MRI Super-Resolution using Multi-Channel Total Variation

Mikael Brudfors, Yael Balbastre, Parashkev Nachev, John Ashburner
10/8/2018
cs.CV

Official code: brudfors/spm_superres (16 stars, last commit 2023-02-24).

Abstract

This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast. The model recasts the recovery of high resolution images as an inverse problem, in which a forward model simulates the slice-select profile of the MR scanner. The paper introduces a prior based on multi-channel total variation for MRI super-resolution. Bias-variance trade-off is handled by estimating hyper-parameters from the low resolution input scans. The model was validated on a large database of brain images. The validation showed that the model can improve brain segmentation, that it can recover anatomical information between images of different MR contrasts, and that it generalises well to the large variability present in MR images of different subjects. The implementation is freely available at https://github.com/brudfors/spm_superres

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Code Implementations(1)

brudfors/spm_superresOfficial100%

Super-resolution of multimodal clinical magnetic resonance images (MRIs)

164MATLABAug 31, 20193 years ago

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Cite this paper

@article{brudfors2018superresolution,
  title  = {MRI Super-Resolution using Multi-Channel Total Variation},
  author = {Mikael Brudfors and Yael Balbastre and Parashkev Nachev and John Ashburner},
  year   = {2018},
  eprint = {1810.03422},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1810.03422}
}

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