Fast CT-FFR Analysis Method for the Coronary Artery Based on 4D-CT Image Analysis and Structural and Fluid Analysis

Abstract

Non invasive fractional flow reserve derived from CT coronary angiography (CT-FFR) has to date been typically performed using the principles of computational fluid analysis in which a lumped parameter coronary vascular bed model is assigned to represent the impedance of the downstream coronary vascular networks absent in the computational domain for each coronary outlet. This approach may have a number of limitations. It may not account for the impact of the myocardial contraction and relaxation during the cardiac cycle, patient-specific boundary conditions for coronary artery outlets and vessel stiffness. We have developed a novel approach based on 4D-CT image tracking (registration) and structural and fluid analysis based on one dimensional mechanical model, to address these issues. In our approach, we analyzed the deformation variation of vessels and the volume variation of vessels to better define boundary conditions and stiffness of vessels. We focused on the blood flow and vessel deformation of coronary arteries and aorta near coronary arteries in the diastolic cardiac phase from 70% to 100 %. The blood flow variation of coronary arteries relates to the deformation of vessels, such as expansion and contraction of the cross-sectional area, during this period where resistance is stable, pressure loss is approximately proportional to flow. We used a statistical estimation method based on a hierarchical Bayes model to integrate 4D-CT measurements and structural and fluid analysis data. Under these analysis conditions, we performed structural and fluid analysis to determine pressure, flow rate and CT-FFR. Furthermore, the reduced-order model based on fluid analysis was studied in order to shorten the computational time for 4D-CT-FFR analysis. The consistency of this method has been verified by a comparison of 4D-CT-FFR analysis results derived from five clinical 4D-CT datasets with invasive measurements of FFR. Additionally, phantom experiments of flexible tubes with and without stenosis using pulsating pumps, flow sensors and pressure sensors were performed. Our results show that the proposed 4D-CT-FFR analysis method has the potential to accurately estimate the effect of coronary artery stenosis on blood flow.

Prognostic value of the optimal measurement location of on-site CT-derived fractional flow reserve

“…After the procedure, CT-FFR was calculated using the analysis software (Canon Medial Systems Corporation, Otawara, Japan), which allows the computation of CT-FFR values at any selected points of the coronary tree. CT-FFR was calculated according to the fluid structure interaction, in consideration of changes in the shape, movement, cross-sectional area, and volume of the coronary artery using several optimal cardiac phases to acquire 70-99% of the cardiac phase [16,17]. Hierarchical Bayes & Markov-Chain Monte Carlo Methods were applied to determine conditions for the analysis.…”

Incremental Diagnostic Value of CT Fractional Flow Reserve Using Subtraction Method in Patients with Severe Calcification: A Pilot Study

“…CT-FFR was calculated for the original and the subtracted data. CT-FFR was calculated using a previously reported method [ 11 , 12 , 15 , 24 ].…”

“…A FFR calculation algorithm was developed from CCTA acquired via 320-row area detector CT (320-ADCT) using fluid–structure interaction as a method for CT-derived FFR (CT-FFR). This is considered to be capable of setting conditions unique to each patient in CT-FFR calculations, based on the shape, movement, cross-sectional area, and changes in the volume of the coronary artery, by acquiring multiple optimum cardiac phases from 70–99% of the cardiac phase data within one heartbeat and analyzing these data based on the hierarchical Bayes and Markov chain Monte Carlo method [ 10 , 11 ]. In addition, on-site analysis at a workstation is possible by calculating the 1D computational fluid dynamics.…”