Keywords: AI/ML Image Reconstruction, Data Analysis
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.
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