Critical resources to evaluate CT scan techniques and dose reduction approaches

评估 CT 扫描技术和剂量减少方法的关键资源

• 批准号: 8550930
• 负责人: Cynthia H McCollough
• 金额: $ 90.68万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8550930
• 关键词: Accounting; Algorithms; Clinical; Communities; Computer software; Data; Data Set; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Disease; Documentation; Dose; Ensure; Etiology; Evaluation; Excision; Gold; Human; Image; Ionizing radiation; Link; Location; Low Dose Radiation; Maps; Measures; Medical; Methods; Metric; Modeling; National Institute of Biomedical Imaging and Bioengineering; Noise; Output; Pathology; Patient Care; Patients; Performance; Physicians; Protocols documentation; Provider; Reader; Recording of previous events; Reference Standards; Research; Research Personnel; Resources; Risk; Scanning; Scientist; Simulate; Software Tools; Techniques; Time; Translating; Translations; Validation; Work; X-Ray Computed Tomography; abstracting; base; clinical practice; clinically relevant; diagnostic accuracy; image reconstruction; innovation; meetings; new technology; novel; novel strategies; patient safety; public health relevance; radiologist; reconstruction; software development; standard measure; tool

DESCRIPTION (provided by applicant): Computed tomography (CT) provides important medical benefits, but for patient safety it is essential that CT providers use the lowest dose of radiation consistent with achieving the needed diagnostic performance. New algorithmic approaches to image reconstruction will be critical to reducing the dose without compromising image quality; however, the development of novel approaches to image reconstruction is hampered because many image scientists do not have access to CT projection data from patient exams. We propose to develop data sets, metrics, and software tools that will help investigators create and compare new approaches to dose reduction and will guide clinical users in selecting optimized scanning parameters and reconstruction methods. In Aim 1, we will make reference patient data sets available to researchers in a standardized format after removal of proprietary information. These data will include projection data, statistical noise maps, reconstructed images, and clinical reference information (validation of diagnosis and location, abstracted patient history) for common CT exams, as well as data simulating lower exposure levels. These data sets will greatly expand the pool of researchers that can develop and evaluate algorithms, and will permit comparison of competing approaches. The gold standard for measuring diagnostic performance, observer performance studies, is however very expensive and time consuming. In Aim 2, we will develop highly automated, interactive, and freely available software tools that will facilitate rapid completion of observer performance studies in order to efficiently and meaningfully compare alternative scanning protocols and reconstruction methods. Still, because of the rapid pace of technical innovation, a substitute for efficient observer performance studies is essential to rapidly translate advances in dose reduction into patient care. Although task-based image quality metrics using model observers are attractive for this purpose, they have not been demonstrated to correlate with radiologist performance in clinical CT imaging. In Aim 3, we will determine model observers that are substantially correlated with human observer performance in patient data for three common diagnostic tasks and for linear and non-linear image reconstruction techniques. Finally, quantitative methods are needed to assist clinical practices in choosing scanning protocol parameters that will achieve the required level of diagnostic performance using the lowest radiation dose. In Aim 4, we will develop tools to efficiently and quantitatively optimize CT scanning protocols for specific diagnostic tasks. These tools will calculate quantitative measures of task-based image quality from easily performed phantom scans and will recommend scanning protocol parameters that will deliver the closest match to the desired level of diagnostic performance using the lowest radiation dose. This research is highly innovative and significant in that it will provide the CT community with novel data, methods, and software tools for objective evaluation and efficient optimization of scanning protocol parameters and emerging dose reduction approaches.

描述（由申请人提供）：计算机断层扫描（CT）提供了重要的医疗益处，但为了患者安全，CT提供者必须使用与实现所需诊断性能一致的最低辐射剂量。图像重建的新算法方法对于在不影响图像质量的情况下减少剂量至关重要;然而，由于许多图像科学家无法获得患者检查的CT投影数据，因此图像重建新方法的开发受到阻碍。我们建议开发数据集、指标和软件工具，以帮助研究人员创建和比较新的剂量降低方法，并指导临床用户选择优化的扫描参数和重建方法。在目标1中，我们将在删除专有信息后，以标准化格式向研究人员提供参考患者数据集。这些数据将包括投影数据、统计噪声图、重建图像和常见CT检查的临床参考信息（诊断和位置确认、患者病史摘要），以及模拟较低暴露水平的数据。这些数据集将极大地扩大能够开发和评估算法的研究人员队伍，并将允许对竞争方法进行比较。然而，测量诊断性能的黄金标准，观察者性能研究，是非常昂贵和耗时的。在目标2中，我们将开发高度自动化、交互式和免费可用的软件工具，这些工具将促进快速完成观察者性能研究，以便高效且有意义地比较替代扫描协议和重建方法。尽管如此，由于技术创新的快速步伐，有效的观察者性能研究的替代品是必不可少的，以迅速将剂量减少的进步转化为病人护理。虽然基于任务的图像质量指标，使用模型观察员是有吸引力的，用于此目的，他们还没有被证明与放射科医生在临床CT成像的性能。在目标3中，我们将确定与患者数据中的人类观察者表现基本相关的模型观察者，用于三种常见的诊断任务以及线性和非线性图像重建技术。最后，需要定量方法来帮助临床实践选择扫描协议参数，使用最低的辐射剂量达到所需的诊断性能水平。在目标4中，我们将开发工具来有效地和定量地优化特定诊断任务的CT扫描协议。这些工具将从容易执行的体模扫描中计算基于任务的图像质量的定量测量，并将推荐扫描协议参数，这些参数将使用最低辐射剂量提供与所需诊断性能水平最接近的匹配。这项研究具有高度创新性和重要意义，因为它将为CT界提供新的数据，方法和软件工具，用于客观评估和有效优化扫描协议参数和新兴的剂量降低方法。