Abstract #2601

Deep convolution neural network exploration for super resolution of abdominal 3D mDixon scans

Johannes M Peeters¹ and Marcel Breeuwer^1,2

¹MR Clinical Science, Philips, Best, Netherlands, ²Biomedical Engineering – Medical Image Analysis, Eindhoven University of Technology, Eindhoven, Netherlands

Breath holding is often applied for abdominal imaging to avoid motion artifacts. However, breath holding limits the acquisition time and thus the resolution of the images. We propose to use 3D super resolution deep convolutional neural networks (CNN) to enhance the sharpness of 3D mDixon MRI. We found that sharpness increases with increasing number of network layers, but levels off already at 6 layers.

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