Iterative hierarchal network for regulating medical image reconstruction

Patent metadata

Field	Value
Publication number	US-12008690-B2
Application number	US-202117155630-A
Country	US
Kind code	B2
Filing date	Jan 22, 2021
Priority date	Nov 25, 2020
Publication date	Jun 11, 2024
Grant date	Jun 11, 2024

Abstract

For reconstruction in medical imaging, such as reconstruction in MR imaging, an iterative, hierarchal network for regularization may decrease computational complexity. The machine-learned network of the regularizer is unrolled or made iterative. For each iteration, nested U-blocks form a hierarchy so that some of the down-sampling and up-sampling of some U-blocks begin and end with lower resolution data or features, reducing computational complexity.

First claim

What is claimed is:

1. A method for reconstruction of a medical image in a medical imaging system, the method comprising:
scanning, by the medical imaging system, a patient, the scanning resulting in measurements;
reconstructing, by an image processor, the medical image from the measurements, the reconstructing including a regularizer implemented with a machine-learned network comprising iterative hierarchal convolutional networks, each of the iterative hierarchal convolutional networks including both down-sampling and up-sampling nested within other down-sampling and up-sampling; and
displaying the medical image.

2. The method of claim 1 wherein scanning comprises scanning with the medical imaging system being a magnetic resonance (MR) scanner and the measurements being k-space measurements.

3. The method of claim 1 wherein reconstructing comprises reconstructing as an unrolled iterative reconstruction where each of multiple reconstructions in the unrolled iterative reconstruction includes regularization, the regularizer providing the regularization for at least one of the multiple reconstructions.

4. The method of claim 1 wherein reconstructing comprises reconstructing with the machine-learned network including a feature extraction block comprising one or more convolution layers, the feature extraction block being prior to the iterative hierarchal convolutional networks.

5. The method of claim 1 wherein reconstructing comprises reconstructing with the machine-learned network wherein the iterative hierarchal convolutional networks have different weights.

6. The method of claim 1 wherein reconstructing comprises reconstructing with the machine-learned network further comprising a memory network with convolution layers, the memory network applied separately to the outputs of the iterative hierarchal convolutional networks.

7. The method of claim 6 wherein reconstructing comprises reconstructing with the machine-learned network further comprising an enhancement block of one or more convolutional layers, the enhancement block receiving a concatenation of outputs of the memory network and outputting the medical image as regularized.

8. The method of claim 1 wherein reconstructing comprises receiving a complex image output by a gradient update, the machine-learned network having two channel input for the complex image, and outputting the medical image with complex values.

9. The method of claim 1 wherein reconstructing comprises reconstructing with the iterative hierarchal convolutional networks each comprising U-blocks at different levels of the down-sampling and the up-sampling and comprising at least one concatenation connection in parallel with a bottleneck.

10. The method of claim 9 wherein the U-blocks comprise down-sampling and up-sampling layers and comprise local and global connections.

11. The method of claim 1 wherein scanning comprise scanning for patient diagnosis as one of different imaging applications for different anatomy and/or disease, and wherein reconstructing comprises application of the machine-learned network independent of the different imaging applications, the machine-learned network having been trained on reconstructions for the different imaging applications.

12. A method of machine training for reconstruction in medical imaging, the method comprising:
machine training a neural network with an unrolled arrangement of U-blocks in a sequence as a regularizer for the reconstruction in the medical imaging, the neural network comprising some of the U-blocks nested within other of the U-blocks; and
storing the neural network as machine trained.

13. The method of claim 12 wherein machine training comprises machine training the neural network with the U-blocks comprising convolutional neural networks with down-sampling and up-sampling.

14. The method of claim 12 wherein machine training comprises machine training with each of the U-blocks in the sequence having a hierarchy of U-networks.

15. The method of claim 14 wherein machine training comprises machine training with the hierarchy of U-networks, each of the U-networks in the hierarchy having local and global residual connections.

16. The method of claim 12 wherein machine training comprises machine training the neural network with the neural network further comprising a convolution network configured to receive outputs from the U-blocks of the sequence and separately process the outputs with shared weights.

17. The method of claim 12 wherein machine training comprises machine training with ground truth imaging from different medical applications and/or types of imaging.

18. A system for reconstruction in medical imaging, the system comprising:
a medical scanner configured to scan a region of a patient, the scan providing scan data;
an image processor configured to reconstruct a representation of the region from the scan data, the image processor configured to reconstruct by application of a machine-learned model in a regularization stage, the machine-learned model comprising a down-sampling and up-sampling first block having multiple down-sampling and up-sampling second blocks; and
a display configured to display an image of the region from the reconstructed representation.

19. The system of claim 18 wherein the down-sampling and up-sampling first block is one of a sequence of down-sampling and up-sampling first blocks in the regularization stage.

20. The system of claim 18 wherein the down-sampling and up-sampling first block comprises a first convolutional neural network, and wherein the down-sampling and up-sampling second blocks comprise second convolutional neural networks.

Assignees

• Siemens Healthcare Gmbh
• Siemens Healthineers Ag

Inventors

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

Classifications

• G06T12/20

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

• G06T12/30 Primary

  Image post-processing, e.g. metal artefact correction · CPC title

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title