MRI Super-Resolution using Multi-Channel Total Variation

TL;DR

This paper introduces a novel MRI super-resolution method using a multi-channel total variation prior, effectively enhancing image quality and anatomical detail across various contrasts and orientations in clinical MRI scans.

Contribution

It proposes a new generative model that recasts MRI super-resolution as an inverse problem with a multi-channel total variation prior, handling bias-variance trade-off via hyper-parameter estimation.

Findings

Improves brain segmentation accuracy.

Recovers anatomical details across different MR contrasts.

Generalizes well to diverse MR images.

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