Self-supervised machine learning for medical image reconstruction
AssigneeSiemens Healthineers AG
InventorsSimon Arberet, Mariappan S. Nadar, Mahmoud Mostapha, Dorin Comaniciu
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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.
Claims19
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.
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.
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.
Dependencies are inferred from the claim text.