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RETROSPECTIVE DETECTION OF INTERLEAVED SLICE ACQUISITION PARAMETERS FROM FMRI DATA
David Parker1, Georges Rotival1, Andrew Laine1
1Department of Biomedical Engineering, Columbia University, New York, NY 10032. USA.
Summary
Researchers developed a method to automatically detect the interleave parameter in functional MRI (fMRI) data, crucial for accurate analysis. This technique identifies disruptions in temporal-distance correlation, achieving 94% accuracy on real fMRI scans.
Area of Science:
- Neuroimaging
- Biomedical Engineering
- Data Analysis
Background:
- Interleaved slice acquisition is standard in functional MRI (fMRI) to prevent signal leakage.
- The interleave parameter, defining skipped slices, is vital for fMRI data analysis.
- Loss of interleave parameter information can severely compromise fMRI study outcomes.
Purpose of the Study:
- To develop a method for retrospectively detecting the interleave parameter and its acquisition axis in fMRI data.
- To address the critical need for accurate interleave parameter identification in fMRI analysis.
Main Methods:
- Utilized the temporal-distance correlation function, analyzing disruptions along the interleaved acquisition axis.
- Tested the method on simulated and real fMRI data, including common artifacts like physiological noise and motion.
- Evaluated the reliability across various interleave parameters.
Main Results:
- The method accurately detects the interleave parameter and its axis.
- Achieved approximately 94% accuracy in detecting interleave parameters across over 1000 real fMRI scans.
- Demonstrated robustness in the presence of fMRI artifacts.
Conclusions:
- The proposed method reliably and accurately identifies the interleave parameter in fMRI data.
- This technique is essential for ensuring the integrity and validity of fMRI data analysis.
- Facilitates retrospective correction and improves the reliability of fMRI studies.
