Multi-coil magnetic resonance imaging using deep learning

Patent metadata

Field	Value
Publication number	US-11324418-B2
Application number	US-202016817402-A
Country	US
Kind code	B2
Filing date	Mar 12, 2020
Priority date	Mar 14, 2019
Publication date	May 10, 2022
Grant date	May 10, 2022

Abstract

Techniques for generating magnetic resonance (MR) images from MR data obtained by a magnetic resonance imaging (MRI) system comprising a plurality of RF coils configured to detect RF signals. The techniques include: obtaining a plurality of input MR datasets obtained by the MRI system to image a subject, each of the plurality of input MR datasets comprising spatial frequency data and obtained using a respective RF coil in the plurality of RF coils; generating a respective plurality of MR images from the plurality of input MR datasets by using an MR image reconstruction technique; estimating, using a neural network model, a plurality of RF coil profiles corresponding to the plurality of RF coils; generating an MR image of the subject using the plurality of MR images and the plurality of RF coil profiles; and outputting the generated MR image.

First claim

What is claimed is:

1. A method for generating magnetic resonance (MR) images from MR data obtained by a magnetic resonance imaging (MRI) system comprising a plurality of RF coils configured to detect RF signals, the method comprising:
obtaining a plurality of input MR datasets obtained by the MRI system to image a subject, each of the plurality of input MR datasets comprising spatial frequency data and obtained using a respective RF coil in the plurality of RF coils, wherein the MRI system comprises at least 8 RF coils and the plurality of input MR datasets comprises at least 8 input MR datasets;
generating a respective plurality of MR images from the plurality of input MR datasets by using an MR image reconstruction technique;
estimating, using a neural network model, a plurality of RF coil profiles corresponding to the plurality of RF coils;
generating an MR image of the subject using the plurality of MR images and the plurality of RF coil profiles; and
outputting the generated MR image.

2. The method of claim 1 , further comprising using the MRI system to image the subject to obtain the plurality of input MR datasets.

3. The method of claim 1 , wherein generating the respective plurality of MR images from the plurality of input MR datasets is performed using a neural network MR image reconstruction technique.

4. The method of claim 1 , wherein generating the respective plurality of MR images from the plurality of input MR datasets is performed using a compressed sensing MR image reconstruction technique.

5. The method of claim 1 , wherein the neural network model comprises one or more convolutional layers.

6. The method of claim 1 , wherein generating the MR image of the subject using the plurality of MR images and the plurality of RF coil profiles comprises:
generating the MR image of the subject as a weighted combination of the plurality of MR images, each of the plurality of MR images being weighted by a respective RF coil profile in the plurality of RF coil profiles.

7. The method of claim 1 ,
wherein the plurality of MR images comprises a first MR image generated from a first input MR dataset obtained using a first RF coil of the plurality of RF coils, and
wherein generating the MR image of the subject comprises weighting different pixels of the first MR image using different values of a first RF coil profile among the plurality of RF coil profiles, the first RF coil profile being associated with the first RF coil.

8. A magnetic resonance imaging (MRI) system, comprising:
a magnetics system having a plurality of magnetics components to produce magnetic fields for performing MRI, the magnetics system comprising:
a plurality of RF coils configured to detect MR signals; and
at least one permanent B 0 magnet configured to produce a B 0 field for an imaging region of the MRI system, the B 0 field having a strength between 50 milliTesla and 100 milliTesla; and

at least one processor configured to perform:
obtaining a plurality of input MR datasets obtained by the MRI system to image a subject, each of the plurality of input MR datasets comprising spatial frequency data and obtained using a respective RF coil in the plurality of RF coils;
generating a respective plurality of MR images from the plurality of input MR datasets by using an MR image reconstruction technique;
estimating, using a neural network model, a plurality of RF coil profiles corresponding to the plurality of RF coils;
generating an MR image of the subject using the plurality of MR images and the plurality of RF coil profiles; and
outputting the generated MR image.

9. The MRI system of claim 8 , wherein the at least one permanent B 0 magnet comprises a plurality of concentric permanent magnet rings.

10. The MRI system of claim 8 , wherein the plurality of magnetics components include at least one gradient coil.

11. At least one non-transitory computer readable storage medium storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system having a plurality of RF coils configured to detect MR signals, the method comprising:
obtaining a plurality of input MR datasets obtained by the MRI system to image a subject, each of the plurality of input MR datasets comprising spatial frequency data and obtained using a respective RF coil in the plurality of RF coils, wherein the MRI system comprises at least 8 RF coils and the plurality of input MR datasets comprises at least 8 input MR datasets;
generating a respective plurality of MR images from the plurality of input MR datasets by using an MR image reconstruction technique;
estimating, using a neural network model, a plurality of RF coil profiles corresponding to the plurality of RF coils;
generating an MR image of the subject using the plurality of MR images and the plurality of RF coil profiles; and
outputting the generated MR image.

12. The at least one non-transitory computer readable storage medium of claim 11 , further comprising using the MRI system to image the subject to obtain the plurality of input MR datasets.

13. The at least one non-transitory computer readable storage medium of claim 11 , wherein generating the respective plurality of MR images from the plurality of input MR datasets is performed using a neural network MR image reconstruction technique.

14. The at least one non-transitory computer readable storage medium of claim 11 , wherein generating the respective plurality of MR images from the plurality of input MR datasets is performed using a compressed sensing MR image reconstruction technique.

15. The at least one non-transitory computer readable storage medium of claim 11 , wherein the neural network model comprises one or more convolutional layers.

16. The at least one non-transitory computer readable storage medium of claim 11 , wherein generating the MR image of the subject using the plurality of MR images and the plurality of RF coil profiles comprises:
generating the MR image of the subject as a weighted combination of the plurality of MR images, each of the plurality of MR images being weighted by a respective RF coil profile in the plurality of RF coil profiles.

17. The at least one non-transitory computer readable storage medium of claim 11 ,
wherein the plurality of MR images comprises a first MR image generated from a first input MR dataset obtained using a first RF coil of the plurality of RF coils, and
wherein generating the MR image of the subject comprises weighting different pixels of the first MR image using different values of a first RF coil profile among the plurality of RF coil profiles, the first RF coil profile being associated with the first RF coil.

Assignees

• Hyperfine Operations Inc

Inventors

• Schlemper Jo
• Moshen Salehi Seyed Sadegh
• Sofka Michal

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

• A61B5/055 Primary

  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

• G06V10/52

  Scale-space analysis, e.g. wavelet analysis (multi-scale boundary representations G06V10/42) · CPC title

• G06V10/431

  Frequency domain transformation; Autocorrelation · CPC title

• G06V10/30

  Noise filtering · CPC title