20 March 2015 Evaluating MRI based vascular wall motion as a biomarker of Fontan hemodynamic performance
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Abstract
The Fontan procedure for single-ventricle heart disease involves creation of pathways to divert venous blood from the superior & inferior venacavae (SVC, IVC) directly into the pulmonary arteries (PA), bypassing the right ventricle. For optimal surgical outcomes, venous flow energy loss in the resulting vascular construction must be minimized and ensuring close to equal flow distribution from the Fontan conduit connecting IVC to the left & right PA is paramount. This requires patient-specific hemodynamic evaluation using computational fluid dynamics (CFD) simulations which are often time and resource intensive, limiting applicability for real-time patient management in the clinic. In this study, we report preliminary efforts at identifying a new non-invasive imaging based surrogate for CFD simulated hemodynamics. We establish correlations between computed hemodynamic criteria from CFD modeling and cumulative wall displacement characteristics of the Fontan conduit quantified from cine cardiovascular MRI segmentations over time (i.e. 20 cardiac phases gated from the start of ventricular systole), in 5 unique Fontan surgical connections. To focus our attention on diameter variations while discounting side-to-side swaying motion of the Fontan conduit, the difference between its instantaneous regional expansion and inward contraction (averaged across the conduit) was computed and analyzed. Maximum Fontan conduit-average expansion over the cardiac cycle correlated with the anatomy-specific diametric offset between the axis of the IVC and SVC (r2=0.13, p=0.55) – a known factor correlated with Fontan energy loss and IVC-to-PA flow distribution. Investigation in a larger study cohort is needed to establish stronger statistical correlations.
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Prahlad G. Menon, Prahlad G. Menon, Haifa Hong, Haifa Hong, } "Evaluating MRI based vascular wall motion as a biomarker of Fontan hemodynamic performance", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132Q (20 March 2015); doi: 10.1117/12.2080825; https://doi.org/10.1117/12.2080825
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