Computer-Aided Detection of Pulmonary Embolism on CT Pulmonary Angiography

CT 肺血管造影计算机辅助检测肺栓塞

• 批准号: 7229841
• 负责人: CHUAN ZHOU
• 金额: $ 19.46万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7229841
• 关键词: 3-Dimensional; Algorithms; Angiography; Archives; Arteries; Benign; Biological Neural Networks; Blood Vessels; Cause of Death; Characteristics; Classification; Complex; Computer Assisted; Computer Vision Systems; Computer-Assisted Diagnosis; Condition; Data Set; Database Management Systems; Databases; Detection; Development; Diagnosis; Evaluation; Evaluation Methodology; Evaluation Studies; Goals; Gold; Image; Image Analysis; Interobserver Variability; Location; Lung; Malignant - descriptor; Methods; Morbidity - disease rate; Numbers; Patients; Performance; Phase; Public Health; Pulmonary Embolism; Pulmonary vessels; Range; Rate; Reading; Recovery; Reporting; Research; Scanning; Scheme; Screening procedure; Second Opinions; Slice; Speed; Standards of Weights and Measures; Structure; System; Techniques; Testing; Three-Dimensional Image; Three-Dimensional Imaging; TimeLine; Training; Trees; United States; X-Ray Computed Tomography; base; clinical Diagnosis; computerized; design; detector; improved; mortality; programs; radiologist

DESCRIPTION (provided by applicant): Pulmonary embolism (PE) is a leading cause of death in the United States if untreated. Prompt diagnosis and treatment can dramatically reduce the mortality rate and morbidity of the disease. Computed tomographic pulmonary angiography (CTPA) has been reported to be an effective means for clinical diagnosis of PE. Interpretation of a CT scan for PE demands extensive reading efforts from a radiologist who has to visually track a large number of vessels in the lungs to detect suspected PEs. Despite the efforts, the sensitivities were reported to range from 53% to 100%. Preliminary results from the PIOPED II study indicated a sensitivity of 83% by multi-detector CTPA. Computer-aided diagnosis (CAD) can be a viable approach to improving the sensitivity and efficiency of PE detection in CTPA images, as well as reducing inter-observer variability. The overall goal of the proposed project is to develop a robust CAD system that can provide a systematic screening of PE on CTPA scans and serve as a second opinion by automatically alerting the radiologists to suspicious locations on 2D slice and 3D volume rendering display of the CTPA images. We will develop advanced computer vision techniques to enhance the characteristics of vessels, automatically extract the pulmonary vessels, reconstruct the vessel tree, detect candidate PEs, differentiate PE from normal pulmonary structures, and identify the true PEs. The techniques will be specifically designed for analysis of the complex vascular structures on CTPA images. The specific aims of this project include: (1) developing image preprocessing method to enhance vessel characteristics, (2) developing a new rolling balloon technique in combination with structure analysis to track vessels accurately, including vessels partially or completely occluded by PEs, (3) developing multi-prescreening method for the identification of suspicious PEs at different levels of artery branches, especially for PEs in small subsegmental arteries, (4) analyzing PE features for development of classification methods, (5) developing false positive reduction method based on feature analysis and fuzzy rule-based, linear, or neural network classifiers, (6) exploring performance evaluation methodology for computerized detection of PEs, and (7) performing observer ROC study to evaluate the effects of CAD on radiologists' accuracy in PE diagnosis. The relevance of this research to public health lies in the fact that there is substantial false-negative diagnosis of PEs. CAD will potentially reduce missed PEs and improve the chance of timely treatment of patients, thus reducing the mortality rate and speed up recovery from this condition.

描述（由申请人提供）：肺栓塞（PE）是美国未经治疗的主要死亡原因。及时的诊断和治疗可以大大降低该病的死亡率和发病率。CT肺动脉造影（CTPA）已被报道是临床诊断PE的有效手段。PE的CT扫描的解释需要放射科医生进行大量的阅读，放射科医生必须在视觉上跟踪肺部的大量血管以检测可疑的PE。尽管作出了努力，但据报告，敏感性范围为53%至100%。PIOPED II研究的初步结果表明，多探测器CTPA的灵敏度为83%。计算机辅助诊断（CAD）是一种可行的方法，可以提高CTPA图像中PE检测的灵敏度和效率，并减少观察者之间的差异。拟议项目的总体目标是开发一个强大的CAD系统，该系统可以在CTPA扫描上提供系统的PE筛查，并通过自动提醒放射科医生CTPA图像的2D切片和3D体积渲染显示上的可疑位置来作为第二意见。我们将开发先进的计算机视觉技术，以增强血管的特征，自动提取肺血管，重建血管树，检测候选PE，区分PE与正常肺结构，并识别真正的PE。该技术将专门设计用于分析CTPA图像上的复杂血管结构。该项目的具体目标包括：（1）开发图像预处理方法以增强血管特征，（2）开发结合结构分析的新的滚动气球技术以精确地跟踪血管，包括被PE部分或完全闭塞的血管，（3）开发用于识别不同水平动脉分支处的可疑PE的多重预筛选方法，特别是对于小亚段动脉中的PE，（4）分析PE特征以开发分类方法，（5）开发基于特征分析和基于模糊规则、线性或神经网络分类器的假阳性减少方法，（6）探索用于PE计算机检测的性能评估方法，（7）通过观察者ROC分析，评价CAD对放射科医师PE诊断准确性的影响。这项研究与公共卫生的相关性在于，PE的假阴性诊断存在大量。CAD将潜在地减少漏诊的PE并提高患者及时治疗的机会，从而降低死亡率并加速从这种疾病中恢复。