US 2025/0029248 A1 · MACHINE LEARNING FOR MEDICAL IMAGE RECONSTRUCTION WITH PHASE CORRECTION

Abstract

For reconstruction in medical imaging using phase correction, a machine learning model is trained for reconstruction of an image. The reconstruction may be for a sequence without repetitions or may be for a sequence with repetitions. Where repetitions are used, rather than using just a loss for that repetition in training, the loss based on an aggregation of images reconstructed from multiple repetitions may used to train the machine learning model. In either approach, a phase correction is applied in machine training. A phase map is extracted from output of the model in training or extracted from the ground truth of the training data. The phase correction, based on the phase map, is applied to the ground truth and/or the output of the model in training. The resulting machine-learned model may better reconstruct an image as a result of having been trained using phase correction.

Claims13

Dependent claims

A method of machine training for magnetic resonance (MR) reconstruction in medical imaging, the method comprising:

acquiring MR training data including ground truth representations;
machine training a neural network for the MR reconstruction using the MR training data, wherein an output of the neural network and/or the ground truth representations are phase corrected;
and
storing the neural network as machine trained.

1 more element

Dependencies are inferred from the claim text.

Machine learning for medical image reconstruction with phase correction

Life of this patent

Machine learning for medical image reconstruction with phase correction

Life of this patent

Abstract

Claims13