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

Bulant, Carlos A.; Blanco, Pablo; Talou, Gonzalo D. Maso; Bezerra, Cristiano Guedes; Lemos, Pedro A.; Feijóo, Raúl A.

doi:10.1016/j.jbiomech.2016.11.070

Cited by 29 publications

(30 citation statements)

References 37 publications

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“…Blood flow is modeled as a Newtonian fluid, therefore, the Navier-Stokes equations for incompressible flows hold where v and p are the velocity and pressure fields, (⋅) s denotes the symmetrization operation, and ρ and μ are the fluid density and dynamic viscosity, respectively. The 3D finite element strategy used to find approximate solutions to this model is described in 39 .…”

Section: Methodsmentioning

confidence: 99%

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

Blanco

Bulant

Muller

et al. 2018

Sci Rep

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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.

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Section: Methodsmentioning

confidence: 99%

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

Blanco

Bulant

Muller

et al. 2018

Sci Rep

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“…A processing methodology was employed to generate a three‐dimensional model of the arterial lumen of the interrogated vessel (hereafter simply referred as mesh) . In brief, for each study, gating of IVUS images was performed to retrieve a end‐diastolic phase from the IVUS dataset .…”

Section: Methodsmentioning

confidence: 99%

“…Finally, the computational mesh is obtained using standard mesh operations and condenses the geometric characteristics of the vessel with high detail . Figure illustrates the three‐dimensional IVUS processing pipeline.…”

Section: Methodsmentioning

confidence: 99%

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Coronary fractional flow reserve derived from intravascular ultrasound imaging: Validation of a new computational method of fusion between anatomy and physiology

Bezerra

Hideo‐Kajita

Bulant

et al. 2018

Cathet Cardio Intervent

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Objectives: To evaluate the diagnostic performance of a novel computational algorithm based on three-dimensional intravascular ultrasound (IVUS) imaging in estimating fractional flow reserve (IVUS FR ), compared to gold-standard invasive measurements (FFR INVAS ).Background: IVUS provides accurate anatomical evaluation of the lumen and vessel wall and has been validated as a useful tool to guide percutaneous coronary intervention. However, IVUS poorly represents the functional status (i.e., flow-related information) of the imaged vessel.Methods: Patients with known or suspected stable coronary disease scheduled for elective cardiac catheterization underwent FFR INVAS measurement and IVUS imaging in the same procedure to evaluate intermediate lesions. A processing methodology was applied on IVUS to generate a computational mesh condensing the geometric characteristics of the vessel. Computation of IVUS FR was obtained from patient-level morphological definition of arterial districts and from territory-specific boundary conditions. FFR INVAS measurements were dichotomized at the 0.80 threshold to define hemodynamically significant lesions.Results: A total of 24 patients with 34 vessels were analyzed. IVUS FR significantly correlated (r = 0.79; P < 0.001) and showed good agreement with FFR INVAS , with a mean difference of −0.008 AE 0.067 (P = 0.47). IVUS FR presented an overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 91%, 89%, 92%, 80%, and 96%, respectively, to detect significant stenosis. Conclusion:The computational processing of IVUS FR is a new method that allows the evaluation of the functional significance of coronary stenosis in an accurate way, enriching the anatomical information of grayscale IVUS. K E Y W O R D Scomputational fluid dynamics, coronary blood flow/physiology/microvascular function, coronary artery disease, fractional flow reserve, imaging intravascular ultrasound, interventional devices/innovation, quantitative coronary angiography, three-dimensional coronary models

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“…In order to accurately simulate patient specific conditions, three kinds of input data are required: (i) patient-specific anatomical models of the vasculature, (ii) the loads to which the anatomical structures are subjected to, and (iii) the patient-specific distribution of the arterial-wall constituents and their corresponding material parameters. As far as anatomical data of the arteries is concerned, it can be straighforwardly extracted from different medical imaging modalities (Wahle et al, 1995 ; Milner et al, 1998 ; Bulant et al, 2017 ). Regarding the force exerted by the blood pressure, it can be accurately estimated from cuff-pressure measurements (O'brien et al, 2001 ; Miyashita, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Mechanical Characterization of the Vessel Wall by Data Assimilation of Intravascular Ultrasound Studies

et al. 2018

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Atherosclerotic plaque rupture and erosion are the most important mechanisms underlying the sudden plaque growth, responsible for acute coronary syndromes and even fatal cardiac events. Advances in the understanding of the culprit plaque structure and composition are already reported in the literature, however, there is still much work to be done toward in-vivo plaque visualization and mechanical characterization to assess plaque stability, patient risk, diagnosis and treatment prognosis. In this work, a methodology for the mechanical characterization of the vessel wall plaque and tissues is proposed based on the combination of intravascular ultrasound (IVUS) imaging processing, data assimilation and continuum mechanics models within a high performance computing (HPC) environment. Initially, the IVUS study is gated to obtain volumes of image sequences corresponding to the vessel of interest at different cardiac phases. These sequences are registered against the sequence of the end-diastolic phase to remove transversal and longitudinal rigid motions prescribed by the moving environment due to the heartbeat. Then, optical flow between the image sequences is computed to obtain the displacement fields of the vessel (each associated to a certain pressure level). The obtained displacement fields are regarded as observations within a data assimilation paradigm, which aims to estimate the material parameters of the tissues within the vessel wall. Specifically, a reduced order unscented Kalman filter is employed, endowed with a forward operator which amounts to address the solution of a hyperelastic solid mechanics model in the finite strain regime taking into account the axially stretched state of the vessel, as well as the effect of internal and external forces acting on the arterial wall. Due to the computational burden, a HPC approach is mandatory. Hence, the data assimilation and computational solid mechanics computations are parallelized at three levels: (i) a Kalman filter level; (ii) a cardiac phase level; and (iii) a mesh partitioning level. To illustrate the capabilities of this novel methodology toward the in-vivo analysis of patient-specific vessel constituents, mechanical material parameters are estimated using in-silico and in-vivo data retrieved from IVUS studies. Limitations and potentials of this approach are exposed and discussed.

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

Cited by 29 publications

References 37 publications

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

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

Coronary fractional flow reserve derived from intravascular ultrasound imaging: Validation of a new computational method of fusion between anatomy and physiology

Mechanical Characterization of the Vessel Wall by Data Assimilation of Intravascular Ultrasound Studies

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