A novel MRA-based framework for the detection of changes in cerebrovascular blood pressure.

Background: High blood pressure (HBP) affects 75 million adults and is the primary or contributing cause of mortality in 410,000 adults each year in the United States. Chronic HBP leads to cerebrovascular changes and is a significant contributor for strokes, dementia, and cognitive impairment. Non-invasive measurement of changes in cerebral vasculature and blood pressure (BP) may enable physicians to optimally treat HBP patients. This manuscript describes a method to non-invasively quantify changes in cerebral vasculature and BP using Magnetic Resonance Angiography (MRA) imaging. Methods: MRA images and BP measurements were obtained from patients (n=15, M=8, F=7, Age= 49.2 ± 7.3 years) over a span of 700 days. A novel segmentation algorithm was developed to identify brain vasculature from surrounding tissue. The data was processed to calculate the vascular probability distribution function (PDF); a measure of the vascular diameters in the brain. The initial (day 0) PDF and final (day 700) PDF were used to correlate the changes in cerebral vasculature and BP. Correlation was determined by a mixed effects linear model analysis. Results: The segmentation algorithm had a 99.9% specificity and 99.7% sensitivity in identifying and delineating cerebral vasculature. The PDFs had a statistically significant correlation to BP changes below the circle of Willis (p-value = 0.0007), but not significant (p-value = 0.53) above the circle of Willis, due to smaller blood vessels. Conclusion: Changes in cerebral vasculature and pressure can be non-invasively obtained through MRA image analysis, which may be a useful tool for clinicians to optimize medical management of HBP

Minimum average-cost path for real time 3d coronary artery segmentation of ct images

Abstract. In this paper, we propose a Minimum Average-cost Path (MACP) model for segmenting 3D coronary arteries by minimizing the average edge cost along path in discrete 4D graph constructed by image voxels and associated radii. Prim’s Minimum Spanning Tree method is used for efficient optimization of the MACP model. The centerline and the radii of the cross sections of the coronary artery are extracted simultaneously during the optimization. The method does not need any image preprocessing steps and has been intensively validated as an effective approach with the Rotterdam Coronary Artery Algorithm Evaluation Framework [1]. The computational cost of the proposed method is particularly low (7.467 seconds per segment, 18.5mm/s on average), which makes real time segmentation of coronary artery possible. Shortcut problem, which is a classic issue of the minimal path techniques, can also be overcome by the proposed method.