Related Experiment Videos
Removal of phase artifacts from fMRI data using a Stockwell transform filter improves brain activity detection
Bradley G Goodyear1, Hongmei Zhu, Robert A Brown
1Department of Radiology, Seaman Family MR Research Centre, University of Calgary and Foothills Medical Centre, Calgary, Alberta T2N 2T9, Canada. goodyear@ucalgary.ca
Magnetic Resonance in Medicine
|January 6, 2004
Summary
This study introduces an automated method using the Stockwell transform (ST) to remove motion artifacts in functional MRI (fMRI) scans. This technique enhances brain activity detection by preserving crucial data while eliminating interference.
Area of Science:
- Neuroimaging
- Signal Processing
- Medical Physics
Background:
- Functional MRI (fMRI) is crucial for detecting brain activity.
- Motion artifacts, particularly from outside the field of view, degrade fMRI data quality.
- Existing methods for artifact removal can be complex or data-intensive.
Purpose of the Study:
- To develop a novel, automated technique for removing motion-induced artifacts in fMRI.
- To improve the accuracy of brain activity detection in fMRI datasets.
- To preserve essential signal information while effectively filtering out interference.
Main Methods:
- The technique employs the Stockwell transform (ST) for time-frequency analysis of fMRI data.
- One-dimensional Fourier transforms (FTs) generate phase profiles from raw image data.
- Artifacts are identified by analyzing frequency component magnitudes and removed by replacing them with median values, followed by an inverse ST.
Main Results:
- The ST-filtering method successfully identifies and removes artifact frequencies within specific time windows.
- Image artifacts overlapping anatomical regions of interest are significantly reduced.
- The technique preserves frequency information in non-artifactual parts of the signal.
Conclusions:
- The novel ST-based technique offers an effective and automated solution for fMRI motion artifact removal.
- This method enhances the reliability of fMRI for brain activity detection.
- The ability to selectively filter artifacts in narrow time windows is a key advantage.