Feasibility of coronary blood flow simulations using mid-fidelity numeric and geometric models.

The fractional flow reserve index (FFR) is currently used as a gold standard to quantify coronary stenosis's functional relevance. Due to its highly invasive nature, the development of noninvasive surrogates based on simulations has drawn much attention in recent years, emphasizing efficient strategies that enable translational research. The focus of this work is twofold. First, to assess the feasibility of using a mid-fidelity numerical strategy (transversally enriched pipe element method, TEPEM), placed between low- and high-fidelity models, for the estimation of flow-related quantities, such as FFR and wall shear stress (WSS). Low-fidelity models, as zero- or one-dimensional models, are computationally inexpensive but in detriment of poorer spatially detailed predictions. On the other hand, high-fidelity models, such as classical three-dimensional numerical approximations, can provide detailed predictions but their transition to clinical application is prohibitive due to high computational costs. As a second goal, we quantify the impact of the length of lateral branches in the blood flow through the interrogated vessel of interest to further reduce the computational burden. Both studies are addressed considering a cohort of 17 coronary geometries. A total of 20 locations were selected to estimate the FFR index for a wide range of Coronary Flow Reserve (CFR) scenarios. Numerical results suggest that the mid-fidelity TEPEM model is a reliable approach for the efficient estimation of the FFR index and WSS, with an error in the order of [Formula: see text] and [Formula: see text], respectively, when compared to the high-fidelity prediction. Moreover, such mid-fidelity models require much less computational resources, in compliance with infrastructure frequently available in the clinic, by achieving a speedup between 30 and 60 times compared to a conventional finite element approach. Also, we show that shortening peripheral branches does not introduce considerable perturbations either in the flow patterns, in the wall shear stress, or the pressure drop. Comparing the different geometric models, the error in the estimation of FFR index and WSS is reduced to less than [Formula: see text] and [Formula: see text], respectively.

Author Correction: Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve.

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Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve.

In this work we propose to validate the predictive capabilities of one-dimensional (1D) blood flow models with full three-dimensional (3D) models in the context of patient-specific coronary hemodynamics in hyperemic conditions. Such conditions mimic the state of coronary circulation during the acquisition of the Fractional Flow Reserve (FFR) index. Demonstrating that 1D models accurately reproduce FFR estimates obtained with 3D models has implications in the approach to computationally estimate FFR. To this end, a sample of 20 patients was employed from which 29 3D geometries of arterial trees were constructed, 9 obtained from coronary computed tomography angiography (CCTA) and 20 from intra-vascular ultrasound (IVUS). For each 3D arterial model, a 1D counterpart was generated. The same outflow and inlet pressure boundary conditions were applied to both (3D and 1D) models. In the 1D setting, pressure losses at stenoses and bifurcations were accounted for through specific lumped models. Comparisons between 1D models (FFR1D) and 3D models (FFR3D) were performed in terms of predicted FFR value. Compared to FFR3D, FFR1D resulted with a difference of 0.00 ± 0.03 and overall predictive capability AUC, Acc, Spe, Sen, PPV and NPV of 0.97, 0.98, 0.90, 0.99, 0.82, and 0.99, with an FFR threshold of 0.8. We conclude that inexpensive FFR1D simulations can be reliably used as a surrogate of demanding FFR3D computations.

A head-to-head comparison between CT- and IVUS-derived coronary blood flow models.

The goal of this work is to compare coronary hemodynamics as predicted by computational blood flow models derived from two imaging modalities: coronary computed tomography angiography (CCTA) and intravascular ultrasound integrated with angiography (IVUS). Criteria to define boundary conditions are proposed to overcome the dissimilar anatomical definition delivered by both modalities. The strategy to define boundary conditions is novel in the present context, and naturally accounts for the flow redistribution induced by the resistance of coronary vessels. Hyperemic conditions are assumed to assess model predictions under stressed hemodynamic environments similar to those encountered in Fractional Flow Reserve (FFR) calculations. As results, it was found that CCTA models predict larger pressure drops, higher average blood velocity and smaller FFR. Concerning the flow rate at distal locations in the major vessels of interest, it was found that CCTA predicted smaller flow than IVUS, which is a consequence of a larger sensitivity of CCTA models to coronary steal phenomena. Comparisons to in-vivo measurements of FFR are shown.

A computational framework to characterize and compare the geometry of coronary networks.

This work presents a computational framework to perform a systematic and comprehensive assessment of the morphometry of coronary arteries from in vivo medical images. The methodology embraces image segmentation, arterial vessel representation, characterization and comparison, data storage, and finally analysis. Validation is performed using a sample of 48 patients. Data mining of morphometric information of several coronary arteries is presented. Results agree to medical reports in terms of basic geometric and anatomical variables. Concerning geometric descriptors, inter-artery and intra-artery correlations are studied. Data reported here can be useful for the construction and setup of blood flow models of the coronary circulation. Finally, as an application example, similarity criterion to assess vasculature likelihood based on geometric features is presented and used to test geometric similarity among sibling patients. Results indicate that likelihood, measured through geometric descriptors, is stronger between siblings compared with non-relative patients. Copyright © 2016 John Wiley & Sons, Ltd.