Performance Metric for Assessment of Reconstructed Magnetic Resonance Image Phase

Motivation: Many deep learning (DL) reconstruction models do not assess the reconstructed phase despite its importance in certain imaging techniques. Goal(s): To develop a phase-specific metric and demonstrate its suitability for reconstruction assessment. Approach: We used our developed metric to assess and analyze DL and non-DL reconstruction results in an experiment investigating the effect of coil overlap on DL reconstruction methods. The phase metric results were compared to magnitude metric results. Results: The phase metric results were consistent with the magnitude metric results and provided useful insights into model performance. Impact: We propose and test a phase-specific metric that can be used to assess and further the development of complex-valued DL reconstruction methods. This metric would allow for DL reconstruction methods to be applied to MR imaging techniques such as phase contrast imaging.