Undersampled MRI reconstruction directly in the k-space using a complex valued ResNet

Complete title: Going beyond the image space: undersampled MRI reconstruction directly in the k-space using a complex valued residual neural network

ISMRM 2021 presentation - May 2021

Synopsis:
One of the common problems in MRI is the slow acquisition speed, which can be solved using undersampling. But this might result in image artefacts. Several deep learning based techniques have been proposed to mitigate this problem. Most of these methods work only in the image space. Fine anatomical structures obscured by artefacts in the image can be challenging to reconstruct for a model working in the image space, but not in k-space. In this research, a novel complex-valued ResNet has been proposed to work directly in the k-space to reconstruct undersampled MRI. The preliminary experiments have shown promising results.

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