A Data-Driven Normalization Framework for Subject-Specific Cerebrovascular Reactivity Assessment in Cerebrovascular Disease.

backgroundAssessment of cerebrovascular reactivity (CVR) has been reported using acetazolamide-augmented blood oxygenation level-dependent (BOLD) MRI; however, wide intersubject baseline variability can complicate interpretation. PURPOSE: To develop an integrated normalization framework combining machine learning-guided identification of healthy voxels for subject-specific baseline rescaling with atlas-based assessment. STUDY TYPE: Retrospective. POPULATION: 26 subjects (age 51 ± 13 at first scan, 50% women, 34 exams) with unilateral steno-occlusive disease (SOD) and 9 subjects with bilateral disease (age 55 ± 12, 4/9 women) underwent acetazolamide-augmented BOLD-MRI. FIELD STRENGTH/SEQUENCE: T1-weighted MPRAGE (1 mm isotropic) and gradient-echo EPI at 3 T. ASSESSMENT: Machine learning models (random forest, XGBoost, LightGBM, and neural networks) were trained to identify healthy candidate voxels using a previously reported pipeline based on baseline resting-state BOLD temporal shift features derived from 32 arterial territories. A healthy reference CVR atlas was constructed from unaffected hemispheres. Individual CVR maps were rescaled using predicted healthy voxels and converted to Z-scores and risk index maps using the reference atlas. STATISTICAL TESTS: Coefficient of variation (CV) within normal hemispheres, lesion contrast-to-noise ratio (CNR), Lesion ROC AUC, asymmetry index (AI) in unilateral patients, and chi-square distance between thresholded normal and abnormal CVR distributions across all patients were compared between atlas-based group normalization alone and the proposed integrated normalization using paired t-tests. (cutoff p = 0.05).

resultsCV within normal hemispheres significantly decreased (1.07 ± 0.15 vs. 0.45 ± 0.09), indicating improved inter-subject stability. Lesion CNR and ROC AUC improved across all impairment thresholds (CNR: 0.73-1.12 vs. 0.79-1.19; AUC: 0.76-0.89 vs. 0.81-0.90). AI increased significantly at higher risk thresholds (≥ 0.78), with no significant difference at the lowest threshold (p = 0.181). Chi-square analysis demonstrated significantly increased separation between CVR distributions at intermediate thresholds. DATA

conclusionThe proposed integrated normalization improves the stability, discriminability, and interpretability of acetazolamide-augmented BOLD-CVR for detection of cerebrovascular impairment. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.