Abstract #1948

Joint deep learning-based optimization of undersampling pattern and reconstruction for dynamic contrast-enhanced MRI

Jiaren Zou^1,2 and Yue Cao^1,2,3

¹Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States, ²Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ³Department of Radiology, University of Michigan, Ann Arbor, MI, United States

Joint optimization of deep learning based undersampling pattern and the reconstruction network has shown to improve the reconstruction accuracy for a given acceleration factor in static MRI. Here, we investigate the joint training of a reconstruction network, sampling pattern and data sharing for dynamic contrast-enhanced MRI. By adding a degree of freedom in the temporal direction to the sampling pattern, better reconstruction quality can be achieved. Jointly learned data sharing can further improve the reconstruction accuracy.

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