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Abstract #2258

Improved Dynamic Imaging with PS-Model-Based Sparse Sampling

Xiang Feng1, Guoxi Xie1, Chao Zou1, Anthony G. Christodoulou2, Xin Liu1, Bensheng Qiu1

1Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

The Partial Separability (PS) of spatiotemporal signals has been exploited effectively for sparse (k, t)-space sampling in dynamic MRI. It has been successfully applied to real-time cardiac MRI and myocardial perfusion. In the conventional PS model-based sparse sampling scheme, the navigator data are reordered in every navigator cycle by using a projection strategy. This reordering method assumes that temporal signal changes are negligible within the navigator cycle. However, this assumption may result in an suboptimal temporal estimation. To address this issue, we present a sliding window method for reordering the navigator data. in vivo cardiac imaging results demonstrated that the proposed method could produce much better reconstructions with higher temporal resolution.