Automatic identification of motion in multishot MRI using convolutional neural networks

A major concern of multishot MRI acquisitions is the effect of subject motion, which can result in undesirable image artifacts. In order to discard or correct these images, the first step is to identify the images which have been corrupted. We describe an automated machine-learning method to identify motion-corrupted multishot images using unsupervised feature learning and a convolutional neural network. We demonstrate that the method can accurately classify motion-corrupted images of different contrasts and different multishot acquisition types. The result is an effective technique which eliminates the need for manual identification of motion artifacts in multishot images.

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