Digital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic resonance angiography

Abstract

Characterization of the complex branching architecture of cerebral arteries across a representative sample of the human population is important for diagnosing, analyzing, and predicting pathological states. Brain arterial vasculature can be visualized by magnetic resonance angiography (MRA). However, most MRA studies are limited to qualitative assessments, partial morphometric analyses, individual (or small numbers of) subjects, proprietary datasets, or combinations of the above limitations. Neuroinformatics tools, developed for neuronal arbor analysis, were used to quantify vascular morphology from 3 T time-of-flight MRA high-resolution (620 μm isotropic) images collected in 61 healthy volunteers (36/25 F/M, average age = 31.2 ± 10.7, range = 19–64 years). We present in-depth morphometric analyses of the global and local anatomical features of these arbors. The overall structure and size of the vasculature did not significantly differ across genders, ages, or hemispheres. The total length of the three major arterial trees stemming from the circle of Willis (from smallest to largest: the posterior, anterior, and middle cerebral arteries; or PCAs, ACAs, and MCAs, respectively) followed an approximate 1:2:4 proportion. Arterial size co-varied across individuals: subjects with one artery longer than average tended to have all other arteries also longer than average. There was no net right–left difference across the population in any of the individual arteries, but ACAs were more lateralized than MCAs. MCAs, ACAs, and PCAs had similar branch-level properties such as bifurcation angles. Throughout the arterial vasculature, there were considerable differences between branch types: bifurcating branches were significantly shorter and straighter than terminating branches. Furthermore, the length and meandering of bifurcating branches increased with age and with path distance from the circle of Willis. All reconstructions are freely distributed through a public database to enable additional analyses and modeling (cng.gmu.edu/brava).

Persistent homology analysis of brain artery trees

“…Hotz et al (2013), followed by Barden, Le and Owen (2013, 2014), investigated surprising nonstandard central limit theory in phylogenetic tree spaces. Finally, an analysis [Wright et al (2013)] of a different, and smaller, set of MRA brain artery images also found a connection between vessel length and healthy aging.…”

A computational framework to characterize and compare the geometry of coronary networks

“…Centerlines are widely accepted representations of arterial networks [31,36,37,[45][46][47][48], because they retain most of the geometric characteristics of arteries in a more compact and simpler structure. Three-dimensional centerlines are represented by polylines with point-wise information (e.g., lumen radius).…”

“…Such work also made use of the large deformation diffeomorphic metric curve mapping [52] to provide correspondences between centerlines and to define a metric in shape space. Wright et al [36] performed statistical analysis of morphometric variables (e.g., size, distances, angles, and branching structure) of the brain arterial vasculature; it was extended and complemented by Mut et al [37] by describing the sample-averaged shape and geographic distribution of the brain arterial trees based on a probabilistic vascular atlas.…”

“…Fractal dimension: [36] It is defined as the slope of the linear regression obtained from the log-log scatter plot of length versus euclidean distance, moving from the second to the last point along the centerline. Table II is produced for each arterial segment, based on clinical data.…”

Representação e quantificação de redes vasculares a partir de imagens de angiografia tridimensional

“…Também usamos duas imagens de angiografia reais, nas quais os centros de linha e os diâmetros foram obtidos por procedimentos manuais. Concernente aos testes de correspondência entre redes vasculares, usamos um dataset de 57 imagens, baseadas no projeto Brava [Wright et al, 2013].…”