Is Perfect Filtering Enough Leading to Perfect Phase Correction for dMRI data?

TL;DR

This paper argues that perfect filtering alone cannot achieve perfect phase correction in diffusion MRI due to noise sign ambiguity, and proposes a calibration method to address this issue.

Contribution

It introduces a new perspective on noise-floor correction in dMRI, emphasizing the importance of noise sign distinction and proposing a simple calibration procedure.

Findings

Perfect filtering is insufficient for phase correction due to noise sign ambiguity.

A new calibration method effectively distinguishes noise signs without external tools.

The proposed approach improves the accuracy of phase correction in complex-valued dMRI data.

Abstract

Being complex-valued and low in signal-to-noise ratios, magnitude-based diffusion MRI is confounded by the noise-floor that falsely elevates signal magnitude and incurs bias to the commonly used diffusion indices, such as fractional anisotropy (FA). To avoid noise-floor, most existing phase correction methods explore improving filters to estimate the noise-free background phase. In this work, after diving into the phase correction procedures, we argue that even a perfect filter is insufficient for phase correction because the correction procedures are incapable of distinguishing sign-symbols of noise, resulting in artifacts (\textit{i.e.}, arbitrary signal loss). With this insight, we generalize the definition of noise-floor to a complex polar coordinate system and propose a calibration procedure that could conveniently distinguish noise sign symbols. The calibration procedure is conceptually simple and easy to implement without relying on any external technique while keeping distinctly effective.