Motivation: ASL MRI lacks a standardized pipeline for reliable functional connectivity analysis, particularly due to challenges in denoising and subtraction methods, limiting accuracy in brain network studies. Goal(s): To develop and optimize ASL MRI-specific denoising and subtraction methods that enhance functional connectivity mapping precision. Approach: We applied three subtraction methods (No Subtraction, Pairwise, Surround) and six denoising techniques (e.g., aCompCor, GSR) to resting-state ASL data from 63 participants, followed by dual regression and connectivity analysis. Results: Surround Subtraction combined with aCompCor demonstrated the highest accuracy in connectivity metrics (e.g., Dice Similarity Coefficient, Spatial Cross-Correlation), establishing a reliable ASL MRI pipeline for connectivity studies. Impact: This optimized ASL MRI pipeline enables more accurate functional connectivity analysis, benefiting neuroscience research and potential clinical applications by providing improved brain connectivity measurements for studying network abnormalities in neurodegenerative and psychiatric conditions.
Rahimzadeh et al. (Tue,) studied this question.