Using Data-Driven Methods to Improve Brain Blood Flow Measurements in Cerebrovascular Disease with Dynamic Imaging.

Background and purpose: Cerebrovascular reactivity (CVR) is a widely studied biomarker of cerebral hemodynamics, commonly used in risk stratification and treatment planning in patients with steno-occlusive disease (SOD). Conventional use relies on normalization of estimates to contralateral hemisphere reference values, which is unsuitable for bilateral or indeterminate distributions of disease. We report upon a custom data-driven approach leveraging random forest classifiers (RFc) to identify candidate voxels for normalization in order to facilitate interrogation outside conditions of known unilateral SOD MATERIALS AND METHODS: We retrospectively analyzed 16 patients with unilateral SOD who underwent acetazolamide-augmented BOLD-MRI and DSC perfusion. Three RFc models were trained using leave-one-out cross-validation (LOOCV) to identify candidate voxels brain-wide whose CVR were within 10% of the normal hemispheric median: i. all voxels; ii. gray matter only; and iii. white matter only. Model input features included time-to-maximum (Tmax), mean transit time (MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV) from contemporaneous DSC. The median model-predicted reference CVR (CVRref) was compared to ground-truth medians in LOOCV, and its impact on threshold-based volumetric classification of CVR reduction assessed.

Results: RFc models effectively predicted ground-truth CVR voxels, achieving median absolute percent differences of 12.8% (IQR: 5.0%-18.9%) using all voxels, 11.3% (IQR: 9.3%-16.1%) for gray matter, and 9.8% (IQR: 4.4%-16.9%) for white matter. Volumetric estimates of CVR reduction across thresholds for the models revealed excellent agreement between ground-truth and model estimates without statistically significant differences (p>0.01), excepting lowest white matter CVR thresholds. Model use in a small pilot deployment of bilateral SOD cases demonstrated the potential utility, enabling voxel-wise CVR assessment without reliance on contralateral reference.

Conclusions: We present a novel data-driven approach for normalizing CVR maps in patients with bilateral or indeterminate SOD. Using an RFc, our method provides an individualized, brain-wide reference CVR, expanding the utility of CVR estimates beyond the typical constraints of unilateral disease, and with potential application to other, similarly constrained scenarios such as for SPECT or PET hemodynamic studies.

Abbreviations: CVR = cerebrovascular reactivity; RFc = random forest classifier; SOD = steno-occlusive disease.