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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

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Published on: June 21, 2024

Respiratory noise correction using phase information.

Hu Cheng1, Yu Li

  • 1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA. hucheng@indiana.edu

Magnetic Resonance Imaging
|January 26, 2010
PubMed
Summary
This summary is machine-generated.

A new method corrects respiratory noise in functional MRI (fMRI) data using phase information. This technique effectively removes noise without needing to monitor subject respiration, offering an advantage over existing methods.

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Signal Processing

Background:

  • Respiratory motion is a significant source of noise in functional MRI (fMRI) data.
  • This noise confounds the analysis of brain activity and reduces data quality.
  • Existing methods often require simultaneous respiration monitoring, complicating experimental protocols.

Purpose of the Study:

  • To introduce and evaluate a novel retrospective method for correcting respiratory noise in fMRI.
  • To demonstrate the correlation between respiratory movement and image phase in fMRI.
  • To compare the proposed method with existing techniques like RETROICOR.

Main Methods:

  • Developed a method using phase information to estimate and correct respiratory noise in fMRI time series.
  • Employed Wiener filtering to remove estimated respiratory signal fluctuations.
  • Compared performance against RETROICOR, a standard method requiring simultaneous respiration recording.

Main Results:

  • Respiratory movement and fMRI image phase are highly correlated.
  • The proposed method effectively estimates and removes respiratory noise from fMRI data.
  • Performance was comparable to RETROICOR, but without the need for respiration monitoring.

Conclusions:

  • The novel phase-based method provides effective retrospective respiratory noise correction for fMRI.
  • This technique offers a significant advantage by eliminating the need for respiration monitoring.
  • The method is robust and applicable to various breathing patterns, including abnormal or non-periodic respiration.