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Abstract #1174

Motion Correction in MRI using Deep Convolutional Neural Network

Kamlesh Pawar1,2, Zhaolin Chen1,3, N Jon Shah1,4, and Gary F Egan1,2

1Monash Biomedical Imaging, Monash University, Melbourne, Australia, 2School of Psychological Sciences, Monash University, Melbourne, Australia, 3Department of Electrical and Computer System Engineering, Monash University, Melbourne, Australia, 4Institute of Medicine, Research Centre Juelich, Juelich, Germany

Patient motion during MR data acquisition appears in the reconstructed image as blurring and incoherent artefacts. In this work, we present a novel deep learning encoder-decoder convolutional neural network (CNN) that recasts the problem of motion correction into an artefact reduction problem. A CNN was designed and trained on simulated motion corrupted images that learned to remove the motion artefact. The motion compensated image reconstruction was transformed into quantized pixel classification, where each pixel in the motion corrupted MR image was classified to its uncorrupted quantized value using a trained deep learning encoder-decoder CNN.

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