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

基本信息

批准号：
8719101
负责人：
Cynthia H McCollough
金额：
$ 82.89万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
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/8719101
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

项目摘要

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，但是为了患者安全，CT提供商必须使用最低剂量的辐射剂量，与实现所需的诊断性能一致。图像重建的新算法方法对于减少剂量而不会损害图像质量至关重要。但是，由于许多图像科学家无法从患者考试中访问CT投影数据，因此可以开发新的图像重建方法。我们建议开发数据集，指标和软件工具，以帮助调查人员创建和比较减少剂量的新方法，并将指导临床用户选择优化的扫描参数和重建方法。在AIM 1中，我们将在删除专有信息后以标准化格式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，重建图像和临床参考信息（诊断和位置的验证，抽象的患者历史记录）以及模拟较低暴露水平的数据。这些数据集将大大扩展可以开发和评估算法的研究人员池，并允许对竞争方法进行比较。测量诊断性能的黄金标准，即观察者性能研究，但是非常昂贵且耗时。在AIM 2中，我们将开发高度自动化，交互式和自由使用的软件工具，这些工具将有助于快速完成观察者绩效研究，以便有效而有意义地比较替代扫描协议和重建方法。尽管如此，由于技术创新的速度迅速，对于有效的观察者绩效研究的替代品对于迅速将剂量减少的进步转化为患者护理至关重要。尽管使用模型观察者的基于任务的图像质量指标为此目的具有吸引力，但尚未证明它们与临床CT成像中的放射科医生的性能相关。在AIM 3中，我们将确定模型观察者与人类观察者在患者数据中基本相关的三个常见诊断任务以及线性和非线性图像重建技术的模型观察者。最后，需要定量方法来协助临床实践选择扫描协议参数，以使用最低的辐射剂量达到所需的诊断性能水平。在AIM 4中，我们将开发工具来有效，定量优化针对特定诊断任务的CT扫描协议。这些工具将通过轻松执行的幻影扫描来计算基于任务的图像质量的定量度量，并建议使用最低的辐射剂量实现最接近所需诊断性能水平的扫描协议参数。这项研究具有很高的创新性和重要意义，因为它将为CT社区提供新颖的数据，方法和软件工具，以进行客观评估和有效优化扫描协议参数和新兴剂量减少方法。