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

Wei Zhu, Xiaodong Ma, Xiao Hong Zhu, Kamil Ugurbil, Wei Chen, Xiaoping Wu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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 simulation and in-vivo high-resolution fMRI data show that the proposed denoising method dramatically improves image restoration quality, promoting functional sensitivity at the same level of functional mapping blurring compared to existing denoising methods. Moreover, the denoising method can serve as a drop-in step in data preprocessing pipelines along with other procedures aimed at removal of structured physiological noises. We expect that the proposed denoising method will play an important role in leveraging high-quality, high-resolution task fMRI, which is desirable in many neuroscience and clinical applications.

Original languageEnglish (US)
Pages (from-to)3377-3388
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number11
StatePublished - Nov 1 2022

Bibliographical note

Publisher Copyright:
© 1964-2012 IEEE.


  • Functional magnetic resonance imaging (fMRI)
  • image denoising
  • principal component analysis (PCA)
  • random matrix theory (RMT)
  • variance stabilizing transformation
  • Brain/diagnostic imaging
  • Algorithms
  • Computer Simulation
  • Signal-To-Noise Ratio
  • Image Processing, Computer-Assisted/methods
  • Magnetic Resonance Imaging/methods
  • Principal Component Analysis

Center for Magnetic Resonance Research (CMRR) tags

  • BFC
  • IRP

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural


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