A Positive and Negative Learning based Image Decomposition Network for Phase Unwrapping and Background Removal

Motivation: Phase images contain important information useful in many fields. However, the phase data is often wrapped into a specific range, while background or noise signal in imaging scene may bring significant interference. Goal(s): To obtain the exact information, phase images need an accurate processing that includes the unwrapping and the background removal. Approach: In this paper, we propose a positive and negative learning based image decomposition network (PNnet) to accomplish the phase processing by a single network. Results: Experimental results demonstrate that PNnet can achieve excellent performance and efficient generalization, even for complex wrapping and inhomogeneous background. Impact: Except magnitude images, phase data in MRI also contain important information that is useful in many fields and scenarios. This work proposed a SOTA method for phase processing with high accuracy and excellent performance.