Non-stationary noise estimation in accelerated parallel MRI data (MISS Workshops 2016)
Poster presented at the Medical Imaging Summer School'2016, Favignana, Italy
The aim of this study is to retrieve spatially variant noise patterns from accelerated parallel MRI data using only a single image. Variance-stabilizing transformations (VSTs) for noncentral Chi data are derived: (1) an analytic model, and (2) a numerical model to improve the performance for low signal-to-noise ratios (SNRs). The VSTs generate Gaussian-like distributed variates from noncentral Chi data. The noise patterns are estimated then using Gaussian homomorphic filter.