Wahbi K El-Bouri1, Stephen J Payne2. 1. Department of Engineering, Institute of Biomedical Engineering, University of Oxford, Oxford, UK. wahbi.el-bouri@eng.ox.ac.uk. 2. Department of Engineering, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
Abstract
OBJECTIVE: Models of the cerebral microvasculature are required at many different scales in order to understand the effects of microvascular topology on CBF. There are, however, no data-driven models at the arteriolar/venular scale. In this paper, we develop a data-driven algorithm based on available data to generate statistically accurate penetrating arterioles and venules. METHODS: A novel order-based density-filling algorithm is developed based on the statistical data including bifurcating angles, LDRs, and area ratios. Three thousand simulations are presented, and the results validated against morphological data. These are combined with a previous capillary network in order to calculate full vascular network parameters. RESULTS: Statistically accurate penetrating trees were successfully generated. All properties provided a good fit to experimental data. The k exponent had a median of 2.5 and an interquartile range of 1.75-3.7. CBF showed a standard deviation ranging from ±18% to ±34% of the mean, depending on the penetrating vessel diameter. CONCLUSIONS: Small CBF variations indicate that the topology of the penetrating vessels plays only a small part in the large regional variations of CBF seen in the brain. These results open up the possibility of efficient oxygen and blood flow simulations at MRI voxel scales which can be directly validated against MRI data.
OBJECTIVE: Models of the cerebral microvasculature are required at many different scales in order to understand the effects of microvascular topology on CBF. There are, however, no data-driven models at the arteriolar/venular scale. In this paper, we develop a data-driven algorithm based on available data to generate statistically accurate penetrating arterioles and venules. METHODS: A novel order-based density-filling algorithm is developed based on the statistical data including bifurcating angles, LDRs, and area ratios. Three thousand simulations are presented, and the results validated against morphological data. These are combined with a previous capillary network in order to calculate full vascular network parameters. RESULTS: Statistically accurate penetrating trees were successfully generated. All properties provided a good fit to experimental data. The k exponent had a median of 2.5 and an interquartile range of 1.75-3.7. CBF showed a standard deviation ranging from ±18% to ±34% of the mean, depending on the penetrating vessel diameter. CONCLUSIONS: Small CBF variations indicate that the topology of the penetrating vessels plays only a small part in the large regional variations of CBF seen in the brain. These results open up the possibility of efficient oxygen and blood flow simulations at MRI voxel scales which can be directly validated against MRI data.
Authors: Maiko T Uemura; Takakuni Maki; Masafumi Ihara; Virginia M Y Lee; John Q Trojanowski Journal: Front Aging Neurosci Date: 2020-04-14 Impact factor: 5.750
Authors: Wahbi K El-Bouri; Andrew MacGowan; Tamás I Józsa; Matthew J Gounis; Stephen J Payne Journal: PLoS Comput Biol Date: 2021-03-12 Impact factor: 4.475
Authors: Yidan Xue; Theodosia Georgakopoulou; Anne-Eva van der Wijk; Tamás I Józsa; Ed van Bavel; Stephen J Payne Journal: PLoS Comput Biol Date: 2022-08-05 Impact factor: 4.779