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Objectives: This study sought to present a novel computer model for fast computation of myocardial fractional flow reserve (FFR) and to evaluate it in patients with intermediate coronary stenoses., Background: FFR is an indispensable tool to identify individual coronary stenoses causing ischemia. Calculation of FFR from x-ray angiographic data may increase the utility of FFR assessment., Methods: Consecutive patients with intermediate coronary stenoses undergoing pressure wire-based FFR measurements were analyzed by a core laboratory. Three-dimensional quantitative coronary angiography (QCA) was performed and the mean volumetric flow rate at hyperemia was calculated using TIMI (Thrombolysis In Myocardial Infarction) frame count combined with 3-dimensional QCA. Computational fluid dynamics was applied subsequently with a novel strategy for the computation of FFR. Diagnostic performance of the computed FFR (FFRQCA) was assessed using wire-based FFR as reference standard., Results: Computation of FFRQCA was performed on 77 vessels in 68 patients. Average diameter stenosis was 46.6 ± 7.3%. FFRQCA correlated well with FFR (r = 0.81, p < 0.001), with a mean difference of 0.00 ± 0.06 (p = 0.541). Applying the FFR cutoff value of ≤0.8 to FFRQCA resulted in 18 true positives, 50 true negatives, 4 false positives, and 5 false negatives. The area under the receiver-operating characteristic curve was 0.93 for FFRQCA, 0.73 for minimum lumen area, and 0.65 for percent diameter stenosis., Conclusions: Computation of FFRQCA is a novel method that allows the assessment of the functional significance of intermediate stenosis. It may emerge as a safe, efficient, and cost-reducing tool for evaluation of coronary stenosis severity during diagnostic angiography., (Copyright © 2014 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

The previously study found, using a mouse model, that acute myocardial infarction accelerated atherosclerosis. This study assessed whether ST elevation myocardial infarction (STEMI) accelerates the progression of non-culprit coronary lesion (NCCL) in patients who underwent percutaneous coronary interventions (PCI). Four hundred and forty-nine patients who underwent successful PCI with stents and follow-up coronary angiography in a single center were enrolled. The NCCL progression was assessed using three-dimensional quantitative coronary angiography and was defined as ≥10 % diameter reduction of a preexisting stenosis ≥50, ≥30 % diameter reduction of a stenosis <50 %, development of a new stenosis ≥30 % in a previously normal segment, or progression to total occlusion. The patients were classified into two groups according to whether the progression existed or not. The median age of patients was 58.4 years. The mean angiographic follow-up period was 12.3 months, 134 (29.8 %) patients had NCCL progression. Multivariate Cox regression analysis (step-wise) showed that STEMI was the only independent determinant of NCCL progression. Compared to the other coronary artery disease group, the crude hazard ratio (HR) of NCCL progression for the STEMI group was 3.20 (95 % CI 2.27-4.50; p < 0.001), and the association remained significantly after adjustment for age, sex, BMI, SBP, DBP, serum lipids, fasting blood glucose, peak monocyte count, smoking, drinking, hypertension, diabetes mellitus and lesion characteristics of NCCL (adjusted HR 3.56, 95 % CI 2.41-5.27; p < 0.001). The ST elevation acute myocardial infarction accelerates non-culprit coronary lesion atherosclerosis.

The combination/fusion of quantitative coronary angiography (QCA) and intravascular ultrasound (IVUS)/optical coherence tomography (OCT) depends to a great extend on the co-registration of X-ray angiography (XA) and IVUS/OCT. In this work a new and robust three-dimensional (3D) segmentation and registration approach is presented and validated. The approach starts with standard QCA of the vessel of interest in the two angiographic views (either biplane or two monoplane views). Next, the vessel of interest is reconstructed in 3D and registered with the corresponding IVUS/OCT pullback series by a distance mapping algorithm. The accuracy of the registration was retrospectively evaluated on 12 silicone phantoms with coronary stents implanted, and on 24 patients who underwent both coronary angiography and IVUS examinations of the left anterior descending artery. Stent borders or sidebranches were used as markers for the validation. While the most proximal marker was set as the baseline position for the distance mapping algorithm, the subsequent markers were used to evaluate the registration error. The correlation between the registration error and the distance from the evaluated marker to the baseline position was analyzed. The XA-IVUS registration error for the 12 phantoms was 0.03 ± 0.32 mm (P = 0.75). One OCT pullback series was excluded from the phantom study, since it did not cover the distal stent border. The XA-OCT registration error for the remaining 11 phantoms was 0.05 ± 0.25 mm (P = 0.49). For the in vivo validation, two patients were excluded due to insufficient image quality for the analysis. In total 78 sidebranches were identified from the remaining 22 patients and the registration error was evaluated on 56 markers. The registration error was 0.03 ± 0.45 mm (P = 0.67). The error was not correlated to the distance between the evaluated marker and the baseline position (P = 0.73). In conclusion, the new XA-IVUS/OCT co-registration approach is a straightforward and reliable solution to combine X-ray angiography and IVUS/OCT imaging for the assessment of the extent of coronary artery disease. It provides the interventional cardiologist with detailed information about vessel size and plaque size at every position along the vessel of interest, making this a suitable tool during the actual intervention.

Background: Accurate on-line assessments of vessel dimensions are of utmost importance for selecting the appropriate stent size in coronary interventions. Recently a new three-dimensional quantitative coronary angiography (3D QCA) analytical software package was developed to accurately assess the vessel dimensions for the planning and guidance of such coronary interventions. This study aimed to validate the 3D QCA software package for assessing arterial segment length by comparing with intravascular ultrasound (IVUS). In addition, the difference in the two measurements from 3D QCA and IVUS for curved segments was studied., Methods: A retrospective study including 20 patients undergoing both coronary angiography and IVUS examinations of the left coronary artery was set up for the validation. The same vessel segments of interest between proximal and distal markers were identified and measured on both angiographic and IVUS images, by the 3D QCA software and by a quantitative IVUS software package, respectively. In addition, the curvature of each of the segments of interest was assessed and the correlation between the accumulated curvature of the segment and the difference in segment lengths measured from the two imaging modalities was analyzed., Results: 37 vessel segments of interest were identified from both angiographic and IVUS images. The 3D QCA segment length was slightly longer than the IVUS segment length (15.42 +/- 6.02 mm vs. 15.12 +/- 5.81 mm, P = 0.040). The linear correlation of the two measurements was: 3D QCA Length = -0.09 + 1.03 x IVUS Length (r(2) = 0.98, P < 0.001). Bland-Altman plot showed that the difference in the two measurements was not correlated with the average of the two measurements (P = 0.141), but with the accumulated curvature of the segment (P = 0.015). After refining the difference by the correlation, the average difference of the two measurements decreased from 0.30 +/- 0.86 mm (P = 0.040) to 0.00 +/- 0.78 mm (P = 0.977)., Conclusions: The 3D QCA software package can accurately assess the actual arterial segment length. The difference in segment lengths measured from 3D QCA and IVUS was correlated with the accumulated curvature of the segment., ((c) 2010 Wiley-Liss, Inc.)