Validation of Wall Shear Stress Assessment in Non-invasive Coronary CTA versus Invasive Imaging: A Patient-Specific Computational Study.

Endothelial shear stress (ESS) identifies coronary plaques at high risk for progression and/or rupture leading to a future acute coronary syndrome. In this study an optimized methodology was developed to derive ESS, pressure drop and oscillatory shear index using computational fluid dynamics (CFD) in 3D models of coronary arteries derived from non-invasive coronary computed tomography angiography (CTA). These CTA-based ESS calculations were compared to the ESS calculations using the gold standard with fusion of invasive imaging and CTA. In 14 patients paired patient-specific CFD models based on invasive and non-invasive imaging of the left anterior descending (LAD) coronary arteries were created. Ten patients were used to optimize the methodology, and four patients to test this methodology. Time-averaged ESS (TAESS) was calculated for both coronary models applying patient-specific physiological data available at the time of imaging. For data analysis, each 3D reconstructed coronary artery was divided into 2 mm segments and each segment was subdivided into 8 arcs (45°).TAESS and other hemodynamic parameters were averaged per segment as well as per arc. Furthermore, the paired segment- and arc-averaged TAESS were categorized into patient-specific tertiles (low, medium and high). In the ten LADs, used for optimization of the methodology, we found high correlations between invasively-derived and non-invasively-derived TAESS averaged over segments (n = 263, r = 0.86) as well as arcs (n = 2104, r = 0.85, p < 0.001). The correlation was also strong in the four testing-patients with r = 0.95 (n = 117 segments, p = 0.001) and r = 0.93 (n = 936 arcs, p = 0.001).There was an overall high concordance of 78% of the three TAESS categories comparing both methodologies using the segment- and 76% for the arc-averages in the first ten patients. This concordance was lower in the four testing patients (64 and 64% in segment- and arc-averaged TAESS). Although the correlation and concordance were high for both patient groups, the absolute TAESS values averaged per segment and arc were overestimated using non-invasive vs. invasive imaging [testing patients: TAESS segment: 30.1(17.1-83.8) vs. 15.8(8.8-63.4) and TAESS arc: 29.4(16.2-74.7) vs 15.0(8.9-57.4) p < 0.001]. We showed that our methodology can accurately assess the TAESS distribution non-invasively from CTA and demonstrated a good correlation with TAESS calculated using IVUS/OCT 3D reconstructed models.

Automated Quantitative Assessment of Coronary Calcification Using Intravascular Ultrasound.

Coronary calcification represents a challenge in the treatment of coronary artery disease by stent placement. It negatively affects stent expansion and has been related to future adverse cardiac events. Intravascular ultrasound (IVUS) is known for its high sensitivity in detecting coronary calcification. At present, automated quantification of calcium as detected by IVUS is not available. For this reason, we developed and validated an optimized framework for accurate automated detection and quantification of calcified plaque in coronary atherosclerosis as seen by IVUS. Calcified lesions were detected by training a supported vector classifier per IVUS A-line on manually annotated IVUS images, followed by post-processing using regional information. We applied our framework to 35 IVUS pullbacks from each of the three commonly used IVUS systems. Cross-validation accuracy for each system was >0.9, and the testing accuracy was 0.87, 0.89 and 0.89 for the three systems. Using the detection result, we propose an IVUS calcium score, based on the fraction of calcium-positive A-lines in a pullback segment, to quantify the extent of calcified plaque. The high accuracy of the proposed classifier suggests that it may provide a robust and accurate tool to assess the presence and amount of coronary calcification and, thus, may play a role in image-guided coronary interventions.

Impact of bioresorbable scaffold design characteristics on local haemodynamic forces: an ex vivo assessment with computational fluid dynamics simulations.

AIMS: Bioresorbable scaffold (BRS) regions exposed to flow recirculation, low time-averaged wall shear stress (TAWSS) and high oscillatory shear index (OSI) develop increased neointima tissue. We investigated haemodynamic features in four different BRSs. METHODS AND RESULTS: Fantom (strut height [SH] = 125 µm), Fantom Encore (SH = 98 µm), Absorb (SH = 157 µm) and Magmaris (SH = 150 µm) BRSs were deployed in phantom tubes and imaged with microCT. Both 2D and 3D geometrical scaffold models were reconstructed. Computational fluid dynamics (CFD) simulation was performed to compute TAWSS and OSI. Thicker struts had larger recirculation zones and lower TAWSS in 2D. Absorb had the largest recirculation zone and the lowest TAWSS (240 µm and -0.18 Pa), followed by Magmaris (170 µm and -0.15 Pa), Fantom (140 µm and -0.14 Pa) and Fantom Encore (100 µm and -0.13 Pa). Besides strut size, stent design played a dominant role in 3D. The highest percentage area adverse TAWSS (<0.5 Pa) and OSI (>0.2) were found for Fantom (56% and 30%) and Absorb (53% and 33%), followed by Fantom Encore (30% and 25%) and Magmaris (25% and 20%). Magmaris had the smallest areas due to a small footprint and rounded struts. CONCLUSIONS: Due to stent design, both Fantom Encore and Magmaris showed smaller TAWSS and OSI than Fantom and Absorb. This study quantifies which scaffold features are most important to reduce long-term restenosis.