Training a Machine Learning Model for Image Enhancement for use in Magnetic Resonance Image Reconstruction

Abstract: techniques are provided for training a machine learning model (mlm) for image enhancement for use in a magnetic resonance (mr) image, in which a point spread function for undersampled mr data acquisition is received. a cropped point spread function is determined, which is given by the point spread function within a predefined spatial region. at least one training mr dataset corresponding to at least one coil channel is received, and a ground truth reconstructed mr image corresponding to the at least one training mr dataset is received. the mlm is trained in a supervised manner depending on the at least one training mr dataset, on the ground truth reconstructed mr image, and on a fourier transform of the cropped point spread function.

Inventor(s): Marcel Dominik Nickel

CPC Classification: G06T5/60 (using machine learning, e.g. neural networks)

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