Xing Lu1, Yajun Ma1, Saeed Jerban1, Hyungseok Jang1, Yanping Xue1, Xiaodong Zhang1, Mei Wu1, Amilcare Gentili1,2, Chun-nan Hsu3, Eric Y Chang1,2, and Jiang Du1
1Department of Radiology, University of California, San Diego, San Diego, CA, United States, 2Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States, 3Department of Neurosciences, University of California, San Diego, San Diego, CA, United States
1Department of Radiology, University of California, San Diego, San Diego, CA, United States, 2Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States, 3Department of Neurosciences, University of California, San Diego, San Diego, CA, United States
Towards simultaneous
segmentation and quantification analysis on multi-tissue of the knee, DCNNs of MSQ-Net and MSQ-Net with physical constraint(pcMSQ-Net) were proposed and testified its feasibility in this study. The results show promising results for both networks.
Figure
1. Network architecture of MSQ-Net and pcMSQ-Net. MSQ-Net with lossphy
to feed back the maps predicted from the model to the input MRI signals,
according to equation (6), named physical constraint MSQ-Net (pcMSQ-Net).
Figure 2. Typical results for MSQ-Net and pcMSQ-Net.(a)
MRI input signals with different Flip Angles(FAs); (b). T1 maps of GT, and
predicted by MSQ-Net, pcMSQ-Net; (c). Difference map from GT. Yellow arrows
demonstrates obvious errors could be found in MSQ-Net in some low-signal area
while not shown in pcMSQ-Net. (d) and (e), masks of cartilage and meniscus
of GT, and predicted by MSQ-Net and pcMSQ-Net.