallel imaging) sequence.

20 . The non-transitory computer readable storage medium of claim 18 , wherein an input of the time compression network is the non-Cartesian k-space scan data comprising a two-dimensional matrix of size: number of coils x number of time points and wherein the time compression matrix is a matrix of size: number of compressed time components x number of time points, wherein the number of compressed time components is predefined by an operator.

Assignees

• Siemens Healthineers Ag

Inventors

• Arberet Simon
• Mostapha Mahmoud
• Nickel Marcel Dominik
• Nadar Mariappan S

Classifications

• G01R33/5611 Primary

  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

• 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

• G06T7/97

  Determining parameters from multiple pictures (depth or shape recovery from multiple images G06T7/55; stereo camera calibration G06T7/85) · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/10088

  Magnetic resonance imaging [MRI] · CPC title