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Physiological noise reduction for arterial spin labeling functional MRI.

Khaled Restom1, Yashar Behzadi, Thomas T Liu

  • 1Department of Radiology, UCSD Center for Functional MRI, La Jolla, CA 92093-0677, USA.

Neuroimage
|March 15, 2006
PubMed
Summary

This study introduces three methods to reduce physiological noise in arterial spin labeling (ASL) functional MRI. The second method, which does not assume equal noise in tag/control images, showed superior performance, especially in the hippocampus.

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Physiological Monitoring

Background:

  • Physiological noise significantly impacts the signal quality of arterial spin labeling (ASL) functional magnetic resonance imaging (fMRI).
  • Existing methods like RETROICOR address physiological noise in blood oxygenation level dependent (BOLD) fMRI but require adaptation for ASL.
  • ASL-fMRI is crucial for measuring cerebral blood flow, offering insights into brain function, but is susceptible to noise artifacts.

Purpose of the Study:

  • To present and compare three novel methods for reducing physiological noise in ASL-fMRI.
  • To evaluate the effectiveness of these methods in improving statistical performance in different brain regions.
  • To identify the most robust method for enhancing ASL-fMRI data quality, particularly for cognitive neuroscience studies.

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Main Methods:

  • Developed three methods based on a general linear model of the ASL measurement process.
  • Adapted retrospective image-based methods (RETROICOR) for ASL-fMRI, with variations in assumptions about physiological noise distribution.
  • Assessed methods using functional activity data from visual cortex and hippocampal regions.

Main Results:

  • The first and second methods significantly improved statistical performance in both visual cortex and hippocampus.
  • The third method, assuming noise primarily affects the perfusion time series, did not yield significant gains.
  • The second method demonstrated superior performance over the first in the hippocampal region, likely due to cardiac fluctuation effects.

Conclusions:

  • The proposed methods effectively reduce physiological noise in ASL-fMRI, enhancing statistical power.
  • The second method, which relaxes assumptions about noise equality between tag and control images, is particularly effective.
  • These techniques are valuable for ASL studies, especially those investigating cognitive processes with inherently lower signal-to-noise ratios.