School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia.
School of Electrical Electronic Engineering, University of Adelaide, Adelaide, SA 5005, Australia.
Tomography. 2022 May 17;8(3):1307-1349. doi: 10.3390/tomography8030108.
Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients' arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.
冠状动脉光学相干断层扫描（OCT）是一种基于近红外光的血管内成像方式，能够达到 10-20 µm 的轴向分辨率。这种分辨率能够准确确定高风险斑块特征，如薄帽纤维粥样瘤；然而，仅观察形态特征仍然不能为斑块进展或未来主要不良心血管事件（MACE）提供可靠的阳性预测能力。生物力学模拟可以辅助进行这种预测，但这需要从血管内成像中提取形态特征，以构建患者动脉的准确三维（3D）模拟。提取这些特征是一个繁琐的过程，通常由经过培训的专家手动进行。为了解决这一挑战，已经出现了许多技术来自动执行这些过程，同时克服了与 OCT 成像相关的困难，例如其有限的穿透深度。本系统综述总结了过去五年（2016-2021 年）中自动化分割技术的进展，重点介绍了它们在血管 3D 重建及其后续模拟中的应用。我们根据正在处理的特征将其分为四类，即：冠状动脉管腔；动脉层；斑块特征和亚型；以及支架。还讨论了未来的创新领域以及它们未来转化的潜力。
