Blind Compressed Sensing Dynamic MRI

Sajan Goud Lingala¹, Mathews Jacob²

In this work, we introduce a novel blind compressive sensing frame work for dynamic MRI reconstruction. This models the temporal profile at each voxel as a sparse linear combination of temporal basis functions chosen from a large dictionary, which are also estimated from the data. We show that the model significantly reduces the number of degrees of freedom than what is seen in schemes based on promoting low rank structure of the data. We demonstrate this concept on myocardial perfusion data sets with significant inter-frame motion. Significant improvement in the reconstruction qualities over low rank schemes are observed (eg: better preservation of subtle spatial details, reduced temporal blur and artifacts).