Iterative Non-Local Shrinkage Algorithm for MR Image Reconstruction
This work addresses MRI image reconstruction for medical imaging applications, presenting an incremental improvement in speed and artifact reduction over existing non-local algorithms.
The authors tackled the problem of reconstructing MRI data from undersampled Fourier measurements by introducing a fast iterative non-local shrinkage algorithm, which resulted in a considerable reduction in alias artifacts and preservation of edges compared to state-of-the-art methods.
We introduce a fast iterative non-local shrinkage algorithm to recover MRI data from undersampled Fourier measurements. This approach is enabled by the reformulation of current non-local schemes as an alternating algorithm to minimize a global criterion. The proposed algorithm alternates between a non-local shrinkage step and a quadratic subproblem. We derive analytical shrinkage rules for several penalties that are relevant in non-local regularization. The redundancy in the searches used to evaluate the shrinkage steps are exploited using filtering operations. The resulting algorithm is observed to be considerably faster than current alternating non-local algorithms. The comparisons of the proposed scheme with state-of-the-art regularization schemes show a considerable reduction in alias artifacts and preservation of edges.