A deep image prior based refinement for 3D phase unwrapping in brain MRI

Motivation: MRI signals have phase information from the GRE sequence, which reflects B0 field homogeneities. Goal(s): Due to acquisition, the phase is converted from complex data, ranging from -π to π and causing visual discontinuities. However, previous learning-based approaches have difficulties processing 3D brain data directly. Approach: In this study, we introduced an unsupervised refinement based on Deep Image Prior to enhance the performance of the pre-trained networks (PHU-DIP), and the inference were performed on one simulated and one in vivo brain. Results: The PHU-DIP method corrected the misclassification regions from the pre-trained networks and exhibited the significant time-efficiency compared to conventional method. Impact: The PHU-DIP provided a refinement scheme that help to improve the performance of a well-trained network. This technique could also be expanded onto other training modes and other pathological conditions.