Zuojun Wang1, Peng Xia1, Henry Ka Fung Mak2, and Peng Cao1
1Diagnostic Radiology, Department of Diagnostic Radiology, HKU, Hong Kong, China, 2Department of Diagnostic Radiology, HKU, Hong Kong, China
1Diagnostic Radiology, Department of Diagnostic Radiology, HKU, Hong Kong, China, 2Department of Diagnostic Radiology, HKU, Hong Kong, China
Here, we apply the cycle generative adversarial network with a perceptual loss to synthesize QSM images from SWI images. The predicted QSM images showed their application in brain microbleed detection.
Figure 2: Training results on the dataset from PD cohort. Most brain structures were delineated accurately by S2Q, compared with real QSM calculated from STAR-QSM. Furthermore, some residual artifacts near the boundary (red arrows) were disappeared in S2Q.
Figure 3: Testing results on another dataset from PD cohort. The residual artifacts near the nasal cavity were all removed. The testing set's model performance was comparable with that on the training set, suggesting minimal generalization error.
