Computer-Aided Detection of Pulmonary Embolism on CT Pulmonary Angiography

描述(由申请人提供)：肺栓塞(PE)是美国的主要死亡原因，如果不治疗的话。及时诊断和治疗可以大大降低该病的死亡率和发病率。CT肺血管造影(CTPA)是临床诊断PE的有效手段。解释肺栓塞的CT扫描需要放射科医生的广泛阅读努力，他必须在视觉上追踪肺部的大量血管，以发现可疑的肺栓塞。尽管做出了这些努力，但据报道，敏感性在53%到100%之间。PIOPED II研究的初步结果表明，多检测器CTPA的灵敏度为83%。计算机辅助诊断(CAD)是提高CTPA图像中PE检测的灵敏度和效率以及减少观察者间变异性的可行方法。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在CTPA图像的2D切片和3D体积再现显示上的可疑位置来充当第二意见。我们将发展先进的计算机视觉技术来增强血管特征，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，识别真实PE。这些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)开发增强血管特征的图像预处理方法；(2)开发一种新的滚动气球技术结合结构分析来准确跟踪血管，包括部分或完全被PE阻塞的血管；(3)开发用于在不同水平的动脉分支上识别可疑PE的多预筛选方法，特别是对于小段动脉中的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的、线性或神经网络分类器的假阳性减少方法，(6)探索PE的计算机检测性能评价方法；(7)开展观察者ROC研究，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，存在大量的PE假阴性诊断。CAD有可能减少漏诊的PE，增加患者得到及时治疗的机会，从而降低死亡率和加快从这种情况中恢复过来。

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.