Denoise Functional Magnetic Resonance Imaging With Random Matrix Theory Based Principal Component Analysis.

ABSTRACT: High-resolution functional MRI (fMRI) is largely hindered by random thermal noise. Random matrix theory (RMT)-based principal component analysis (PCA) is promising to reduce such noise in fMRI data. However, there is no consensus about the optimal strategy and practice in implementation. In this work, we propose a comprehensive RMT-based denoising method that consists of 1) rank and noise estimation based on a set of newly derived multiple criteria, and 2) optimal singular value shrinkage, with each module explained and implemented based on the RMT. By incorporating the variance stabilizing approach, the denoising method can deal with low signal-to-noise ratio (SNR) (such as <5) magnitude fMRI data with favorable performance compared to other state-of-the-art methods. Results from both sim

SUBMITTER: Zhu W 

PROVIDER: S-EPMC9579216 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature