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

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。临床 近年来的证据表明，非钙化斑块(NCP)比 钙化斑块。斑块破裂和随之而来的血栓形成是急性心肌梗塞的主要原因 脑梗塞。多层冠状动脉CT血管成像(CCTA)具有早期帮助临床医生的潜力 在NCP的检测和量化方面。因此，CCTA可用于冠心病检测、风险分层、 监测和评估降低风险治疗的有效性。然而，这些潜力中的许多 应用程序还没有在临床上使用。 这个项目的目标是开发一个计算机辅助检测(CADE)系统作为第二个 用于辅助临床医生在CCTA检查中检测和量化NCP的阅读器。我们的具体目标是 开发机器学习方法以检测导致狭窄和/或正向重构的NCP 和(2)评价CADE对放射科医生检测CCTA中NCP的影响。 观察者ROC研究。为了达到这些目标，我们将收集CCTA案例数据库进行培训和 测试CADE系统，通过设计3D多尺度冠状动脉响应来定义搜索空间 增强、分割和动态气球血管跟踪方法，开发了一种独特的血管- 一种自动识别每个动脉段最佳质量时相的缝合方法 未来或回顾CCTA考试的可用阶段，开发创新的船舶部门- 轮廓分析和血管管腔分析以检测导致狭窄或正向重构的NCP， 估计NCP的总体积，探索用模体定量斑块密度的校准方法 学习。为了证明CADE的有效性，将进行一项临床前读者研究，以比较 放射科医生对有无CADE的NCP的检测准确率。 该项目的主要创新点包括：(1)成为第一个自动检测的CADE系统 非钙化斑块，包括那些导致CCTA阳性重构或狭窄的斑块，(2)新的 机器学习技术，包括血管缝合方法、血管扇形分析、多尺度 增强响应和特别适用于冠状动脉树的动态球囊跟踪，以及(3) 开展首个ROC研究，以评估CADE对放射科医生检测NCP的影响。

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.