Adaptive Denoising Technique for Robust Analysis of Functional Magnetic Resonance Imaging Data

Abstract

A new adaptive signal-preserving technique for noise suppression in functional magnetic resonance imaging (fMRI) data is proposed based on spectrum subtraction. The proposed technique estimates a model for the power spectrum of random noise from the acquired data. This model is used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data for event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components. Moreover, further analysis using principal component analysis (PCA) and independent component analysis (ICA) shows a significant improvement in both convergence and clarity of results when the new technique is used. This suggests the value of the new technique as a useful preprocessing step for this type of signals.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410294

Entities

People

  • Abou-bakr M. Youssef
  • Bassel M. Tawfik
  • Bassem K. Ouda
  • Yasser M. Kadah

Organizations

  • Cairo University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Biomedical Engineering
  • Computational Science
  • Computer Simulations
  • Computers
  • Data Analysis
  • Data Science
  • Data Sets
  • Engineering
  • Information Science
  • Magnetic Resonance
  • Magnetic Resonance Imaging
  • Noise
  • Numbers
  • Power Spectra
  • Random Variables
  • Simulations
  • Spectra

Fields of Study

  • Physics

Readers

  • Image Processing and Computer Vision.
  • Neuroscience
  • Statistical inference.