Self-supervised machine learning for medical image reconstruction

Patent metadata

Field	Value
Publication number	US-12182998-B2
Application number	US-202217652528-A
Country	US
Kind code	B2
Filing date	Feb 25, 2022
Priority date	Feb 25, 2022
Publication date	Dec 31, 2024
Grant date	Dec 31, 2024

Abstract

For reconstruction in medical imaging, self-consistency using data augmentation is improved by including data consistency. Artificial intelligence is trained based on self-consistency and data consistency, allowing training without supervision. Fully sampled data and/or ground truth is not needed but may be used. The machine-trained model is less likely to reconstruct images with motion artifacts, and/or the training data may be more easily gathered by not requiring full sampling.

First claim

What is claimed is:

1. A method of machine training for magnetic resonance (MR) reconstruction in medical imaging, the method comprising:
acquiring MR training data;
generating augmentation data from the MR training data;
machine training a neural network for the MR reconstruction using the MR training data and the augmentation data, wherein the machine training generates an output for each of the pairs of the MR training data and/or augmentation data and uses (1) a self-consistency loss between the respective output pairs and (2) a data-consistency loss and/or data-consistency constraint; and
storing the neural network as machine trained.

2. The method of claim 1 wherein acquiring comprises acquiring samples of MR k-space data, wherein generating comprises generating samples of MR k-space data, and wherein machine training comprises training the neural network to output an MR image from input of the MR k-space data.

3. The method of claim 1 wherein generating the augmentation data comprises generating sampling pattern augmentation and/or transposition augmentation.

4. The method of claim 1 wherein generating the augmentation data comprises separating repetitions for a scan, one of the repetitions being the MR training data and another of the repetitions being the augmentation data.

5. The method of claim 1 wherein acquiring comprises acquiring the MR training data as datasets without full sampling.

6. The method of claim 1 wherein machine training comprises calculating the data-consistency loss and/or applying the data-consistency constraint for each of the outputs of the pair.

7. The method of claim 1 wherein machine training comprises using the data-consistency constraint as integrated into the neural network.

8. The method of claim 1 wherein machine training comprises using the data-consistency loss.

9. The method of claim 1 wherein machine training comprises self-supervised machine training.

10. The method of claim 9 further comprising supervised machine training the neural network after the self-supervised machine training.

11. The method of claim 1 wherein machine training comprises incorporating the self-consistency loss as a regularization term in supervised machine training.

12. 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 applying a machine-learned model, the medical image from the measurements, the machine-learned model having been trained with both self-consistency and data consistency enforcement using outputs of the machine-learned model when input pairs of sample data, wherein at least one of the sample data is augmented; and
displaying the medical image.

13. The method of claim 12 wherein scanning comprises magnetic resonance scanning, the measurements comprise k-space data, and the medical image comprises a magnetic resonance image.

14. The method of claim 12 wherein reconstructing comprises reconstructing by application of the machine-learned model, the machine-learned model having been trained with self-consistency as a self-consistency loss based on data augmentation.

15. The method of claim 14 wherein reconstructing comprises reconstructing by the application of the machine-learned model, the machine-learned model having been trained with self-supervised training using the self-consistency loss where the data augmentation was sampling pattern augmentation and/or transpose augmentation.

16. The method of claim 14 wherein reconstructing comprises reconstructing by the application of the machine-learned model, the machine-learned model having been trained where the data augmentation was division of multiple scan repetitions into separate inputs.

17. The method of claim 12 wherein reconstructing comprises reconstructing by application of the machine-learned model, the machine-learned model having been trained with the data consistency being a data consistency constraint in an architecture of the machine-learned model or a data consistency loss.

18. The method of claim 12 wherein reconstructing comprises reconstructing by application of the machine-learned model, the machine-learned model having been trained with both self-consistency and data consistency in a first optimization and then supervised learning in a second optimization.

19. A system for reconstruction in medical imaging, the system comprising:
a medical scanner configured to scan a patient;
an image processor configured to reconstruct a representation of the patient, the image processor configured to reconstruct by application of a machine-learned model having been trained for the reconstruction based on self-supervised learning with data consistency and self-consistency enforcement using outputs of the machine-learned model when input pairs of sample data, wherein at least one of the sample data is augmented; and
a display configured to display an image of the patient, the image formed from the representation.

Assignees

• Siemens Healthineers Ag

Inventors

• Arberet Simon
• Nadar Mariappan S
• Mostapha Mahmoud
• Comaniciu Dorin

Classifications

• G01R33/4818

  MR characterised by data acquisition along a specific k-space trajectory or by the temporal order of k-space coverage, e.g. centric or segmented coverage of k-space · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/10088

  Magnetic resonance imaging [MRI] · CPC title

• G16H30/40

  for processing medical images, e.g. editing · CPC title