Computer-aided detection of non-calcified plaques in coronary CT angiograms

描述（由申请人提供）：心血管疾病是美国男性和女性的主要死亡原因。超过1600万美国人患有冠心病（CHD），每年造成约50万人死亡。最常见的冠心病是冠状动脉疾病，主要由动脉粥样硬化引起。近年来的临床证据表明，非钙化斑块（NCPs）比钙化斑块更容易破裂。斑块破裂及随之而来的血栓形成是急性心肌梗死的主要原因。多探测器冠状动脉CT血管造影（cCTA）有可能帮助临床医生早期发现和量化NCP。因此，cCTA可用于CHD检测、风险分层、监测和风险降低治疗有效性的评估。然而，这些潜在应用中的许多尚未在临床上利用。 本项目的目标是开发一种计算机辅助检测（CADe）系统，作为辅助临床医生在cCTA检查中检测和定量NCP的第二读片器。我们的具体目标是（1）开发用于检测导致冠状动脉狭窄和/或沿着正性重塑的NCP的机器学习方法，以及（2）通过观察者ROC研究评价CADe对放射科医生在cCTA上检测NCP的影响。为了实现这些目标，我们将收集cCTA病例数据库用于训练和测试CADe系统，通过设计3D多尺度冠状动脉反应增强、分割和动态球囊血管跟踪方法来定义搜索空间，开发独特的血管缝合方法，以便从前瞻性或回顾性门控cCTA检查的所有可用阶段中自动识别每个动脉段的最佳质量阶段，开发创新的血管扇区轮廓分析和血管管腔分析，以检测导致狭窄或正性重塑的NCP，估计总NCP体积，并探索校准方法，通过体模研究量化斑块密度。为了证明CADe的有用性，将进行临床前阅片人研究，以比较放射科医生在使用和不使用CADe的情况下对NCP的检测准确性。 该项目的主要创新包括（1）成为第一个自动检测非钙化斑块的CADe系统，包括在cCTA中导致正性重塑或狭窄的斑块，（2）开发新的机器学习技术，包括血管缝合方法，血管扇区轮廓分析，多尺度增强响应和动态球囊跟踪，特别适用于冠状动脉树，（3）进行首次ROC研究，评价CADe对放射科医师发现NCP的影响。 公共卫生相关性：心血管疾病是美国男性和女性死亡的主要原因。如果所提出的CADe系统被成功开发，则其将（1）提供准确、高效和一致的工具以帮助临床医生检测和量化非钙化斑块（NCP），包括导致个体患者的正性重构和/或狭窄的那些，（2）帮助临床医生估计总NCP负担并研究其意义，类似于总钙评分的意义，这可能导致易损斑块的临床管理的改善，以及（3）通过提供治疗反应的监测工具来加速研究以开发冠心病的新治疗选择。因此，拟议的CADe系统可以作为这些更广泛的未来应用的基础，并提高cCTA的有效性。改进NCP的检测和管理可能会降低心肌梗死的风险，并将对医疗保健产生强大而持久的影响。

DESCRIPTION (provided by applicant): Cardiovascular disease is the leading cause of death in both men and women in the United States. Over 16 million Americans have coronary heart disease (CHD), causing about 0.5 million deaths each year. The most common CHD is coronary artery disease which is mainly caused by atherosclerosis. Clinical evidence in recent years shows that noncalcified plaques (NCPs) are more vulnerable to rupture than calcified plaques. Plaque rupture and the thrombosis that follows is the main cause of acute myocardial infarction. Multidetector coronary CT angiography (cCTA) has the potential to help clinicians in early detection and in quantification of NCPs. cCTA may thus be useful for CHD detection, risk stratification, monitoring, and evaluation of the effectiveness of risk reduction treatment. However, many of these potential applications have not been utilized clinically. The goal of this project is to develop a computer-aided detection (CADe) system to serve as a second reader for assisting clinicians in detection and quantification of NCPs in cCTA exams. Our specific aims are to (1) develop machine learning methods for detection of NCPs causing stenosis and/or positive remodeling along coronary arteries, and (2) evaluate the effect of CADe on radiologists' detection of NCPs on cCTA by observer ROC study. To achieve these aims, we will collect a database of cCTA cases for training and testing the CADe system, define the search space by designing 3D multiscale coronary artery response enhancement, segmentation, and dynamic balloon vessel tracking methods, develop a unique vessel- stitching method to automatically identify the best-quality phase for each individual artery segment from all available phases in prospectively or retrospectively gated cCTA exams, develop innovative vessel-sector- profile analysis and vessel lumen analysis to detect NCPs that cause stenosis or positive remodeling, estimate the total NCP volume, and explore calibration method to quantify plaque density by phantom studies. To demonstrate the usefulness of CADe, a preclinical reader study will be conducted to compare radiologists' detection accuracy of NCPs with and without CADe. The major innovations of this project include (1) being the first CADe system to automatically detect non-calcified plaques including those cause positive remodeling or stenosis in cCTA, (2) development of new machine learning techniques including the vessel-stitching method, vessel-sector-profile analysis, multiscale enhancement response, and dynamic balloon tracking specifically suited for coronary arterial trees, and (3) conducting the first ROC study to evaluate the effect of CADe on radiologists' detection of NCPs. PUBLIC HEALTH RELEVANCE: Cardiovascular disease is the leading cause of death in both men and women in the United States. If the proposed CADe system is successfully developed, it will (1) provide an accurate, efficient, and consistent tool to assist clinicians in detecting and quantifying non-calcified plaques (NCP) including those causing positive remodeling and/or stenosis for an individual patient, (2) help clinicians estimate the total NCP burden and study its significance, in analogy to that of the total calcium score, which may lead to improved clinical management of the vulnerable plaques, and (3) accelerate studies to develop new treatment options of coronary heart disease by providing a monitoring tool of treatment response. The proposed CADe system can thus serve as a foundation for these broader future applications and improve the efficacy of cCTA. Improved detection and management of NCPs may reduce the risk of myocardial infarctions and will have strong and long-lasting impact on health care.