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

冠状动脉 CT 血管造影中非钙化斑块的计算机辅助检测

基本信息

批准号：
8206668
负责人：
HEANG-PING CHAN
金额：
$ 58.54万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-12-15 至 2014-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8206668
关键词：
Acute myocardial infarction American Angiography Arterial Fatty Streak Arteries Atherosclerosis Calcified Calcium Calibration Cardiovascular Diseases Catheters Cause of Death Cessation of life Clinical Clinical Management Computer Vision Systems Coronary Coronary Arteriosclerosis Coronary artery Coronary heart disease Data Data Set Databases Detection Development Dose Early Diagnosis Effectiveness Electrocardiogram Evaluation Event Foundations Future Goals Healthcare Image Imagery Individual Lead Machine Learning Methods Modality Monitor Myocardial Infarction Patients Performance Phase Procedures Radiation Reader Receiver Operating Characteristics Recording of previous events Resolution Risk Risk Reduction Rupture Scanning Stenosis Stratification System Techniques Testing Thrombosis Time Training Trees Ultrasonography United States Visual Woman computer aided detection density design detector improved innovation men pre-clinical prospective radiologist response tool treatment response virtual

项目摘要

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.

心血管疾病是美国男性和女性死亡的主要原因。超过 1600 万美国人患有冠心病 (CHD)，每年导致约 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) 比非钙化斑块更容易破裂。钙化斑块。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梗塞。多排冠状动脉 CT 血管造影 (cCTA) 有潜力帮助临床医生进行早期诊断 NCP 的检测和定量。因此，cCTA 可能有助于 CHD 检测、风险分层、监测和评估风险降低治疗的有效性。然而，其中许多潜在的应用尚未应用于临床。该项目的目标是开发计算机辅助检测（CADe）系统作为第二个用于协助临床医生在 cCTA 考试中检测和量化 NCP 的阅读器。我们的具体目标是 (1) 开发机器学习方法来检测导致狭窄和/或正性重塑的 NCP 沿着冠状动脉，(2) 通过以下方式评估 CADe 对放射科医生在 cCTA 上检测 NCP 的影响：观察员 ROC 研究。为了实现这些目标，我们将收集 cCTA 案例数据库用于培训和测试CADe系统，通过设计3D多尺度冠状动脉反应来定义搜索空间增强、分割和动态气球血管跟踪方法，开发独特的血管- 缝合方法可自动识别来自所有动脉段的最佳质量相位前瞻性或回顾性门控 cCTA 考试的可用阶段，开发创新的船舶行业轮廓分析和血管腔分析，以检测导致狭窄或正性重塑的 NCP，估计NCP总体积，并探索通过体模量化斑块密度的校准方法研究。为了证明 CADe 的实用性，将进行临床前读者研究来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。该项目的主要创新点包括（1）是第一个自动检测的CADe系统非钙化斑块，包括那些在 cCTA 中引起正重塑或狭窄的斑块，(2) 开发新的机器学习技术，包括血管缝合方法、血管扇区剖面分析、多尺度增强响应和特别适合冠状动脉树的动态球囊跟踪，以及 (3) 开展第一项 ROC 研究，以评估 CADe 对放射科医生检测 NCP 的影响。