A vessel length-based method to compute coronary fractional flow reserve from optical coherence tomography images

Abstract

BackgroundHemodynamic simulation for quantifying fractional flow reserve (FFR) is often performed in a patient-specific geometry of coronary arteries reconstructed from the images from various imaging modalities. Because optical coherence tomography (OCT) images can provide more precise vascular lumen geometry, regardless of stenotic severity, hemodynamic simulation based on OCT images may be effective. The aim of this study is to perform OCT–FFR simulations by coupling a 3D CFD model from geometrically correct OCT images with a LPM based on vessel lengths extracted from CAG data with clinical validations for the present method.MethodsTo simulate coronary hemodynamics, we developed a fast and accurate method that combined a computational fluid dynamics (CFD) model of an OCT-based region of interest (ROI) with a lumped parameter model (LPM) of the coronary microvasculature and veins. Here, the LPM was based on vessel lengths extracted from coronary X-ray angiography (CAG) images. Based on a vessel length-based approach, we describe a theoretical formulation for the total resistance of the LPM from a three-dimensional (3D) CFD model of the ROI.ResultsTo show the utility of this method, we present calculated examples of FFR from OCT images. To validate the OCT-based FFR calculation (OCT–FFR) clinically, we compared the computed OCT–FFR values for 17 vessels of 13 patients with clinically measured FFR (M-FFR) values.ConclusionA novel formulation for the total resistance of LPM is introduced to accurately simulate a 3D CFD model of the ROI. The simulated FFR values compared well with clinically measured ones, showing the accuracy of the method. Moreover, the present method is fast in terms of computational time, enabling clinicians to provide solutions handled within the hospital.

Simulation of the Perfusion of Contrast Agent Used in Cardiac Magnetic Resonance: A Step Toward Non-invasive Cardiac Perfusion Quantification

“…The values for the prescribed pressures in the non-ischemic case are taken from Chapelle et al ( 2010 ). For the diseased simulations, the values for the drop of pressure in a location of the epicardium (that simulates a coronary artery) were taken from works that study the FFR technique (Pijls et al, 2000 ; Sant'Anna et al, 2007 ; Lee et al, 2017 ). Here we present the features of each of these scenarios (see Figure 5 ): 1st (normal): p ( x, y ) = 0.0kPa, ∀( x, y ) ∈ Γ i , and p ( x, y ) = 2.0kPa, ∀( x, y ) ∈ Γ o , see Figure 5A ; 2nd (ischemia): In a small region of the right-hand side of Γ o pressure is reduced (to simulate a 30% stenosis), p ( x, y ) = 1.4kPa.…”

“…Another invasive exam that indirectly evaluates perfusion is the Fractional Flow Reserve (FFR) (Lee et al, 2017 ), which gives information related to the severity of a coronary stenosis. It is important to mention that there are softwares that can estimate FFR by combining mathematical models (also PDEs) and images acquired by CT exams.…”

Planning percutaneous coronary interventions using computed tomography angiography and fractional flow reserve‐derived from computed tomography: A state‐of‐the‐art review

“…In recent years, coronary CTA has been used to provide precise anatomic information regarding the presence, extent and composition of coronary artery disease (CAD) , but has similar limitations to ICA regarding the lack of physiological information . There are numerous invasive and noninvasive techniques for measuring FFR, but among the noninvasive techniques, FFR‐derived by CTA (FFRCT) (HeartFlow, Inc., Redwood City, California) is the only method approved by the Food and Drug Administration in the United States and by the Conformité Européene in Europe. FFRCT is an accurate method for determining the hemodynamic significance of CAD compared with FFRINV .…”

Diagnostic performance of deep learning and computational fluid dynamics-based instantaneous wave-free ratio derived from computed tomography angiography

“…Time efficiency is a key factor in diagnosing patients with CAD, and shortening diagnosis time is of great clinically significance. The optimal cut-off values for OCT-FFR and IVUS-FFR were 0.80 [ 30 , 31 ]. As a result, we used AccuiFRct ≤ 0.89 as the optimal threshold for predicting ischemic lesions.…”