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
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