Does perfect filtering really guarantee perfect phase correction for diffusion MRI data?

Abstract

Owing to its merit of avoiding noise-floor, phase correction is recently used to reconstruct real-valued diffusion MRI data by employing an image filter to estimate the noise-free background phase. However, several studies report an unexpected signal-loss issue for their reconstruction results, with its causing reason still remaining unclear. Although phase correction has achieved promising results in mitigating the signal-loss issue via improving the employed image filter, we have observed counterintuitive results that an advanced filter generates severe artifacts in our previous work. Considering the potential issues with phase correction procedures, in this paper, we argue that even a perfect image filter is insufficient to produce perfect phase correction. To point out the reason why phase correction introduces signal-loss and address this issue, we first propose a complex polar coordinate system (CPCS) to analyze its procedures in detail; second, based on CPCS, we find that phase correction has not sufficiently utilized the background phase, and thus propose a quantitative criterion to fully exploit the background phase; eventually, we propose a phase calibration procedure to remedy current phase correction. Extensive experimental results, including those on synthetic and real diffusion MRI data, demonstrate that our proposed method significantly reduces signal-loss and also eliminates artifacts in FA maps, particularly with improved accuracy on FA.

Keywords: Adaptive filtering; Noise-floor; Phase correction; Spatially-varying noise.