Abstract
Background Exhaustion of cerebrovascular reactivity (CVR) portends increased stroke risk. Acetazolamide-augmented blood oxygenation level-dependent (BOLD) MRI has been used to estimate CVR, but low signal-to-noise conditions relegate its use to terminal CVR (CVR(end)) measurements that neglect dynamic features of CVR. Purpose To demonstrate comprehensive characterization of acetazolamide-augmented BOLD MRI response in chronic steno-occlusive disease using a computational framework to precondition signal time courses for dynamic whole-brain CVR analysis. Materials and Methods This study focused on retrospective analysis of consecutive patients with unilateral chronic steno-occlusive disease who underwent acetazolamide-augmented BOLD imaging for recurrent minor stroke or transient ischemic attack at an academic medical center between May 2017 and October 2020. A custom principal component analysis-based denoising pipeline was used to correct spatially varying non-signal-bearing contributions obtained by a local principal component analysis of the MRI time series. Standard voxelwise CVR(end) maps representing terminal responses were produced and compared with maximal CVR (CVR(max)) as isolated from binned (per-repetition time) denoised BOLD time course. A linear mixed-effects model was used to compare CVR(max) and CVR(end) in healthy and diseased hemispheres. Results A total of 23 patients (median age, 51 years; IQR, 42-61, 13 men) who underwent 32 BOLD examinations were included. Processed MRI data showed twofold improvement in signal-to-noise ratio, allowing improved isolation of dynamic characteristics in signal time course for sliding window CVR(max) analysis to the level of each BOLD repetition time (approximately 2 seconds). Mean CVR(max) was significantly higher than mean CVR(end) in diseased (5.2% vs 3.8%, P < .01) and healthy (5.5% vs 4.0%, P < .01) hemispheres. Several distinct time-signal signatures were observed, including nonresponsive; delayed/blunted; brisk; and occasionally nonmonotonic time courses with paradoxical features in normal and abnormal tissues (ie, steal and reverse-steal patterns). Conclusion A principal component analysis-based computational framework for analysis of acetazolamide-augmented BOLD imaging can be used to measure unsustained CVR(max) through twofold improvements in signal-to-noise ratio. © RSNA, 2023 Supplemental material is available for this article.