Dual Energy Chest Radiography CAD

双能胸部放射线摄影 CAD

• 批准号: 6790279
• 负责人: Robert Alvarez
• 金额: $ 34.7万
• 依托单位: APREND TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2006-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6790279
• 关键词: bioimaging /biomedical imaging; calcification; clinical research; computer assisted diagnosis; computer network; computer program /software; computer system design /evaluation; diagnosis design /evaluation; digital imaging; early diagnosis; electron optics; hospital equipment /supply; human data; image enhancement; lung neoplasms; neoplasm /cancer radiodiagnosis; thoracic radiography

DESCRIPTION (provided by applicant): The long-term objective of our proposed research is to improve the early detection of lung cancer with chest radiography. Because chest radiography is used for such a wide variety of medical conditions, it is, by far, the most widely used diagnostic imaging examination with over 30 million exams per year. Radiologists scan these images for all indications of disease, including lung cancer nodules, not just for the initial reason for the examination. Unfortunately, the fraction of nodules missed is quite high, over 30% in many studies. Our specific aim is to develop a computer-assisted detection (CAD) system to reduce the miss rate. The system uses low-noise, dual energy subtraction images, which have significant advantages for CAD yet there has been little research to exploit their unique characteristics. This is the opportunity for technological innovation addressed by our research. Dual energy provides images that eliminate ribs, a major contributor to errors in conventional CAD. It also provides images that may be used to measure nodule calcification, an important factor in malignant vs. benign diagnosis. Recently introduced digital, flat-panel x-ray systems provide dual energy images with lower noise than previous approaches. These lower noise images may improve detection of smaller, early-stage cancers. In Phase I, we showed the feasibility of our approach by developing new methods to utilize dual energy information in CAD. We developed a method for detection of potential nodules with high sensitivity and much lower extraneous response. We also developed a method for locating the lung fields in a chest image with higher accuracy and sensitivity than previous methods. Another innovation was a method of characterizing nodules based on a statistical technique called eigenimages that derives information directly from nodule images and whose accuracy increases as more nodule images become available. In Phase II, we will build on these methods to develop algorithms for the other components of the CAD system. We will study methods to incorporate these algorithms in a network application prototype to provide CAD services to multiple digital x-ray systems connected to a modern hospital information network. We will use the prototype to conduct a small-scale observer study to compare the performance of radiologists using our CAD system with unaided radiologists.

描述（由申请人提供）： 我们所提出的研究的长期目标是提高胸部X线摄影对肺癌的早期发现。由于胸部X线摄影用于各种各样的医疗条件，它是迄今为止使用最广泛的诊断成像检查，每年有超过3000万次检查。放射科医生扫描这些图像的所有迹象的疾病，包括肺癌结节，而不仅仅是最初的原因检查。不幸的是，漏诊结节的比例相当高，在许多研究中超过30%。我们的具体目标是开发一个计算机辅助检测（CAD）系统，以减少失误率。该系统使用低噪声，双能量减影图像，这对CAD有显着的优势，但很少有研究，以利用其独特的特点。这是我们的研究所解决的技术创新的机会。双能量提供消除肋骨的图像，这是传统CAD中错误的主要原因。它还提供可用于测量结节钙化的图像，这是恶性与良性诊断的重要因素。最近推出的数字平板X射线系统提供了比以前的方法噪声更低的双能量图像。这些低噪声图像可以改善对较小的早期癌症的检测。在第一阶段，我们通过开发新方法来利用CAD中的双能量信息，展示了我们方法的可行性。我们开发了一种检测潜在结节的方法，具有高灵敏度和低得多的外来反应。我们还开发了一种方法，用于定位肺部领域在胸部图像具有更高的准确性和灵敏度比以前的方法。另一项创新是根据称为特征图像的统计技术确定结节特征的方法，该方法直接从结节图像中获取信息，其准确性随着结节图像的增多而提高。在第二阶段，我们将在这些方法的基础上开发用于CAD系统其他组件的算法。我们将研究的方法，将这些算法在一个网络应用原型，提供CAD服务，多个数字X射线系统连接到一个现代化的医院信息网络。我们将使用原型进行小规模的观察者研究，比较放射科医生使用我们的CAD系统与独立的放射科医生的性能。