In Magnetic resonance in medicine ; h5-index 66.0
PURPOSE : This work was aimed at designing a deep-learning-based approach for MR image phase unwrapping to improve the robustness and efficiency of traditional methods.
METHODS : A deep learning network called PHU-NET was designed for MR phase unwrapping. In this network, a novel training data generation method was proposed to simulate the wrapping patterns in MR phase images. The wrapping boundary and wrapping counts were explicitly estimated and used for network training. The proposed method was quantitatively evaluated and compared to other methods using a number of simulated datasets with varying signal-to-noise ratio (SNR) and MR phase images from various parts of the human body.
RESULTS : The results showed that our method performed better in the simulated data even under an extremely low SNR. The proposed method had less residual wrapping in the images from various parts of human body and worked well in the presence of severe anatomical discontinuity. Our method was also advantageous in terms of computational efficiency compared to the traditional methods.
CONCLUSION : This work proposed a robust and computationally efficient MR phase unwrapping method based on a deep learning network, which has promising performance in applications using MR phase information.
Zhou Hongyu, Cheng Chuanli, Peng Hao, Liang Dong, Liu Xin, Zheng Hairong, Zou Chao
artificial intelligence, deep learning, magnetic resonance imaging, phase unwrapping