MingJian Hong1, Feng Liu2, XiaoHong Zhang1, YongXin Ge1
1School of Software Engineering, ChongQing University, ChongQing, China; 2School of Information Technology & Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
In this work, a sparsity-enforced Kalman filter technique for dynamic cardiac imaging is presented. The Kalman filter is firstly casted into a framework of optimization, and then a sparsity constraint is incorporated to the framework for better motion capture of the imaging object. Applications to cardiac dynamic MRI clearly demonstrated the strength of the proposed method.