Browsing by Author "Bourantas, C.V."

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An Automated User-Friendly Software for Fast Computation of Blood Flow Velocity From Coronary Angiographic Images
(SAGE Publications Inc., 2025) Çap, M.; Karaduman, M.; Zhou, T.; Erdoğan, E.; Tanboğa, İ.H.; Tufaro, V.; Bourantas, C.V.
Thrombolysis in myocardial infarction frame count enables assessment of coronary flow but cannot measure coronary flow velocity (CFV), which is needed to examine microvascular function. To overcome this limitation, we introduce a semi-automated software for fast CFV computation using contrast bolus tracking techniques in angiography and compare its performance against experts. The study included patients undergoing coronary angiography. Two experts measured the CFV using the number of frames, segment length, and frame rate. Measurements were repeated for shorter segments and different projections, and their estimations were compared with the software. In total, 123 patients (152 vessels) were included. The software had excellent reproducibility in measuring CFV (intraclass correlation coefficient (ICC) =.995), which was superior to experts (ICC =.946) and provided similar estimations irrespective of the segment length (ICC =.992); conversely, the experts overestimated CFV in short segments. The reproducibility of the experts and the software was moderate when comparing CFV measurements in different projections (first expert vs software ICC =.807, second expert vs software ICC =.790, first expert vs second expert ICC =.885). The software provides reproducible CFV estimations that are close to experts’ estimations. Further validation against wire-based functional techniques is needed to examine its potential in assessing microvascular function. © The Author(s) 2025.
Efficacy of Coronary Calcium Score in Predicting Coronary Artery Morphology in Patients With Obstructive Coronary Artery Disease
(Elsevier B.V., 2024) He, X.; Maung, S.; Ramasamy, A.; Mohamed, M.O.; Bajaj, R.; Yap, N.A.L.; Bourantas, C.V.
Background: Coronary artery calcium score (CACS) is an established marker of coronary artery disease (CAD) and has been extensively used to stratify risk in asymptomatic individuals. However, the value of CACS in predicting plaque morphology in patients with advanced CAD is less established. The present analysis aims to assess the association between CACS and plaque characteristics detected by near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) imaging in patients with obstructive CAD. Methods: Seventy patients with obstructive CAD underwent coronary computed tomography angiography (CTA) and 3-vessel NIRS-IVUS imaging were included in the present analysis. The CTA data were used to measure the CACS in the entire coronary tree and the segments assessed by NIRS-IVUS, and these estimations were associated with the NIRS-IVUS measurements at a patient and segment level. Results: In total, 65 patients (188 segments) completed the study protocol and were included in the analysis. A weak correlation was noted between the CACS, percent atheroma volume (r = 0.271, P =.002), and the calcific burden measured by NIRS-IVUS (r = 0.648, P <.001) at patient-level analysis. Conversely, there was no association between the CACS and the lipid content, or the incidence of high-risk plaques detected by NIRS. Linear regression analysis at the segment level demonstrated an association between the CACS and the total atheroma volume (coefficient, 0.087; 95% CI, 0.024-0.149; P =.008) and the calcific burden (coefficient, 0.117; 95% CI, 0.048-0.186; P =.001), but there was no association between the lipid content or the incidence of high-risk lesions. Conclusions: In patients with obstructive CAD, the CACS is not associated with the lipid content or plaque phenotypes. These findings indicate that the CACS may have a limited value for screening or stratifying cardiovascular risk in symptomatic patients with a high probability of CAD. © 2024 The Author(s)
Examination of the Performance of Machine Learning-Based Automated Coronary Plaque Characterization by Near-Infrared Spectroscopy–Intravascular Ultrasound and Optical Coherence Tomography With Histology
(Oxford University Press, 2025) Bajaj, R.; Parasa, R.; Broersen, A.; Johnson, T.; Garg, M.; Prati, F.; Bourantas, C.V.
Aims Near-infrared spectroscopy–intravascular ultrasound (NIRS–IVUS) and optical coherence tomography (OCT) can assess coronary plaque pathology but are limited by time-consuming and expertise-driven image analysis. Recently introduced machine learning (ML)-classifiers have expedited image processing, but their performance in assessing plaque pathology against histological standards remains unclear. The aim of this study is to assess the performance of NIRS–IVUS–ML-based and OCT–ML-based plaque characterization against a histological standard. Methods Matched histological and NIRS–IVUS/OCT frames from human cadaveric hearts were manually annotated and fibrotic (FT), and results calcific (Ca), and necrotic core (NC) regions of interest (ROIs) were identified. Near-infrared spectroscopy–intravascular ultrasound and OCT frames were processed by their respective ML classifiers to segment and characterize plaque components. The histologically defined ROIs were overlaid onto corresponding NIRS–IVUS/OCT frames and the ML classifier estimations were compared with histology. In total, 131 pairs of NIRS–IVUS/histology and 184 pairs of OCT/histology were included. The agreement of NIRS–IVUS–ML with histology [concordance correlation coefficient (CCC) 0.81 and 0.88] was superior to OCT–ML (CCC 0.64 and 0.73) for the plaque area and burden. Plaque compositional analysis showed a substantial agreement of the NIRS–IVUS–ML with histology for FT, Ca, and NC ROIs (CCC: 0.73, 0.75, and 0.66, respectively) while the agreement of the OCT–ML with histology was 0.42, 0.62, and 0.13, respectively. The overall accuracy of NIRS–IVUS–ML and OCT–ML for characterizing atheroma types was 83% and 72%, respectively. Conclusion NIRS–IVUS–ML plaque compositional analysis has a good performance in assessing plaque components while OCT–ML performs well for the FT, moderately for the Ca, and has weak performance in detecting NC tissue. This may be attributable to the limitations of standalone intravascular imaging and to the fact that the OCT–ML classifier was trained on human experts rather than histological standards. © The Author(s) 2025.