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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