Computerized models of the cerebral vasculature are becoming increasingly used for analysis of medical images, patient treatment planning and cerebral haemodynamics simulations. Previously, we presented a method based on universal physical and physiological principles for the growth of realistic arterial trees in-silico using simulated annealing (SA). SA was found to be capable of reliably identifying the optimal arrangement of arteries to supply the heart. In this paper, we extract volumes representing the gray and white matter of the brain from Magnetic Resonance Imaging (MRI) data and automatically generate the optimal arterial tree using simulated annealing . The cerebral arteriesare arguably the most difficult to reproduce from first principles, featuring multiple substrates and interwoven territories , with a complicated layered topology. Comparison of our simulations with human angiography data confirms that vascular trees generated by our algorithm are a good match to real cerebral arteries .
The cerebral arteries are difficult to reproduce from first principles, featuring interwoven territories, and intricate layers of grey and white matter with differing metabolic demand. The aim of this study was to identify the ideal configuration of arteries required to sustain an entire brain hemisphere based on minimisation of the energy required to supply the tissue. The 3D distribution of grey and white matter within a healthy human brain was first segmented from Magnetic Resonance Images. A novel simulated annealing algorithm was then applied to determine the optimal configuration of arteries required to supply brain tissue. The model is validated through comparison of this ideal, entirely optimised, brain vasculature with the known structure of real arteries. This establishes that the human cerebral vasculature is highly optimised; closely resembling the most energy efficient arrangement of vessels. In addition to local adherence to fluid dynamics optimisation principles, the optimised vasculature reproduces global brain perfusion territories with well defined boundaries between anterior, middle and posterior regions. This validated brain vascular model and algorithm can be used for patient-specific modelling of stroke and cerebral haemodynamics, identification of sub-optimal conditions associated with vascular disease, and optimising vascular structures for tissue engineering and URLan design .
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Computerized models of the cerebral vasculature are becoming increasingly used for analysis of medical images, patient treatment planning and cerebral haemodynamics simulations. Previously, we presented a method based on universal physical and physiological principles for the growth of realistic arterial trees in-silico using simulated annealing (SA). SA was found to be capable of reliably identifying the optimal arrangement of arteries
The cerebral arteries are difficult to reproduce from first principles, featuring interwoven territories, and intricate layers of grey and white matter with differing metabolic demand. The aim of this study was
R
supply the heart. In this paper, we extract volumes representing the gray
identify the ideal configuration of arteries required to sustain an entire brain hemisphere based on minimisation of the energy required to supply the tissue. The 3D distribution of grey
R
of the brain
within a healthy human brain was first segmented
R
Imaging (MRI) data and automatically generate the optimal arterial tree using simulated annealing . The cerebral arteriesare arguably the most difficult to reproduce from first principles, featuring multiple substrates and interwoven territories , with a complicated layered topology. Comparison of our simulations with human angiography data confirms that vascular trees generated by our algorithm are a good match to real cerebral arteries
Images. A novel simulated annealing algorithm was then applied to determine the optimal configuration of arteries required to supply brain tissue. The model is validated through comparison of this ideal, entirely optimised, brain vasculature with the known structure of real arteries. This establishes that the human cerebral vasculature is highly optimised; closely resembling the most energy efficient arrangement of vessels. In addition to local adherence to fluid dynamics optimisation principles, the optimised vasculature reproduces global brain perfusion territories with well defined boundaries between anterior, middle and posterior regions. This validated brain vascular model and algorithm can be used for patient-specific modelling of stroke and cerebral haemodynamics, identification of sub-optimal conditions associated with vascular disease, and optimising vascular structures for tissue engineering and URLan design