Helical CT Reconstruction and Lung Cancer Screening

描述(由申请人提供)： 该方案的目的是开发和评估新的CT图像重建算法，以检测和表征肺部恶性肿瘤。需要新的、准确的和高效的算法来利用未来医用CT系统中有望采用的新型探测器的大锥角接受度。开发的和现有的算法将针对物理因素进行测试，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。还将探索利用未来探测器可能的X射线能量分辨能力的算法。这种能力可以减轻射束硬化伪影，并提供额外的诊断信息。新的螺旋锥束CT技术可能应用于肺癌筛查的一个临床领域。快速的体积扫描将减少运动伪影，检测不同能量窗中的X射线可以帮助放射科医生区分肺部的良恶性结节。 开发和现有的图像重建算法最初将通过对简单的计算机生成的模体执行特定的检测和估计任务来进行评估。为了更好地评估用于肺癌筛查的图像重建算法，将开发计算机模拟的、拟人化的胸部模型，模拟肺部正常和病理结构的物理属性。这种幻影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都是已知的。用于肺结节检测的计算机辅助诊断程序可以被用作评估各种图像重建和处理算法的观察者。

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.