Fractional Flow Reserve Estimated at Coronary CT Angiography in Intermediate Lesions: Comparison of Diagnostic Accuracy of Different Methods to Determine Coronary Flow Distribution

Abstract

Purpose To compare the diagnostic accuracy of different computed tomographic (CT) fractional flow reserve (FFR) algorithms for vessels with intermediate stenosis. Materials and Methods This cross-sectional HIPAA-compliant and human research committee-approved study applied a four-step CT FFR algorithm in 61 patients (mean age, 69 years ± 10; age range, 29-89 years) with a lesion of intermediate-diameter stenosis (25%-69%) at CT angiography who underwent FFR measurement within 90 days. The per-lesion diagnostic performance of CT FFR was tested for three different approaches to estimate blood flow distribution for CT FFR calculation. The first two, the Murray law and the Huo-Kassab rule, used coronary anatomy; the third used contrast material opacification gradients. CT FFR algorithms and CT angiography percentage diameter stenosis (DS) measurements were compared by using the area under the receiver operating characteristic curve (AUC) to detect FFRs of 0.8 or lower. Results Twenty-five lesions (41%) had FFRs of 0.8 or lower. The AUC of CT FFR determination by using contrast material gradients (AUC = 0.953) was significantly higher than that of the Huo-Kassab (AUC = 0.882, P = .043) and Murray law models (AUC = 0.871, P = .033). All three AUCs were higher than that for 50% or greater DS at CT angiography (AUC = 0.596, P < .001). Correlation of CT FFR with FFR was highest for gradients (Spearman ρ = 0.80), followed by the Huo-Kassab rule (ρ = 0.68) and Murray law (ρ = 0.67) models. All CT FFR algorithms had small biases, ranging from -0.015 (Murray) to -0.049 (Huo-Kassab). Limits of agreement were narrowest for gradients (-0.182, 0.147), followed by the Huo-Kassab rule (-0.246, 0.149) and the Murray law (-0.285, 0.256) models. Conclusion Clinicians can perform CT FFR by using a four-step approach on site to accurately detect hemodynamically significant intermediate-stenosis lesions. Estimating blood flow distribution by using coronary contrast opacification variations may improve CT FFR accuracy. RSNA, 2017 Online supplemental material is available for this article.

Advances in Cardiac Computed Tomography Functional Imaging Technology

“…Moreover, the study included some patients with severe calcification, and it was found that the accuracy of CT-FFR prediction was not affected. Furthermore, due to the improvement of quality control in the process of CCTA and the analysis software, the specificity of CT-FFR was much better than that in previous multicenter studies, and had a better correlation with invasive FFR (r = 0.82, p < 0.001) [31][32][33][34][35][36][37][38][39][40][41][42][43][44] (Table 2).…”

A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT‐FFR)

“…Personalized boundary conditions are usually derived from physiological rules associated with patient anatomy and blood flow‐related parameters, but can also be derived by more careful analysis of the CCTA data such as by, for example, using contrast kinetics or structural changes . A unique challenge in physics‐based simulation of blood flow in the coronary arteries is the modeling of autoregulatory mechanisms controlling different factors influencing overall flow.…”

CT Fractional Flow Reserve for Stable Coronary Artery Disease: The Ongoing Journey

“…While only one CT FFR technology is currently approved for clinical use (Heartflow, Redwood City, Calif) in the United States, there are several other algorithms that are being explored. In this month's issue of Radiology, Kishi et al (6) present data from an onsite algorithm with a focus on the potential of contrast material gradients to further improve the accuracy of this algorithm as compared with invasive FFR. In their study, the area under the receiver operating characteristic curve (AUC) of CT FFR calculated by using contrast gradients (AUC = 0.953) was significantly higher than the AUCs of the Huo-Kassab model (AUC = 0.882, P = .043) and Murray law model (AUC = 0.871, P = .033).…”

“…To date, much of the focus has been on the pattern and extent of atherosclerosis. For example, a higher likelihood of ischemia occurs with lesions composed of extensive plaque, with high aggregate plaque volume (6)(7)(8), and with plaques with adverse features such as outward expansion and low density. Other investigators have introduced the concept of coronary volume to mass as a potential predictor of ischemia in the absence of obstructive disease wherein baseline coronary blood flow is so tenuous that even modest atherosclerosis can result in ischemia (9).…”