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 # Unified Super-Resolution in PyTorch
 <img style="float: right;" src="https://github.com/brudfors/UniRes/blob/master/figures/example_2.png" width="100%" height="100%"> 
 
-This repository implements a unified model for super-resolving neuroimaging data 
-(MRI and CT scans), which combines: super-resolution with a multi-channel denoising 
+This repository implements a unified model for super-resolving medical images (MRI and CT scans), which combines: super-resolution with a multi-channel denoising 
 prior, rigid registration and a correction for interleaved slice acquisition. 
 The archetype use-case is when having multiple scans of the same subject 
 (e.g., T1w, T2w and FLAIR MRIs) and an analysis requires these scans to be