T W Peach1, Y Ventikos2, J V Byrne3, Z You4. 1. From the Department of Mechanical Engineering (T.W.P., Y.V.), University College London, London, UK. 2. From the Department of Mechanical Engineering (T.W.P., Y.V.), University College London, London, UK y.ventikos@ucl.ac.uk. 3. Department of Neuroradiology (J.V.B.), John Radcliffe Hospital, Oxford, UK. 4. Department of Engineering Science (Z.Y.), University of Oxford, Oxford, UK.
Abstract
BACKGROUND AND PURPOSE: Accurately and efficiently modeling the transport of angiographic contrast currently offers the best method of verifying computational fluid dynamics simulations and, with it, progress toward the lofty goal of prediction of aneurysm treatment outcome a priori. This study specifically examines the influence of estimated flow rate and contrast properties on such in silico predictions of aneurysm contrast residence and decay. MATERIALS AND METHODS: Four experimental sidewall aneurysms were created in swine, with aneurysm contrast flow patterns and decay rates observed under angiography. A simplified computational fluid dynamics model of the experimental aneurysm was constructed from 3D angiography and contrast residence predicted a priori. The relative influence of a number of estimated model parameters (contrast viscosity, contrast density, and blood flow rate) on contrast residence was then investigated with further simulations. RESULTS: Contrast infiltration and washout pattern were accurately predicted by the a priori computational fluid dynamics model; however, the contrast decay rate was underestimated by ∼25%. This error was attributed to the estimated parent vessel flow rate alone, and the effects of contrast viscosity and density on the decay rate were found to be inconsequential. A linear correlation between the parent vessel flow rate and the corresponding contrast decay rate was observed. CONCLUSIONS: In experimental sidewall aneurysms, contrast fluid properties (viscosity and density) were shown to have a negligible effect on variation in the modeled contrast decay rate. A strong linear correlation was observed between parent vessel flow rate and contrast decay over a physiologically reasonable range of flow rates.
BACKGROUND AND PURPOSE: Accurately and efficiently modeling the transport of angiographic contrast currently offers the best method of verifying computational fluid dynamics simulations and, with it, progress toward the lofty goal of prediction of aneurysm treatment outcome a priori. This study specifically examines the influence of estimated flow rate and contrast properties on such in silico predictions of aneurysm contrast residence and decay. MATERIALS AND METHODS: Four experimental sidewall aneurysms were created in swine, with aneurysm contrast flow patterns and decay rates observed under angiography. A simplified computational fluid dynamics model of the experimental aneurysm was constructed from 3D angiography and contrast residence predicted a priori. The relative influence of a number of estimated model parameters (contrast viscosity, contrast density, and blood flow rate) on contrast residence was then investigated with further simulations. RESULTS: Contrast infiltration and washout pattern were accurately predicted by the a priori computational fluid dynamics model; however, the contrast decay rate was underestimated by ∼25%. This error was attributed to the estimated parent vessel flow rate alone, and the effects of contrast viscosity and density on the decay rate were found to be inconsequential. A linear correlation between the parent vessel flow rate and the corresponding contrast decay rate was observed. CONCLUSIONS: In experimental sidewall aneurysms, contrast fluid properties (viscosity and density) were shown to have a negligible effect on variation in the modeled contrast decay rate. A strong linear correlation was observed between parent vessel flow rate and contrast decay over a physiologically reasonable range of flow rates.
Authors: Matthew D Ford; Gordan R Stuhne; Hristo N Nikolov; Damiaan F Habets; Stephen P Lownie; David W Holdsworth; David A Steinman Journal: IEEE Trans Med Imaging Date: 2005-12 Impact factor: 10.048
Authors: David A Steinman; Jaques S Milner; Chris J Norley; Stephen P Lownie; David W Holdsworth Journal: AJNR Am J Neuroradiol Date: 2003-04 Impact factor: 3.825
Authors: Juan R Cebral; Mariano Vazquez; Daniel M Sforza; Guillaume Houzeaux; Satoshi Tateshima; Esteban Scrivano; Carlos Bleise; Pedro Lylyk; Christopher M Putman Journal: J Neurointerv Surg Date: 2014-05-14 Impact factor: 5.836