Motion artefacts remain a common problem in MRI. Deep learning presents a solution for motion correction requiring no modifications to image acquisition. This work investigates incorporating multichannel MRI data for motion correction using a conditional generative adversarial network (cGAN). Correcting for motion artefacts in the single-channel images prior to coil combination improved performance compared to motion correction on coil-combined images. The model trained for simultaneous motion correction of multichannel data produced the worst result, likely a result of its limited modelling capacity (reduced due to memory limitations).
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