Jiaren Zou1,2 and Yue Cao1,2,3
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
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 training of reconstruction network, sampling
pattern and data sharing for dynamic contrast-enhanced MRI was investigated. The
temporal degree of freedom of the sampling pattern and learned data sharing can
improve the reconstruction accuracy.
Figure 1. The network structure with joint learning of
sampling pattern and data sharing. The interconnections in the data sharing
stage highlight the different data sharing used by each frame.
Figure 2. Exemplary reconstruction results of a
testing frame. The (a) ground truth, (b, c) with temporal DoF and data sharing (model
A) and its error map, (d, e) with temporal DoF and without data sharing (model
B) and its error map, (f, g) without temporal DoF and data sharing (model C) and
its error map, and (h, i) trained with pseudo golden angle radial sampling and
without data sharing (model D) and its error map are shown. Model A shows
better qualitative reconstruction quality.