Abstract #0076

Dynamic Imaging Using Sparse Sampling with Rank & Group Sparsity Constraints

Anthony G. Christodoulou¹,², S. Derin Babacan², Zhi-Pei Liang¹,²

¹Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States; ²Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States

This work highlights a novel dynamic imaging method which jointly uses partial separability (PS), sparsity, and group sparsity constraints to enable sparse sampling in (k,t)-space. The specific formulation of the group sparsity spatially varies the effective model order of the PS constraint as a form of controlling the balance between the PS and sparsity constraints.