Deep learning techniques for magnetic resonance image reconstruction

Patent metadata

Field	Value
Publication number	US-11300645-B2
Application number	US-201916524638-A
Country	US
Kind code	B2
Filing date	Jul 29, 2019
Priority date	Jul 30, 2018
Publication date	Apr 12, 2022
Grant date	Apr 12, 2022

Abstract

A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.

First claim

What is claimed is:

1. A method, comprising:
generating a magnetic resonance (MR) image from input MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform processing using a non-uniform Fourier transformation for transforming image domain data to spatial frequency domain data; and
applying the first neural network block to image domain data, wherein the applying comprises:
applying, to the image domain data, the non-uniform Fourier transformation followed by an adjoint non-uniform Fourier transformation to obtain first output;
applying the adjoint non-uniform Fourier transformation to the input MR spatial frequency data to obtain second output; and
providing the image domain data, the first output, and the second output as inputs to a plurality of convolutional layers.

2. The method of claim 1 , wherein each of the one or more neural network blocks is configured to perform processing using the non-uniform Fourier transformation.

3. The method of claim 1 , further comprising:
obtaining the input MR spatial frequency data;
generating an initial image from the input MR spatial frequency data using the non-uniform Fourier transformation; and
applying the neural network model to the initial image at least in part by using the first neural network block to perform the processing using the non-uniform Fourier transformation.

4. The method of claim 1 , wherein the first neural network block is configured to perform processing using the non-uniform Fourier transformation at least in part by performing the non-uniform Fourier transformation on data by applying a gridding interpolation transformation, a Fourier transformation, and a de-apodization transformation to the data.

5. The method of claim 4 , wherein applying the gridding interpolation transformation to the data is performed using sparse graphical processing unit (GPU) matrix multiplication.

6. The method of claim 1 , wherein the first neural network block comprises:
the plurality of convolutional layers.

7. The method of claim 6 , wherein the plurality of convolutional layers include one or more convolutional layers and one or more transposed convolutional layers.

8. The method of claim 6 , wherein the plurality of convolutional layers have a U-net structure.

9. The method of claim 1 , wherein the plurality of convolutional layers is configured to generate the MR image using the image domain data, the first output, and the second output.

10. The method of claim 1 , further comprising:
applying a convolutional neural network to a result of applying the non-uniform Fourier transformation to the image domain data to obtain an intermediate output; and
applying the adjoint non-uniform Fourier transformation to the intermediate output to obtain the first output.

11. The method of claim 1 , wherein points in the input MR spatial frequency data were obtained using a non-Cartesian sampling trajectory.

12. The method of claim 11 , wherein the non-uniform Fourier transformation is determined at least in part by using the non-Cartesian sampling trajectory.

13. At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method comprising:
generating a magnetic resonance (MR) image from input MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform processing using a non-uniform Fourier transformation for transforming image domain data to spatial frequency domain data; and
applying the first neural network block to image domain data, wherein the applying comprises:
applying, to the image domain data, the non-uniform Fourier transformation followed by an adjoint non-uniform Fourier transformation to obtain first output;
applying the adjoint non-uniform Fourier transformation to the input MR spatial frequency data to obtain second output; and
providing the image domain data, the first output, and the second output as inputs to a plurality of convolutional layers.

14. The at least one non-transitory computer-readable storage medium of claim 13 , wherein the method further comprises:
obtaining the input MR spatial frequency data;
generating an initial image from the input MR spatial frequency data using the non-uniform Fourier transformation; and
applying the neural network model to the initial image at least in part by using the first neural network block to perform the processing using the non-uniform Fourier transformation.

15. The at least one non-transitory computer-readable storage medium of 13 , wherein applying the first neural network block further comprises:
performing processing using the non-uniform Fourier transformation at least in part by performing the non-uniform Fourier transformation on data by applying a gridding interpolation transformation, a Fourier transformation, and a de-apodization transformation to the data.

16. The at least one non-transitory computer-readable storage medium of 13 , wherein the method further comprises:
applying a convolutional neural network to a result of applying the non-uniform Fourier transformation to the image domain data to obtain an intermediate output; and
applying the adjoint non-uniform Fourier transformation to the intermediate output to obtain the first output.

17. A magnetic resonance imaging (MRI) system, comprising:
a magnetics system comprising:
a B 0 magnet configured to provide a B 0 field for the MRI system;
gradient coils configured to provide gradient fields for the MRI system;
at least one RF coil configured to detect magnetic resonance (MR) signals; and
a controller configured to:
control the magnetics system to acquire MR spatial frequency data using a non-Cartesian sampling trajectory;
generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform processing using a non-uniform Fourier transformation; and
apply the first neural network block to image domain data, wherein the applying comprises:
applying, to the image domain data, the non-uniform Fourier transformation followed by an adjoint non-uniform Fourier transformation to obtain first output;
applying the adjoint non-uniform Fourier transformation to the input MR spatial frequency data to obtain second output; and
providing the image domain data, the first output, and the second output as inputs to a plurality of convolutional layers.

18. The MRI system of claim 17 , wherein the B 0 magnet is a permanent magnet.

19. The MRI system of claim 17 , wherein the controller is further configured to:
obtain the input MR spatial frequency data;
generate an initial image from the input MR spatial frequency data using the non-uniform Fourier transformation; and
apply the neural network model to the initial image at least in part by using the first neural network block to perform the processing using the non-uniform Fourier transformation.

20. The MRI system of claim 17 , wherein the first neural network block is configured to perform processing using the non-uniform Fourier

Assignees

• Hyperfine Operations Inc

Inventors

• Schlemper Jo
• Moshen Salehi Seyed Sadegh
• Sofka Michal
• Kundu Prantik
• Wang Ziyi
• Lazarus Carole
• Dyvorne Hadrien A
• Sacolick Laura
• O'Halloran Rafael
• Rothberg Jonathan M

Classifications

• G06V10/768

  using context analysis, e.g. recognition aided by known co-occurring patterns · CPC title

• G06V10/454

  Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN] · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06T12/20

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

• G06N3/045

  Combinations of networks · CPC title