Screening Lung Cancer by Ultra Low-Dose Computed Tomography

通过超低剂量计算机断层扫描筛查肺癌

• 批准号: 7986503
• 负责人: JEROME Z LIANG
• 金额: $ 32.38万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7986503
• 关键词: American Cancer Society; Back; Biopsy; Breast; Cancer Etiology; Cessation of life; Clinic; Clinical; Clinical Trials; Clinical assessments; Colon; Complex; Computer software; Data; Data Analyses; Data Correlations; Detection; Devices; Diagnosis; Discipline of Nuclear Medicine; Dose; Evaluation; Goals; Gold; Heart; High Resolution Computed Tomography; Image; Journals; Lead; Least-Squares Analysis; Lung; Lung nodule; Malignant Neoplasms; Malignant neoplasm of lung; Manufacturer Name; Measures; Medicine; Modality; Modeling; Modification; Morphologic artifacts; New England; Nodule; Noise; Normal Statistical Distribution; Organ; Patients; Performance; Pilot Projects; Population; Positron-Emission Tomography; Principal Investigator; Probability; Procedures; Process; Property; Protocols documentation; Radiation; Radiology Specialty; Reading; Receiver Operating Characteristics; Recruitment Activity; Reporting; Research; Resolution; Risk; Roentgen Rays; Sampling; Scanning; Screening procedure; Signal Transduction; Slice; Solutions; Source; Speed; Staging; Survival Rate; System; Testing; Time; Tomography, Computed, Scanners; Tube; United States; Variant; Weight; X-Ray Computed Tomography; base; clinical practice; cost; data modeling; digital; distraction; experience; follow-up; image reconstruction; image visualization; improved; lung cancer screening; new technology; novel; prevent; programs; public health relevance; reconstruction; research study; restoration; simulation; single photon emission computed tomography; statistics; treatment strategy; volunteer

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.

描述（申请人提供）：根据美国癌症协会统计，肺癌是美国癌症相关死亡的主要原因，2007年新诊断出212,380例肺癌，死亡160,390例。早期发现肺癌（少于3例） cm）可达到90%的十年生存率。癌症的早期迹象是肺部小结节。目前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《新英格兰医学杂志》最近的一份报告，这种技术具有显着的辐射，可能会增加患癌症的风险高达 2%。除了筛查之外，还进行更多 CT 扫描以进行后续和/或活检程序。 CT制造商一直在尝试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，以便在当前可用的硬件配置上为超低剂量 CT 应用以极低的 mAs 水平重建相似的图像质量。统计成本函数下的迭代图像重建是需要强大计算引擎（成本超过50万美元）的策略之一。通过统计成本函数进行数据恢复后的分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。我们通过人体模型和志愿者实验进行的初步研究表明，后一种恢复策略在减少辐射方面具有巨大潜力，同时保留了当前可用 CT 扫描仪的图像质量和重建速度。进一步探索肺结节筛查潜力的拟议具体目标是：（SA-1）。为了进一步研究自适应噪声治疗策略，以实现肺部筛查尽可能低的 mAs：因为低 mAs CT 数据的第一和第二时刻包含有关噪声的基本统计信息（高阶时刻对降噪的影响较小） ，我们将研究当 mAs 水平降至尽可能低时数据的样本均值和方差的特性。此外，还将研究与断层扫描成像相关的三维空间域中的数据相关性。噪声属性和数据相关性都将被纳入统计成本函数，即 Kharhunen-Lohve 域惩罚加权最小二乘法，可以通过分析方式以最高速度有效地最小化数据恢复。根据恢复的数据重建图像也将以最高的速度进行分析。为了进行比较，将改进类似统计成本函数下的迭代图像重建。 (SA-2)。为了通过检测小肺结节来评估所研究的自适应策略：首先将通过使用噪声分辨率权衡测量和接收器操作特性（ROC）和通道化霍特林轨迹对具有可变低 mAs 协议的拟人模型进行重复实验来评估所提出的策略（ CHT）观察者研究。然后，将对患者肺结节检测进行评估，并与同一 CT 扫描仪当前使用的 mAs 水平进行比较，其中将使用性能等效测试和 ROC 研究进行定量测量。成功评估的策略可能会导致对肺结节进行超低剂量 CT 筛查的大型临床试验，并可能扩展到其他重要器官的筛查，例如结肠、心脏和乳房。 公众健康相关性：目前临床上计算机断层扫描 (CT) 的实践经常使用比需要的更高 mAs 的扫描方案，从而向患者提供过量的 X 射线辐射。当筛查是临床任务（例如筛查肺癌）时，这会引起主要关注。如果 mAs 值降低，图像噪声将会增加，并且可能会出现条纹伪影（因为当前的 CT 扫描仪没有有效的噪声处理方法），从而影响临床评估。该提案旨在通过尽可能降低 mAs 值来降低 X 射线暴露风险，同时保持适合临床任务的图像质量。关键的技术组件是一个软件模块，除了几秒钟的计算时间外，它可以轻松地适应当前的临床 CT 扫描仪，无需任何硬件修改。该模块读取 CT 数据，进行分析，然后在重建数据之前处理数据噪声，从而防止图像噪声和伪影。