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Abstract #1175

Low-rank and Framelet Based Sparsity Decomposition for Reconstruction of Interventional MRI in Real Time

Zhao He1, Ya-Nan Zhu2, Suhao Qiu1, Xiaoqun Zhang2, and Yuan Feng1
1Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2School of Mathematical Sciences, MOE-LSC and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China

A low-rank and sparsity (LS) decomposition algorithm with framelet transform was proposed for real-time interventional MRI (i-MRI). Different from the existing LS decomposition, we exploited the spatial sparsity of both the low-rank and sparsity components. A primal dual fixed point (PDFP) method was adopted for optimization to avoid solving subproblems. We carried out intervention experiments with gelatin and brain phantoms to validate the algorithm. Reconstruction results showed that the proposed method can achieve an acceleration of 40 folds.

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