  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.
