MRI Super-Resolution using Multi-Channel Total Variation
This work addresses the challenge of enhancing resolution in routine clinical MRI images, which is important for medical imaging applications, but it appears incremental as it builds on existing inverse problem and total variation methods.
The paper tackles the problem of super-resolution in clinical MRI images by introducing a generative model that recasts high-resolution recovery as an inverse problem, using a multi-channel total variation prior and hyper-parameter estimation from low-resolution inputs; validation on a large brain image database showed improvements in brain segmentation and recovery of anatomical information across different MR contrasts.
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