Browsing by Author "Bourantas, Christos V."

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A Deep Learning Methodology for Fully-Automated Quantification of Calcific Burden in High-Resolution Intravascular Ultrasound Images
(Springer, 2025) He, Xingwei; Mohamed, Mohamed O.; Ng, Nathaniel Yu Jian; Kumaran, Thamil; Bajaj, Retesh; Yap, Nathan Angelo Lecaros; Bourantas, Christos V.
Quantification of the calcific burden is valuable in percutaneous coronary intervention (PCI) planning and in research to assess its changes after pharmacotherapies targeting plaque progression. In intravascular ultrasound (IVUS) images this analysis is currently performed manually and time consuming. To overcome these limitations, we introduce a deep-learning (DL) method for seamless detection of the calcific tissue. IVUS images from 197 vessels were analysed by an expert who identified the presence of calcium, and these estimations were used to train a DL model for fast detection of calcific deposits. The output of the model was tested in a set of 30 vessels against the estimations of the two experts. Comparison was performed at a frame-, lesion- and segment level. In total 26,211 frames were included in the training and 5,138 in the test set. The estimations of the DL method for the presence of calcium were similar to the experts (kappa 0.842 and 0.848, p < 0.001), while the correlation between the DL approach and the two experts for the arc of calcium (0.946 and 0.947, p < 0.001) and calcific area (0.745 and 0.706, p < 0.001) were high. Lesion- (0.971 and 0.990, p < 0.001) and segment-level analysis (0.980 and 0.981, p < 0.001) demonstrated a high correlation between the method and the two experts for calcific burden. The proposed DL method is able to accurately detect the calcific tissue and quantify its burden. These features render it useful in research and are expected to facilitate its application in the clinical workflows to guide PCI.
End-Diastolic Segmentation of Intravascular Ultrasound Images Enables More Reproducible Volumetric Analysis of Atheroma Burden
(Wiley, 2022) Erdogan, Emrah; Huang, Xingru; Cooper, Jackie; Jain, Ajay; Ramasamy, Anantharaman; Bajaj, Retesh; Bourantas, Christos V.
Background Volumetric intravascular ultrasound (IVUS) analysis is currently performed at a fixed frame interval, neglecting the cyclic changes in vessel dimensions occurring during the cardiac cycle that can affect the reproducibility of the results. Analysis of end-diastolic (ED) IVUS frames has been proposed to overcome this limitation. However, at present, there is lack of data to support its superiority over conventional IVUS. Objectives The present study aims to compare the reproducibility of IVUS volumetric analysis performed at a fixed frame interval and at the ED frames, identified retrospectively using a novel deep-learning methodology. Methods IVUS data acquired from 97 vessels were included in the present study; each vessel was segmented at 1 mm interval (conventional approach) and at ED frame twice by an expert analyst. Reproducibility was tested for the following metrics; normalized lumen, vessel and total atheroma volume (TAV), and percent atheroma volume (PAV). Results The mean length of the analyzed segments was 50.0 +/- 24.1 mm. ED analysis was more reproducible than the conventional analysis for the normalized lumen (mean difference: 0.76 +/- 4.03 mm(3) vs. 1.72 +/- 11.37 mm(3); p for the variance of differences ratio < 0.001), vessel (0.30 +/- 1.79 mm(3) vs. -0.47 +/- 10.26 mm(3); p < 0.001), TAV (-0.46 +/- 4.03 mm(3) vs. -2.19 +/- 14.39 mm(3); p < 0.001) and PAV (-0.12 +/- 0.59% vs. -0.34 +/- 1.34%; p < 0.001). Results were similar when the analysis focused on the 10 mm most diseased segment. The superiority of the ED approach was due to a more reproducible detection of the segment of interest and to the fact that it was not susceptible to the longitudinal motion of the IVUS probe and the cyclic changes in vessel dimensions during the cardiac cycle. Conclusions ED IVUS segmentation enables more reproducible volumetric analysis and quantification of TAV and PAV that are established end points in longitudinal studies assessing the efficacy of novel pharmacotherapies. Therefore, it should be preferred over conventional IVUS analysis as its higher reproducibility is expected to have an impact on the sample size calculation for the primary end point.
Implications of Coronary Calcification on the Assessment of Plaque Pathology: a Comparison of Computed Tomography and Multimodality Intravascular Imaging
(Springer, 2025) Yap, Nathan Angelo Lecaros; Ramasamy, Anantharaman; Tanboga, Ibrahim Halil; He, Xingwei; Cap, Murat; Bajaj, Retesh; Bourantas, Christos V.
Objectives This study aimed to investigate the impact of calcific (Ca) on the efficacy of coronary computed coronary angiography (CTA) in evaluating plaque burden (PB) and composition with near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) serving as the reference standard. Materials and methods Sixty-four patients (186 vessels) were recruited and underwent CTA and 3-vessel NIRS-IVUS imaging (NCT03556644). Expert analysts matched and annotated NIRS-IVUS and CTA frames, identifying lumen and vessel wall borders. Tissue distribution was estimated using NIRS chemograms and the arc of Ca on IVUS, while in CTA Hounsfield unit cut-offs were utilized to establish plaque composition. Plaque distribution plots were compared at segment-, lesion-, and cross-sectional-levels. Results Segment- and lesion-level analysis showed no effect of Ca on the correlation of NIRS-IVUS and CTA estimations. However, at the cross-sectional level, Ca influenced the agreement between NIRS-IVUS and CTA for the lipid and Ca components (p-heterogeneity < 0.001). Proportional odds model analysis revealed that Ca had an impact on the per cent atheroma volume quantification on CTA compared to NIRS-IVUS at the segment level (p-interaction < 0.001). At lesion level, Ca affected differences between the modalities for maximum PB, remodelling index, and Ca burden (p-interaction < 0.001, 0.029, and 0.002, respectively). Cross-sectional-level modelling demonstrated Ca's effect on differences between modalities for all studied variables (p-interaction <= 0.002). Conclusion Ca burden influences agreement between NIRS-IVUS and CTA at the cross-sectional level and causes discrepancies between the predictions for per cent atheroma volume at the segment level and maximum PB, remodelling index, and Ca burden at lesion-level analysis. Clinical relevance statement Coronary calcification affects the quantification of lumen and plaque dimensions and the characterization of plaque composition coronary CTA. This should be considered in the analysis and interpretation of CTAs performed in patients with extensive Ca burden. Key Points Coronary CT Angiography is limited in assessing coronary plaques by resolution and blooming artefacts. Agreement between dual-source CT angiography and NIRS-IVUS is affected by a Ca burden for the per cent atheroma volume. Advanced CT imaging systems that eliminate blooming artefacts enable more accurate quantification of coronary artery disease and characterisation of plaque morphology.