Improved reduced-order modelling of cerebrovascular flow distribution by accounting for arterial bifurcation pressure drops

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• 作者：C. Chnafa^a ; ^{cchnafa@mie.utoronto.ca} ; K. Valen-Sendstad^b ; O. Brina^c ; V.M. Pereira^c ; D.A. Steinman^a
• 关键词：0D model ; Circle of Willis ; Cerebral blood flow ; Boundary conditions ; Aneurysms
• 刊名：Journal of Biomechanics
• 出版年：2017
• 出版时间：25 January 2017
• 年：2017
• 卷：51
• 期：Complete
• 页码：83-88
• 全文大小：991 K
• 卷排序：51

文摘

Reduced-order modelling offers the possibility to study global flow features in cardiovascular networks. In order to validate these models, previous studies have been conducted in which they compared 3D computational fluid dynamics simulations with reduced-order simulations. Discrepancies have been reported between the two methods. The loss of energy at the bifurcations is usually neglected and has been pointed out as a possible explanation for these discrepancies. We present distributed lumped models of cerebrovasculatures created automatically from 70 cerebrovascular networks segmented from 3D angiograms. The outflow rate repartitions predicted with and without modelling the energy loss at the bifurcations are compared against 3D simulations. When neglecting the energy loss at the bifurcations, the flow rates though the anterior cerebral arteries are overestimated by 4.7±6.8% (error relative to the inlet flow rate, mean ± standard deviation), impacting the remaining volume of flow going to the other vessels. When the energy loss is modelled, this error is dropping to 0.1±3.2%. Overall, over the total of 337 outlet vessels, when the energy losses at the bifurcations are not modelled the 95% of agreement is in the range of ±13.5% and is down to ±6.5% when the energy losses are considered. With minimal input and computational resources, the presented method can estimate the outflow rates reliably. This study constitutes the largest validation of a reduced-order flow model against 3D simulations. The impact of the energy loss at the bifurcations is here demonstrated for cerebrovasculatures but can be applied to other physiological networks.