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Medientyp:
E-Artikel
Titel:
Abstract 19280: Computational Fluid Dynamics Predicts Correlations between Transluminal Contrast and Pressure Gradients in Models of Stenosed Arteries
Beteiligte:
Lardo, Albert C;
Seo, Jung H;
Eslami, Parastou;
Mittal, Rajat
Beschreibung:
Background: Recent computed tomography coronary angiography (CTCA) studies have noted the presence of transluminal contrast gradients (TCG) in stenosed arteries but the mechanism and significance of TCG is unknown. Objective: Use computational fluid dynamics (CFD) modeling to determine mechanisms of contrast dispersion and correlate TCG to transstenotic pressure gradient (TPG) in models of stenosed coronary arteries. Methods: Simulations of flow and contrast dispersion in simple 2-dimensional models of coronary stenosis were carried out. Models with symmetric and asymmetric stenosis of varying severities (25%-75%) were employed. Key parameters including Reynolds and Womersley numbers were also varied. Simulations predict the variations of flow, pressure and contrast concentration in arterial models. Data from the simulations was analyzed to generate correlations between and contrast dispersion patterns and TPG with increasing stenosis severity. For extracting transluminal correlations, pressure and contrast concentration are averaged across the cross-section of the lumen and the contrast gradient is normalized by the maximum concentration at the inlet of the model. Results: Simulations predict the variations of flow, pressure and contrast concentration in arterial models (see figure). The data shows a direct but nonlinear correlation between TCG and TPG. A comparison of contrast dispersion in symmetric and one-sided stenoses also indicates that the correlation between TCG and TPG is sensitive to lesion geometry; the TCG for a symmetric lesion correlates to a higher TPG than a one-sided lesion. Conclusions: The current result is the first direct evidence that TCG may encode for vessel hemodynamics and that lesion geometry also modulates this relationship. CFD modeling provides data for determining important mechanistic insights into TCG and may be used to “decode” flow resistance from CTCA exams.