Screening Lung Cancer by Ultra Low-Dose Computed Tomography

描述(申请人提供)：根据美国癌症协会的统计数据，肺癌是美国癌症相关死亡的主要原因，2007年确诊的新病例中有212,380例，死亡160,390例。早期发现肺癌(小于3厘米)可以达到90%的十年生存率。癌症的早期征兆是小的肺结节。目前对肺结节的筛查是通过高分辨率计算机断层扫描(CT)进行的，根据最近发表在《新英格兰医学杂志》上的一份报告，高分辨率计算机断层扫描(CT)具有显著的辐射，可能会使患癌症的风险增加高达2%。除了筛查外，还会进行更多的CT扫描，以进行后续和/或活组织检查。CT制造商一直试图通过硬件优化和软件增强来降低辐射风险。我们一直在探索自适应噪声处理策略，以在当前可用的硬件配置上，以显著低的mAs水平重建类似的图像质量，用于超低剂量CT应用。统计代价函数下的迭代图像重建是一种需要强大计算引擎的策略之一(耗资50万美元以上)。通过统计代价函数进行数据恢复后的解析图像重建是另一种策略，它产生与迭代方法类似的结果，并且大大减少了计算负担。我们通过体模和志愿者实验进行的初步研究表明，后一种恢复策略在保持现有CT扫描仪的图像质量和重建速度的同时，具有很大的减少辐射的潜力。拟议的进一步探索肺结节筛查潜力的具体目标是：(SA-1)。为了进一步研究肺部筛查中尽可能低mAs的自适应噪声处理策略：由于低mAs CT数据的一阶和二阶矩包含关于噪声的基本统计信息(高阶矩对降噪影响较小)，我们将研究当mAs水平尽可能低时数据的样本均值和方差的特性。此外，还将研究与断层成像相关的三维空间域中的数据相关性。将噪声特性和数据相关性都纳入到统计代价函数中，即Kharhunen-LOHVE域惩罚加权最小二乘，该函数可以通过分析的方式以最快的速度有效地最小化数据恢复。从恢复的数据重建图像也将以最快的速度进行分析。为了比较的目的，在相似的统计代价函数下的迭代图像重建将被改进。(SA-2)。为了通过检测小结节来评估所研究的自适应策略：首先将使用噪声分辨率权衡测量和接收器工作特性(ROC)和信道化霍特林迹(CHT)观察者研究，通过对具有可变低mAs协议的拟人幻象的重复实验来评估所提出的策略。然后，将评估患者肺结节的检测情况，并与目前使用的相同CT扫描仪的mAs水平进行比较，其中将使用性能等价性测试和ROC研究进行定量测量。成功评估的策略可能导致超低剂量CT筛查肺结节的大型临床试验，并可能扩展到其他重要器官的筛查，如结肠、心脏和乳房。 公共卫生相关性：目前临床上使用的计算机断层扫描(CT)经常通过使用比所需更高的mAs扫描方案向患者提供过量的X射线辐射。当筛查是临床任务时，这是一个主要的问题，例如，筛查肺癌。如果mAs值降低，图像噪声将增加，可能会出现条纹伪影(因为目前的CT扫描仪没有有效的噪声处理)，从而影响临床评估。这项建议旨在通过将mas值降低到尽可能低的水平来降低X射线暴露风险，同时保持适合临床任务的图像质量。关键的技术部分是一个软件模块，它可以很容易地被现有的临床CT扫描仪适配，除了几秒钟的计算时间外，不需要任何硬件修改。该模块读取CT数据，在重建数据之前对数据噪声进行分析和处理，防止图像噪声和伪影。

DESCRIPTION (provided by applicant): According to American Cancer Society statistics, lung cancer is the leading cause of cancer-related death in the United States, 212,380 of new cases diagnosed and 160,390 deaths in 2007. Early detection of lung cancers (less than 3 cm) can achieve a 90% ten-year survival rate. Early sign of the cancer is small lung nodules. Current screening of the lung nodules is performed by high-resolution computed tomography (CT), which carries a significant radiation and could increase the risk of getting cancer by as high as 2% according to a recent report in The New England Journal of Medicine. In addition to the screening, more CT scans are performed for follow-up and/or biopsy procedures. Reducing the radiation risk has been attempted by CT manufacturers by both hardware optimization and software enhancement. We have been exploring adaptive noise-treatment strategies to reconstruct similar image quality at significantly low mAs level for ultra low-dose CT applications on currently available hardware configuration. Iterative image reconstruction under a statistical cost function is one of the strategies which needs powerful computing engine (costs more than a half million dollars). Analytical image reconstruction after data restoration by a statistical cost function is another strategy which generates similar results as the iterative means with a dramatic reduction of computing burden. Our pilot studies by both phantom and volunteer experiments have demonstrated great potential of the latter restoration strategy for radiation reduction while retaining the image quality and reconstruction speed on currently available CT scanners. The proposed specific aims to further explore the potential for screening lung nodules are: (SA-1). To further investigate the adaptive noise-treatment strategies toward as low mAs as achievable for lung screening: Because the first and second moments of low-mAs CT data contain the essential statistical information about the noise (higher order moments have less impact on noise reduction), we will study the properties of sample mean and variance of the data as mAs level goes down as low as achievable. In addition, data correlations in the three-dimensional spatial domain associated with tomographic imaging will be investigated. Both the noise properties and data correlation will be incorporated into a statistical cost function, i.e., Kharhunen-Lohve domain penalized weighted least-squares, which can be efficiently minimized for data restoration by an analytical fashion at the highest speed. Image reconstruction from the restored data will also be analytical at the highest speed. For comparison purpose, iterative image reconstruction under a similar statistical cost function will be refined. (SA-2). To evaluate the investigated adaptive strategies by the detection of small lung nodules: The presented strategies will be first evaluated by repeated experiments on anthropomorphic phantoms with variable low mAs protocols using noise-resolution tradeoff measure and receiver operating characteristics (ROC) and channelized Hotelling trace (CHT) observer studies. Then the evaluation will be on patient lung nodule detection with comparison to currently-used mAs level by a same CT scanner, where quantitative measures will be made using performance equivalence tests and ROC studies. The successfully evaluated strategies may lead to a large clinical trial for ultra low-dose CT screening of the lung nodules, and could be extended to screening of other vital organs, such as the colon, heart, and breasts. PUBLIC HEALTH RELEVANCE: Current practice of computed tomography (CT) in clinic frequently delivers excessive X-ray radiation to the patients by using a higher mAs scanning protocol than needed. This causes a major concern when screening is the clinical task, e.g., screening lung cancer. If the mAs value is lowered, image noise will increase and streak artifacts may present (because there is no effective noise treatment in current CT scanners), compromising the clinical assessment. This proposal aims to reduce the X-ray exposure risk by lowering the mAs value as low as achievable, while retaining the image quality suitable to the clinical task. The key technical component is a software module which can be easily adapted by current clinical CT scanners without any hardware modification except for a few seconds of computing time. The module reads in CT data, analyzes and then treats the data noise prior to reconstructing the data, preventing image noise and artifact.