Convolutional neural networks (CNNs) have had incredible success solving image segmentation problems. We explore whether CNNs could have a similar level of success on difficult image registration problems. To this end, we developed a modified U-net to remove respiratory motion, but preserve contrast changes in abdominal free breathing dynamic contrast enhanced (DCE)-MRI. We then compared this network to a state of the art iterative registration algorithm. We demonstrate that our modified U-net outperforms iterative methods both in terms of registration quality and speed (600 registrations in <1 sec vs. Elastix in 2 hours)
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