Computer-Aided Detection of Pulmonary Embolism on CT Pulmonary Angiography

描述（由申请人提供）：在美国，肺栓塞（PE）如果不治疗是导致死亡的主要原因。及时诊断和治疗可显著降低该病的死亡率和发病率。计算机断层肺血管造影（CTPA）已被报道为PE临床诊断的有效手段。对CT扫描的PE进行解释需要放射科医生进行大量的阅读工作，放射科医生必须通过视觉跟踪肺部大量血管来检测可疑的PE。尽管做出了努力，但据报道，敏感性从53%到100%不等。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）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可变性。拟议项目的总体目标是开发一个强大的CAD系统，该系统可以提供CTPA扫描上的PE系统筛选，并通过自动提醒放射科医生在CTPA图像的2D切片和3D体积渲染显示上的可疑位置，作为第二意见。我们将发展先进的计算机视觉技术，增强血管特征，自动提取肺血管，重建血管树，检测候选PE，将PE与正常肺结构区分开来，并识别真正的PE。该技术将专门用于分析CTPA图像上复杂的血管结构。该项目的具体目标包括：(1)发展图像预处理方法，增强血管特征；(2)发展新的滚动球囊技术，结合结构分析，准确跟踪血管，包括PE部分或完全闭塞的血管；(3)发展多重预筛选方法，识别不同水平动脉分支的可疑PE，特别是小亚节段动脉的PE；(4)分析PE特征，开发分类方法；(5)开发基于特征分析和模糊规则、线性或神经网络分类器的假阳性减少方法；(6)探索计算机检测PE的性能评估方法；(7)进行观察者ROC研究，评估CAD对放射科医生PE诊断准确性的影响。这项研究与公共卫生的相关性在于，PEs存在大量假阴性诊断。CAD有可能减少漏诊的pe，提高患者得到及时治疗的机会，从而降低死亡率，加快患者的康复。

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.