Helical CT Reconstruction and Lung Cancer Screening

螺旋CT重建与肺癌筛查

• 批准号: 6929758
• 负责人: Emil Y Sidky
• 金额: $ 8.17万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6929758
• 关键词: bioengineering /biomedical engineering; bioimaging /biomedical imaging; computed axial tomography; computer assisted diagnosis; computer program /software; computer simulation; computer system design /evaluation; image processing; lung neoplasms; model design /development; neoplasm /cancer classification /staging; neoplasm /cancer radiodiagnosis; phantom model

DESCRIPTION (provided by applicant): The goal of this proposal is to develop and evaluate new CT image reconstruction algorithms for the purpose of detecting and characterizing malignant tumors in the lung. New, accurate and efficient algorithms are needed to take advantage of the large cone-angle acceptance of novel detectors that are expected to be employed in future medical CT systems. The developed and existing algorithms will be tested against physical factors such as partial volume averaging, polychromatic x-ray sources, signal noise and x-ray scatter. These physical factors are present in real CT systems and are known to degrade image quality. Algorithms for taking advantage of possible, x-ray energy resolving capability of future detectors will also be explored. Such a capability may alleviate beam-hardening artifacts, and provide additional diagnostic information. One clinical area that the new helical, cone-beam CT technology might be applied to is lung cancer screening. The rapid volume scanning will reduce motion artifacts, and detection of x-rays in different energy windows may assist radiologists in distinguishing malignant and benign nodules in the lung. The developed and existing image reconstruction algorithms will be assessed initially by performance of specific detection and estimation tasks on simple computer-generated phantoms. In order to provide a better means for assessing image reconstruction algorithms for the purpose of lung cancer screening, computer simulated, anthropomorphic chest phantoms, modeling the physical properties of normal and pathological structure of the lung, will be developed. Such phantoms will provide a unique opportunity to assess image reconstruction algorithms, because the data sets will have similar features to actual patient data, the number of generated data sets can be arbitrarily large, and the pathology of each data set is known. Computer-aided diagnosis programs for lung nodule detection may be employed as an observer to assess various image reconstruction and processing algorithms.

描述（由申请人提供）： 该提案的目标是开发和评估新的CT图像重建算法，用于检测和表征肺部恶性肿瘤。需要新的、准确的和有效的算法来利用预期在未来的医学CT系统中采用的新型探测器的大锥角接受度。开发和现有的算法将测试对物理因素，如部分体积平均，多色X射线源，信号噪声和X射线散射。 这些物理因素存在于真实的CT系统中，并且已知会降低图像质量。还将探讨利用未来探测器可能的X射线能量分辨能力的算法。这样的能力可以减轻射束硬化伪影，并且提供附加的诊断信息。新的螺旋锥束CT技术可能应用的一个临床领域是肺癌筛查。 快速体积扫描将减少运动伪影，并且在不同能量窗口中检测X射线可以帮助放射科医师区分肺中的恶性和良性结节。 已开发和现有的图像重建算法将首先通过在简单的计算机生成的幻影上执行特定的检测和估计任务来评估。为了提供一种更好的手段，用于评估图像重建算法的肺癌筛查，计算机模拟，拟人化的胸部模型，模拟肺的正常和病理结构的物理特性，将被开发。这种幻象将提供评估图像重建算法的独特机会，因为数据集将具有与实际患者数据相似的特征，生成的数据集的数量可以任意大，并且每个数据集的病理是已知的。用于肺结节检测的计算机辅助诊断程序可以用作观察者以评估各种图像重建和处理算法。