Abstract #4095

Deep learning-based real time MRI

Vahid Ghodrati^1,2, Jiaxin Shao¹, Ziwu Zhou^1,3, Yu Gao^1,2, Fadil Abbas Ali^1,2, Fei Han¹, and Peng Hu¹

¹Department of Radiological Sciences, University of California, Los Angeles, los angeles, CA, United States, ²Biomedical Physics Interdepartmental Program, University of California, Los Angeles, los angeles, CA, United States, ³Department of Bioengineering, University of California, Los Angeles, los angeles, CA, United States

In this work, we use the dilated U-net to reconstruct the dynamic free breathing cine images from regular under sampled raw data. Also, we consider different under sampling rate to determine maximum achievable rate. Moreover, we modify the acquisition procedure of sequence to show the possibility of prospective dynamic imaging. The proposed method is capable of reconstructing high quality real time 2D cardiac images with up to 6X acceleration with excellent image quality and minimal latency of only 6ms on a typical workstation.

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