Hongyu Zhou1, Chuanli Cheng1, Xin Liu1, Hairong Zheng1, and Chao Zou1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
This work proposed a robust MR phase
unwrapping method based on a deep-learning method. Through comparisons of MR
phase-factor images over the entire body, the model showed promising
performances in both unwrapping errors and computation times. Therefore, it has
promise in applications that use MR phase information.
Fig. 5. Comparison of the four methods using the
phase factor (phasor) images (First column) of (A) brain & neck (coronal);
(B) breast (transverse); (C) hand (coronal); (D) upper abdomen (transverse);
(E) lower abdomen and pelvis (coronal); (F) thighs (transverse); (G) calves
(transverse); (H) ankle (sagittal). Red arrows indicate the unsolved regions.
Color bars of the unwrapped phase images are shown on the right side.
Figure 1: the
unsupervised network architecture of the proposed method