Chuyu Liu1, Shuo Chen1, and Rui Li1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
By adapting 3D FuseUnet, CNN fSNAP showed better performance in lumen and
IPH depiction compared with traditional fSNAP. The results suggest that deep learning can help fast SNAP
scans produce high quality images, which could have great clinical utility.
Figure 2. Comparison of fSNAP and CNN fSNAP
Figure 1. The 3D FuseUnet used in this study. The
real and imaginary parts of IR-TFE and fSNAP are used as input 1 and input 2 respectively
and the loss for the network is MSE.