Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks

Dave Van Veen, Rogier Van der Sluijs, Batu Ozturkler, Arjun D Desai, Christian Bluethgen, Robert D. Boutin, Marc H. Willis, Gordon Wetzstein, David B. Lindell, Shreyas Vasanawala, John M. Pauly, Akshay Chaudhari

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We propose using a coordinate network as a decoder for MRI super-resolution. The continuous signal representation of coordinate networks enables this approach to be scale-agnostic, i.e. training over a continuous range of scales and querying at arbitrary resolutions. We evaluate the benefits of denoising for coordinate networks and also compare our method to a convolutional decoder using image quality metrics and a radiologist study.
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Wednesday 6th July
Poster Session 1.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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