[2210.08676] Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2210.08676 (cs)
[Submitted on 17 Oct 2022 (v1), last revised 18 Oct 2022 (this version, v2)]

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

Authors: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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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2210.08676 [cs.CV]
  (or arXiv:2210.08676v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2210.08676
Journal reference: Medical Imaging with Deep Learning. 2022

Submission history

From: Dave Van Veen [view email]
[v1] Mon, 17 Oct 2022 00:42:12 UTC (13,160 KB)
[v2] Tue, 18 Oct 2022 01:38:32 UTC (13,160 KB)
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