Huisu Yoon1, Jong Chul Ye1
1Bio and Brain engineering, KAIST, Dae-jeon, Korea, Republic of
Compressed sensing (CS) tells us that the perfect reconstruction is possible if the nonzero support in transform domain is sparse and sampling basis are incoherent. By exploiting that dynamic MRI can be sparsified due to the temporal redundancy, we have demonstrated successful application of CS for cardiac imaging. In particular, more accurate prediction using motion estimation/compensation or data-driven optimal temporal sparsifying transforms have proven to be quite effective. However, despite their successes to some extent, there still remain considerable artifacts in edge area when the acceleration factor increases.We propose a non-local motion compensated k-t FOCUSS which generates more accurate prediction images than the existing motion compensated k-t FOCUSS. Non-local motion compensation retrieves similar blocks in another reference frame, not within the processed dynamic frame itself. Non-local motion compensation is shown MMSE optimal and experimental result shows that the proposed algorithm clearly reconstruct the important cardiac structures and improves over k-t FOCUSS.