Background and Purpose: Altered brain vasculature is a key phenomenon in several neurologic disorders. This paper presents a quantitative assessment of the anatomical variations in the Circle of Willis (CoW) and vascular morphology in healthy aging, acute ischemic stroke (AIS) and Alzheimer's Disease (AD). Methods:We used our novel automatic method to segment and extract geometric features of the cerebral vasculature from MR angiography scans of 175 healthy subjects, which were used to create a probabilistic atlas of cerebrovasculature and to study normal aging and intersubject variations in CoW anatomy. Subsequently, we quantified and analyzed vascular alterations in 45AIS and 50 AD patients, two prominent cerebrovascular and neurodegenerative disorders. Results:In the sampled cohort, we determined that the CoW is fully formed in only 35% of healthy adults and found significantly (p < .05) increased tortuosity and fractality, with increasing age and also with disease in both AIS and AD. We also found significantly lower vessel length, volume, and number of branches in AIS patients, as expected. The AD cerebral vessels exhibited significantly smaller diameter and more complex branching patterns, compared to age-matched healthy adults. These changes were significantly heightened (p < .05) among healthy, early onset mild AD, and moderate/severe dementia groups. Conclusion:Although our study does not include longitudinal data due to paucity of such datasets, the specific geometric features and quantitative comparisons demonstrate the potential for using vascular morphology as a noninvasive imaging biomarker for neurologic disorders.

Introduction: Pulmonary vascular distensibility associates with right ventricular function and clinical outcomes in patients with unexplained dyspnea and pulmonary hypertension. Alpha distensibility coefficient is determined from a non-linear fit to multipoint pressure-flow plots. The study aims were 1) to create and test a user-friendly tool to standardize analysis of exercise hemodynamics including distensibility and 2) to investigate changes in distensibility following treatment in patients with pulmonary arterial hypertension. Methods: Participants with repeat exercise right heart catherization and PAH were identified from the University of Arizona PH Registry (n=29). Single-beat analysis was used to quantify right ventricular function including the coupling ratio and diastolic stiffness. Prototypes of the iCPET calculator were developed using Matlab, Python and RShiny to analyze exercise hemodynamics and alpha distensibility coefficient, α (%/mmHg) from multi-point pressure flow plots. Interclass coefficients were calculated for inter-platform and interobserver variability in alpha. Results: No significant bias in the intra-platform (Matlab vs RShiny: ICC: 0.996) or inter-observer (ICC: 0.982) comparison of alpha values. Participants with PAH had a significant decrease in afterload at follow-up (p<0.05) but no significant change in alpha distensibility. At follow-up, participants with a resting mean PA pressure < 25 mmHg had no change in pressure, resistance or alpha distensibility. Alpha distensibility significantly correlated with PA compliance at both the index and follow-up visit. Discussion: The iCPET calculator standardizes alpha distensibility calculations. In this retrospective cohort, alpha distensibility did not change despite a decrease in pulmonary vascular afterload (PVR and mPAP) at follow-up after treatment with pulmonary vasodilators.

BackgroundRapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. Response to acute reperfusion therapies varies significantly based on patient-specific cerebrovascular anatomy that governs cerebral blood flow. We present an end-to-end machine learning approach for automatic stroke triage.MethodsEmploying a validated convolutional neural network (CNN) segmentation model for image processing, we extract each patient’s cerebrovasculature and its morphological features from baseline non-invasive angiography scans. These features are used to detect occlusion’s presence and the site automatically, and for the first time, to estimate collateral circulation without manual intervention. We then use the extracted cerebrovascular features along with commonly used clinical and imaging parameters to predict the 90-day functional outcome for each patient.ResultsThe CNN model achieved a segmentation accuracy of 94%. The automatic stroke detection algorithm had a sensitivity and specificity of 92% and 94%, respectively. The models for occlusion site detection and automatic collateral grading reached 96% and 87.2% accuracy, respectively. Incorporating the automatically extracted cerebrovascular features significantly improved the 90-day outcome prediction accuracy from 0.63 to 0.83.ConclusionsThe fast, automatic, and comprehensive model presented here can improve stroke diagnosis, aid collateral assessment, and enhance prognostication for treatment decisions, using cerebrovascular morphology.

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