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: 8517444
• 负责人: JEROME Z LIANG
• 金额: $ 29.52万
• 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/8517444
• 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; Signal Transduction; Slice; Solutions; Source; Speed; Staging; Survival Rate; System; Testing; Time; Tomography, Computed, Scanners; Tube; United States; Variant; Weight; X-Ray Computed Tomography; abstracting; 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; reconstruction; research study; restoration; screening; simulation; single photon emission computed tomography; statistics; treatment strategy; volunteer

Principal Investigators/Program Directors (Last, First, Middle): Liang, Jerome Z. Project Summary/Abstract 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-Lo¿ve 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. PHS 398 (Rev. 04/06) Page __1___ Continuation Format Page

主要研究者/项目负责人（最后、第一、中间）：Liang，杰罗姆Z. 项目总结/摘要 根据美国癌症协会的统计，肺癌是癌症相关死亡的主要原因， 在美国，2007年有212，380例新确诊病例和160，390例死亡。肺的早期发现 小于3厘米的癌症可以达到90%的十年存活率。癌症的早期症状是小肺 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， 据报道，这种物质含有大量的辐射，患癌症的风险可能会增加2%。 最近发表在《新英格兰医学杂志》上的一篇报道。除了筛查，更多的CT扫描是 进行随访和/或活检程序。降低辐射风险的尝试已通过CT 制造商的硬件优化和软件增强。我们一直在探索适应性 噪声处理策略，以超低剂量在显著低mAs水平下重建相似的图像质量 当前可用硬件配置上的CT应用程序。下的迭代图像重建 统计代价函数是一种需要强大计算引擎的策略（成本超过一半 百万美元）。另一种是通过统计代价函数进行数据恢复后的分析图像重建 该策略产生与迭代方法类似的结果，但显著减少了计算负担。 我们通过体模和志愿者实验进行的初步研究表明，后者具有巨大的潜力 在保持图像质量和重建速度的同时减少辐射的恢复策略 现有的CT扫描仪。建议的具体目的是进一步探索筛查肺结核的潜力。 结节是： （SA-1）。进一步研究尽可能低mAs的自适应噪声治疗策略 对于肺部筛查： 由于低mAs CT数据的一阶矩和二阶矩包含了最基本的统计信息， 关于噪声（高阶矩对降噪的影响较小），我们将研究 随着mAs水平降至尽可能低的水平，数据的样本平均值和方差。此外，数据 将研究与层析成像相关的三维空间域中的相关性。 噪声属性和数据相关性都将被并入统计成本函数中，即， Kharhunen-Lo？ve域惩罚加权最小二乘，可以有效地最小化数据 以最快的速度通过分析方式进行恢复。从恢复的数据的图像重建也将 以最快的速度进行分析。为了比较的目的，迭代图像重建下类似的 将改进统计成本函数。 （SA-2）。通过检测肺小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对拟人幻影的重复实验进行评估 使用噪声分辨率折衷测量和接收机工作特性的可变低mAs协议 (ROC)和信道化霍特林迹（CHT）观察者研究。然后对患者肺部进行评估 结节检测与当前使用的相同CT扫描仪的mAs水平进行比较，其中定量 将使用性能等效性检验和ROC研究进行测量。成功评估的 这些策略可能会导致肺结节超低剂量CT筛查的大型临床试验， 扩展到其他重要器官的筛查，如结肠、心脏和乳房。 PHS 398（修订版04/06）第__1_续格式页