Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate NetworksDownload PDF

22 Apr 2022, 06:20 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • Keywords: Coordinate networks, super-resolution, MRI
  • TL;DR: We present a framework for scale-agnostic MRI super-resolution by using the continuous representation of a coordinate network to decode the latent space.
  • Abstract: 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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  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Image Acquisition and Reconstruction
  • Secondary Subject Area: Application: Radiology
  • Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
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