Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia; ANZAC Research Institute, The University of Sydney, Sydney, NSW, 2139, Australia.
Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia; ANZAC Research Institute, The University of Sydney, Sydney, NSW, 2139, Australia.
Comput Biol Med. 2019 May;108:111-121. doi: 10.1016/j.compbiomed.2019.03.008. Epub 2019 Mar 14.
Although intravascular ultrasound (IVUS) is the commonest intravascular imaging modality, it still is inefficient for clinical use as it requires laborious manual analysis. This study demonstrates the feasibility of a near real-time fully automated technology for accurate identification, detection, and quantification of luminal borders in intravascular images. This technology uses a combination of the novel approaches of a self-tuning engine, dynamic and static masking systems, radar-wise scan, and contour correction cycle method. The performance of the computer algorithm developed based on this technology was tested on a sequence of IVUS and True Vessel Characterization (TVC) images obtained from the left anterior descending (LAD) artery of 6 patients with coronary artery disease. The accuracy of the algorithm was evaluated by comparing luminal borders traced manually with those detected automatically. The processing time of the developed algorithm was also tested on a Dell laptop with an Intel Core i7-8750H Processor (4.1 GHz with 6 cores, 9 MB Cache). Linear regression and Bland-Altman analyses indicated high correlation between manual and automatic tracings (Y = 0.80 × X+1.70, R = 0.88 & 0.67 ± 1.31 (bias±SD)). Whereas analysis of 2000 IVUS images using one CPU core with a 30% load took 23.12 min, the same analysis using six CPU cores with 90% load took 1.0 min. The performance, accuracy, and speed of the presented state-of-the-art technology demonstrates its capacity for use in clinical settings.
尽管血管内超声（IVUS）是最常见的血管内成像方式，但由于需要繁琐的手动分析，因此其在临床应用中仍然效率低下。本研究展示了一种用于准确识别、检测和量化血管内图像管腔边界的近乎实时全自动技术的可行性。该技术结合了自调谐引擎、动态和静态屏蔽系统、雷达扫描以及轮廓校正循环方法等新颖方法。该技术开发的计算机算法在从 6 名冠心病患者的左前降支（LAD）获得的一系列 IVUS 和 True Vessel Characterization（TVC）图像上进行了测试。通过将手动追踪的管腔边界与自动检测的管腔边界进行比较，评估了算法的准确性。还在配备有 Intel Core i7-8750H 处理器（4.1GHz 时具有 6 个内核、9MB 缓存）的 Dell 笔记本电脑上测试了开发算法的处理时间。线性回归和 Bland-Altman 分析表明手动和自动追踪之间具有高度相关性（Y=0.80×X+1.70，R=0.88 和 0.67±1.31（偏差±SD））。而使用一个 CPU 内核以 30%的负载分析 2000 张 IVUS 图像需要 23.12 分钟，而使用六个 CPU 内核以 90%的负载进行相同的分析则需要 1.0 分钟。所提出的最先进技术的性能、准确性和速度表明其有能力在临床环境中使用。
