Dissemination of a Software Platform for Efficient CT Radiation Dose Optimization and Diagnostic Performance Assessment

传播高效 CT 辐射剂量优化和诊断性能评估的软件平台

• 批准号: 10445244
• 负责人: Lifeng Yu
• 金额: $ 31.08万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10445244
• 关键词: Address; Adoption; Classification; Clinical; Collaborations; Communities; Complex; Computer software; Data; Databases; Detection; Development; Diagnosis; Diagnostic; Dose; Educational workshop; Electronic Mail; Encapsulated; Ensure; Evaluation; Exposure to; Funding; Goals; Human; Image; Imaging Phantoms; Knowledge; Laboratories; Lesion; Libraries; Manufacturer Name; Measurement; Measures; Methods; Modeling; Newsletter; Noise; Online Systems; Patient Care; Patients; Performance; Play; Privatization; Protocols documentation; Publications; Radiation; Radiation Dose Unit; Radiation exposure; Reproducibility of Results; Research; Research Personnel; Scanning; Signal Transduction; Software Tools; System; Techniques; Technology; Testing; Translations; United States National Institutes of Health; Validation; Variant; Visual; Work; X-Ray Computed Tomography; base; clinical practice; clinical research site; computerized data processing; deep learning; diagnostic tool; improved; innovation; interest; novel; novel diagnostics; open source; reconstruction; response; symposium; tool; validation studies; web site

PROJECT SUMMARY/ABSTRACT With the introduction of many novel techniques to minimize radiation dose in CT, there is still a large variation in terms of radiation dose levels prescribed in CT exams and therefore a large variation of diagnostic performance. Some patients may receive higher dose than necessary. Some may be under-dosed and mis- diagnosed as a result of insufficient image quality. In order to determine the appropriate amount of radiation dose reduction in each exam, accurate quantification of diagnostic performance is needed so that the dose reduction can be achieved without sacrificing important diagnostic information. However, currently there is a lack of efficient and quantitative tools for objective assessment of diagnostic performance, particularly for many of the novel dose reduction methods involving non-linear processing of the data such as iterative reconstruction and deep-learning-based noise reduction methods. The specific goal of this application is to disseminate a highly automated solution, CT Protocol optimization (CTPro) software, to a wide CT community. This quantitative tool provides an efficient implementation of diagnostic performance assessment and CT radiation dose optimization. This tool is based on channelized Hotelling observer (CHO), which itself was developed decades ago to mimic human observer visual responses in signal detection tasks. However, the use of CHO in clinical CT is quite limited because of a lack of rigorous validation and efficient and robust implementation in practice. We were the first to demonstrate its correlation with human observer performance in low-contrast detection, classification and localization tasks in clinical CT. The main objective of the current proposal is to optimize this tool for simplicity and robustness, and disseminate it to CT researchers and clinical users, which will be accomplished through 3 specific aims: Aim 1: Optimize CTPro for simplicity, robustness, and generalizability. Aim 2: Develop an open-source web-based platform for software dissemination. Aim 3: Build use cases and disseminate CTPro. The proposed work is significant because the software tool will allow any CT users and researchers to perform CT radiation dose optimization and diagnostic performance evaluation in an efficient, quantitative, and objective manner. This work is innovative in that the automated tool will use quantitative measures of diagnostic performance to systematically guide the complex task of CT dose optimization, moving beyond traditional metrics that are inappropriate for many novel dose reduction techniques. The software tool, once widely employed, will facilitate a paradigm shift in how dose optimization and the evaluation of dose reduction techniques are performed, and will allow a more rapid and consistent adoption of dose reduction technology into clinical practice, which will benefit millions of CT patients.

项目总结/摘要 随着许多新技术的引进，以尽量减少辐射剂量的CT，仍然有很大的变化 在CT检查中规定的辐射剂量水平方面， 性能一些患者可能会接受比必要剂量更高的剂量。有些可能是剂量不足和错误的- 诊断为图像质量不足。为了确定合适的辐射量 在每次检查中减少剂量，需要准确量化诊断性能， 可以在不牺牲重要诊断信息的情况下实现减少。然而，目前有一个 缺乏客观评估诊断性能的有效和定量工具，特别是对许多 涉及数据的非线性处理的新型剂量减少方法， 重建和基于深度学习的降噪方法。 该应用程序的具体目标是传播高度自动化的解决方案，即CT协议优化 （CTPro）软件，以广泛的CT社区。这个定量工具提供了一个有效的实施， 诊断性能评估和CT辐射剂量优化。此工具基于通道化 霍特林观察者（CHO），它本身是几十年前开发的，以模仿人类观察者的视觉反应 在信号检测任务中。然而，CHO在临床CT中的使用是相当有限的，因为缺乏严格的 在实践中进行有效和稳健的实施。我们是第一个证明其相关性的人 在临床CT中的低对比度检测、分类和定位任务中具有人类观察者的性能。 目前建议的主要目标是优化这一工具，使其简单和稳健， 将其传播给CT研究人员和临床用户，这将通过3个具体目标来实现： 目标1：优化CTPro以实现简单性、鲁棒性和通用性。 目标2：开发一个基于网络的开放源码软件传播平台。 目标3：构建用例并传播CTPro。 拟议的工作是重要的，因为该软件工具将允许任何CT用户和研究人员执行 CT辐射剂量优化和诊断性能评价， 客观的方式。这项工作是创新的，因为自动化工具将使用定量措施， 诊断性能，以系统地指导CT剂量优化的复杂任务， 传统的度量不适合于许多新的剂量减少技术。该软件工具，一旦 广泛使用，将促进如何剂量优化和剂量减少的评价范式转变 这些技术的实施将使人们能够更迅速、更一致地采用剂量减少技术 这将使数百万CT患者受益。