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Abstract #1769

K-T ARTS-GROWL: An Efficient Combination of Dynamic Artificial Sparsity and Parallel Imaging Method for DCE MRI Reconstruction

Zhifeng Chen1, Liyi Kang1, Allan Jin2, Feng Liu3, Ling Xia1, and Feng Huang2

1Biomedical Engineering, Zhejiang University, Hangzhou, China, People's Republic of, 2Philips Healthcare (Suzhou) Co. Ltd, Suzhou, China, People's Republic of, 3School of Information Technology and Electrical Engineering, The University of Queensland, Queensland, Australia

Dynamic contrast enhanced (DCE) MRI plays an important role in the detection of liver metastases, characterization of tumors, assessing tumor response and studying diffuse liver disease. It requires a high spatial-temporal resolution. Existing iterative dynamic MRI reconstruction algorithms, such as iGRASP and L+S, realize their functions through iterative schemes. Though the solutions are generally acceptable, yet suffer from significantly high computational cost. This study proposed to use dynamic artificial sparsity and non-Cartesian parallel imaging for high spatiotemporal resolution DCE reconstruction, which results in comparable image quality relative to the above iterative schemes with greatly reduced computational cost.

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