Optimally Weighted Average (OWA) of Phase-Sensitive Data

Motivation: To produce accurate phase-sensitive MRI image reconstructions with effective phase-preserving methods, overcoming limitations of current data combination techniques. Goal(s): To investigate the optimally weighted average (OWA) in combining multi-component phase-based images and achieve artifact- and noise-reduced MRI reconstructions. Approach: The Lagrange multiplier is used to develop the theoretical foundation of extending OWA to a multi-component image combination scenario. Performance is validated by combining RF-channel-specific bSSFP off-resonant phase accumulation data in a phantom. Results: The OWA used regional variance weighting with an appropriate kernel size to minimize component-specific artifacts and general noise, outperforming the complex sum method. Impact: OWA data combination significantly enhances phase-preserving MRI reconstructions by minimizing artifacts and noise, outperforming the complex sum method. This technique promises improved diagnostic accuracy and image quality, beneficial for clinical applications where phase-sensitive data reconstruction is crucial.