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: 8068819
• 负责人: JEROME Z LIANG
• 金额: $ 31.41万
• 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/8068819
• 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; cost effective; 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（由申请人提供）：根据美国癌症协会的统计，肺癌是美国癌症相关死亡的主要原因，2007年有212,380例新诊断病例和160390例死亡。早期发现肺癌（小于3厘米）可达到90%的十年生存率。肺癌的早期症状是小的肺结节。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《新英格兰医学杂志》（the New England Journal of Medicine）最近的一篇报道，这种扫描带有明显的辐射，可能会使患癌症的风险增加2%。除筛查外，更多的CT扫描用于随访和/或活检程序。CT制造商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，以在目前可用的硬件配置下，在低mAs水平下重建类似的图像质量。在统计代价函数下的迭代图像重建是需要强大的计算引擎（成本超过50万美元）的策略之一。利用统计代价函数进行数据恢复后的分析图像重建是另一种策略，它产生的结果与迭代方法相似，并且大大减少了计算负担。我们通过模拟实验和志愿者实验进行的试点研究表明，后一种恢复策略在减少辐射的同时，在现有CT扫描仪上保持图像质量和重建速度的巨大潜力。为进一步探索筛查肺结节的潜力，提出的具体目标是：（SA-1）。为了进一步研究肺筛查中尽可能低mAs的自适应噪声处理策略：由于低mAs CT数据的第一阶矩和第二阶矩包含有关噪声的基本统计信息（高阶矩对降噪的影响较小），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性质。此外，将研究与层析成像相关的三维空间域的数据相关性。噪声特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，可以通过分析方式以最高速度有效地最小化数据恢复。从恢复的数据中重建的图像也将以最快的速度进行分析。为了便于比较，我们将细化相似统计代价函数下的迭代图像重建。(2)。为了通过检测小肺结节来评估所研究的自适应策略：所提出的策略将首先通过使用噪声分辨率权衡测量和接受者工作特征（ROC）以及信道化霍特林迹（CHT）观察者研究，在具有可变低mAs协议的拟人模型上进行重复实验来评估。然后评估患者肺结节的检测，并与目前使用的同一台CT扫描仪的mAs水平进行比较，其中将使用性能等效检验和ROC研究进行定量测量。成功评估的策略可能会导致大规模的超低剂量CT筛查肺结节的临床试验，并可扩展到筛查其他重要器官，如结肠、心脏和乳房。