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

• 批准号: 8392109
• 负责人: HEANG-PING CHAN
• 金额: $ 55.28万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 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/8392109
• 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

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），每年造成约50万人死亡。 最常见的CHD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）比破裂更容易受到破裂 钙化斑块。斑块破裂和随后的血栓形成是急性心肌的主要原因 梗塞。多探测器冠状动脉血管造影（CCTA）有可能在早期帮助临床医生 检测和定量NCP。因此，CCTA可能可用于CHD检测，风险分层， 监测和评估降低风险治疗的有效性。但是，其中许多潜力 尚未在临床上使用应用程序。 该项目的目的是开发计算机辅助检测（CADE）系统，以作为第二个 用于协助临床医生在CCTA考试中检测和定量NCP的读者。我们的具体目标是 （1）开发用于检测NCP的机器学习方法，导致狭窄和/或阳性重塑 沿冠状动脉，（2）评估CADE对放射科医生对NCP对CCTA的检测的影响 观察者ROC研究。为了实现这些目的，我们将收集一个培训案件的数据库，以进行培训和 测试CADE系统，通过设计3D多尺冠状动脉反应来定义搜索空间 增强，分割和动态气球容器跟踪方法，开发独特的容器 - 缝合方法可以自动识别每个单个动脉段的最佳质量阶段 前瞻性或回顾性的ccta考试中可用的阶段，发展创新的船只领域 轮廓分析和容器管腔分析以检测导致狭窄或阳性重塑的NCP， 估计NCP的总体积，并探索校准方法以量化斑块密度 研究。为了证明CADE的实用性，将进行一项临床前读者研究以比较 放射科医生的检测准确性，具有和不带有CADE。 该项目的主要创新包括（1）是自动检测到的第一个CADE系统 不倒数的斑块在内，包括在CCTA中引起阳性重塑或狭窄的斑块，（2）开发新的 机器学习技术在内 增强反应和专门适合冠状动脉树木的动态气球跟踪，（3） 进行首次ROC研究，以评估CADE对放射科医生对NCP的检测的影响。