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Abstract #2857

Comparison between Cartesian and Radial Under-sampling Schemes for Fast MRI using a Deep Cascade of Neural Network

Zhengxin GAO1,2, Zhuoran Jiang2,3, and Zheng Jim Chang2
1Medical Physics Program, Duke Kunshan University, Jiangsu, China, 2Department of Radiation Oncology, Duke Univeristy, Durham, NC, United States, 3Electronic Science and Engineering, Nanjing University, Nanjing, China

Cartesian under-sampling scheme was commonly applied in fast MRI using a deep neural network to simulate the process of fast image acquisition, however, it might not be optimal at a high under-sampling rate. Alternatively, radial under-sampling scheme was used and its efficiency was compared against that of Cartesian under-sampling scheme for T1- or T2-weighted brain, breast, prostate and cervical MRI data at various under-sampling rates. The quantitative evaluation results demonstrated that radial under-sampling scheme could outperformed Cartesian under-sampling scheme on reducing scan time while achieving comparable or better image quality.

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