Sparse MRI for motion correction
This addresses motion artifacts in MRI imaging, which is a domain-specific problem, but the method is incremental as it builds on existing compressed sensing techniques.
The paper tackles rigid-body motion correction in MRI by introducing a sparsity-based approach that jointly estimates motion and image content, achieving promising results without requiring additional data.
MR image sparsity/compressibility has been widely exploited for imaging acceleration with the development of compressed sensing. A sparsity-based approach to rigid-body motion correction is presented for the first time in this paper. A motion is sought after such that the compensated MR image is maximally sparse/compressible among the infinite candidates. Iterative algorithms are proposed that jointly estimate the motion and the image content. The proposed method has a lot of merits, such as no need of additional data and loose requirement for the sampling sequence. Promising results are presented to demonstrate its performance.