Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
Dave Van Veen, Rogier van der Sluijs, Batu Ozturkler, Arjun Desai, Christian Bluethgen, Robert D. Boutin, Marc H. Willis, Gordon Wetzstein, David Lindell, Shreyas Vasanawala, John Pauly, Akshay S. Chaudhari
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We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale-agnostic, i.e. one can train over a continuous range of scales and subsequently query at arbitrary resolutions. Due to the difficulty of performing super-resolution on inherently noisy data, we analyze network behavior under multiple denoising strategies. Lastly we compare this method to a standard convolutional decoder using both quantitative metrics and a radiologist study implemented in Voxel, our newly developed tool for web-based evaluation of medical images.