Ramasamy Anantharaman, Safi Hannah, Moon James C, Andiapen Mervyn, Rathod Krishnaraj S, Maurovich-Horvat Pal, Bajaj Retesh, Serruys Patrick W, Mathur Anthony, Baumbach Andreas, Pugliese Francesca, Torii Ryo, Bourantas Christos V
Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.
William Harvey Research Institute, Queen Mary University London, London, United Kingdom.
Cardiology. 2020;145(5):285-293. doi: 10.1159/000506537. Epub 2020 Apr 14.
Computed tomographic coronary angiography (CTCA) is a non-invasive imaging modality, which allows plaque burden and composition assessment and detection of plaque characteristics associated with increased vulnerability. In addition, CTCA-based coronary artery reconstruction enables local haemodynamic forces assessment, which regulate plaque formation and vascular inflammation and prediction of lesions that are prone to progress and cause events. However, the use of CTCA for vulnerable plaque detection in the clinical arena remains limited. To unlock the full potential of CTCA and enable its broad use, further work is needed to develop user-friendly processing tools that will allow fast and accurate analysis of CTCA, computational fluid dynamic modelling, and evaluation of the local haemodynamic forces. The present study aims to develop a seamless platform that will overcome the limitations of CTCA and enable fast and accurate evaluation of plaque morphology and physiology. We will analyse imaging data from 70 patients with coronary artery disease who will undergo state-of-the-art CTCA and near-infrared spectroscopy-intravascular ultrasound imaging and develop and train algorithms that will take advantage of the intravascular imaging data to optimise vessel segmentation and plaque characterisation. Furthermore, we will design an advanced module that will enable reconstruction of coronary artery anatomy from CTCA, blood flow simulation, shear stress estimation, and comprehensive visualisation of vessel pathophysiology. These advances are expected to facilitate the broad use of CTCA, not only for risk stratification but also for the evaluation of the effect of emerging therapies on plaque evolution.
计算机断层扫描冠状动脉造影(CTCA)是一种非侵入性成像方式,可用于评估斑块负荷和成分,并检测与易损性增加相关的斑块特征。此外,基于CTCA的冠状动脉重建能够评估局部血流动力学力,这些力调节斑块形成和血管炎症,并预测易于进展并导致事件发生的病变。然而,在临床领域中使用CTCA检测易损斑块仍然有限。为了充分发挥CTCA的潜力并使其得到广泛应用,需要进一步开展工作,开发用户友好的处理工具,以实现对CTCA的快速准确分析、计算流体动力学建模以及局部血流动力学力的评估。本研究旨在开发一个无缝平台,克服CTCA的局限性,实现对斑块形态和生理学的快速准确评估。我们将分析70例冠状动脉疾病患者的影像数据,这些患者将接受先进的CTCA以及近红外光谱-血管内超声成像检查,并开发和训练算法,利用血管内成像数据优化血管分割和斑块特征描述。此外,我们将设计一个高级模块,能够从CTCA重建冠状动脉解剖结构、进行血流模拟、估计剪切应力,并全面可视化血管病理生理学。这些进展有望促进CTCA的广泛应用,不仅用于风险分层,还用于评估新兴疗法对斑块演变的影响。