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Published on: May 27, 2020
Automatic Quality Control for Resting-State BOLD-Based Cerebrovascular Reactivity Mapping
Yutian Wang1, Peiying Liu2, Mingyang Li1
1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
Researchers developed an automated quality control algorithm to identify poor-quality resting-state cerebrovascular reactivity (CVR) mapping. This tool ensures reliable vascular health research using BOLD fMRI data, improving data quality for large-scale studies.
Area of Science:
- Neuroimaging
- Cardiovascular Science
- Biomedical Engineering
Background:
- Resting-state BOLD fMRI enables cerebrovascular reactivity (CVR) mapping for vascular health research.
- Spontaneous CO2 fluctuations used as endogenous stimuli can lead to low signal-to-noise ratio (SNR) and poor reproducibility in resting-state CVR.
- Automatic quality control is essential for reliable large-scale CVR research.
Purpose of the Study:
- To develop and validate an automatic quality control algorithm for resting-state CVR mapping.
- To create a comprehensive quality evaluation index (QEI) for assessing resting-state CVR data quality.
Main Methods:
- Extracted quality control parameters including pooled variance, proportion of negative voxels, and BOLD signal sensitivity to CVR.
- Combined parameters into a single Quality Evaluation Index (QEI).
- Validated QEI using leave-one-out cross-validation and correlation analysis with test-retest reproducibility across multiple scanning protocols and participants.
Main Results:
- Leave-one-out cross-validation showed significant correlation (r=0.766) between QEI and reference quality evaluation.
- Correlation analyses revealed significant positive correlations between QEI and CVR map similarity across test-retests (r=0.809, 0.890, 0.396, 0.654).
- The QEI demonstrated good agreement with visual inspection and adaptability across different age groups and scanning protocols.
Conclusions:
- The developed automatic quality control algorithm and QEI effectively evaluate resting-state CVR mapping quality.
- The QEI is reliable, reproducible, and adaptable, supporting clinical applications of resting-state CVR.
- This tool facilitates large-scale research and enhances the clinical utility of CVR mapping.
