mIQa: A Highly Scalable and Customizable Platform for Medical Image Quality Assessment - Phase II

mIQa：高度可扩展和可定制的医学图像质量评估平台 - 第二阶段

• 批准号: 10010814
• 负责人: Aashish Chaudhary
• 金额: $ 81.93万
• 依托单位: KITWARE, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-11 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10010814
• 关键词: 3-Dimensional; Active Learning; Address; Adolescence; Alcohols; Archives; Area; BRAIN initiative; Big Data to Knowledge; Brain; Brain imaging; Classification; Collaborations; Communication; Computer software; Custom; Data; Data Analyses; Data Management Resources; Data Provenance; Data Set; Data Sources; Detection; Diffusion; Documentation; Effectiveness; Ensure; Evaluation; Evaluation Studies; FAIR principles; Four-dimensional; Funding; Generations; Geography; Goals; Human; Image; Intelligence; Internet; Investments; Iowa; Label; Learning; Licensing; Machine Learning; Magnetic Resonance Imaging; Maintenance; Manuals; Measurement; Medical Imaging; Modeling; Monitor; National Institute on Alcohol Abuse and Alcoholism; Neurosciences; Notification; Online Systems; Peer Review; Phase; Procedures; Process; Publications; Quality Control; Reporting; Research; Research Design; Research Personnel; Resources; Running; Sample Size; Scanning; Site; Structure; System; Technology; Time; United States National Institutes of Health; Universities; Update; Visual; Visualization; Work; Writing; annotation system; base; cohesion; computing resources; cost; data management; deep learning; design; dexterity; image archival system; imaging study; improved; innovation; learning algorithm; learning strategy; member; nervous system disorder; neurodevelopment; neuroimaging; open source; operation; quality assurance; research study; software systems; success; three-dimensional visualization; tool; web interface

1 Project Summary NIH is increasing its investment in large, multi-center brain MRI studies via projects such as the recently announced BRAIN initiative. The success of these studies depends on the quality of MRIs and the resulting image measurements, regardless of sample size. Even though quality control of MRIs and corresponding measurements could be outsourced, most neuroscience studies rely on in-house procedures that combine automatically generated scores with manually guided checks, such as visual inspection. Implementing these procedures typically requires combining several software systems. For example, the NIH NIAAA- and BD2K- funded Data Analysis Resource (DAR) of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) uses XNAT to consolidate the structural, diffusion, and functional MRIs acquired across five sites, and has also developed their own custom software package to comply with study requirements for a multi-tier, quality control (QC) workflow. However, these custom, one-off tools lack support for the multi-site QC workflows that will come with the unified platform that MIQA represents: a design that supports collaboration and sharing, and strong cohesion between technologies. To improve the effectiveness of QC efforts specific to multi-center neuroimaging studies, we will develop a widely accessible and broadly compatible software platform that simplifies the creation of custom QC workflows in compliance with study requirements, provides core functionality for performing QC of medical images, and automatically generates documentation compliant with the FAIR principle, i.e., making scientific results findable, accessible, interoperable, and reusable. Specifically, our multi-site, web-based software platform for Medical Image Quality Assurance (MIQA) will enable efficient and accurate QC processing by leveraging open-source, state-of-the-art web interface technologies, such as a web-based dataset caching system and machine learning to aid in QC processes. Users will be able to configure workflows that not only reflect the specific requirements of medical imaging studies but also minimize the time spent on labor-intensive operations, such as visually reviewing scans. Issue tracking technology will enhance communication between geographically-distributed team members, as they can easily share image annotations and receive automated notifications of outstanding QC issues. The system will be easy to deploy as it will be able to interface with various imaging storage backends, such as local file systems and XNAT. While parts of this functionality have been developed elsewhere, MIQA is unique as it provides a unified, standard interface for efficient QC setup, maintenance, and review for projects analyzing multiple, independently managed data sources. The usefulness of this unique QC system will be demonstrated on increasing the efficiency of the diverse QC team of the multi-center NCANDA study.

1 项目概要 NIH 正在通过最近的项目等增加对大型多中心脑 MRI 研究的投资 宣布了 BRAIN 计划。这些研究的成功取决于 MRI 的质量及其结果 图像测量，无论样本大小如何。尽管 MRI 和相应的质量控制 测量可以外包，大多数神经科学研究依赖于结合了内部程序 通过手动引导检查（例如目视检查）自动生成分数。实施这些 程序通常需要组合多个软件系统。例如，NIH NIAAA- 和 BD2K- 资助国家酒精和神经发育联盟的数据分析资源（DAR） 青春期 (NCANDA) 使用 XNAT 来整合获得的结构、扩散和功能 MRI 跨五个站点，并且还开发了自己的定制软件包以符合研究 多层质量控制 (QC) 工作流程的要求。然而，这些定制的一次性工具缺乏支持 对于将随 MIQA 代表的统一平台一起提供的多站点 QC 工作流程：一种设计 支持协作和共享以及技术之间的强大凝聚力。为了提高效率 针对多中心神经影像研究的质量控制工作，我们将开发一种可广泛使用和广泛使用的方法 兼容的软件平台，可简化符合研究的定制 QC 工作流程的创建 要求，提供执行医学图像质量控制的核心功能，并自动生成 符合公平原则的文档，即使科学结果可找到、可访问、 可互操作、可重用。 具体来说，我们的多站点、基于网络的医学图像质量保证软件平台 (MIQA) 将利用开源、最先进的网络界面实现高效、准确的质量控制处理 技术，例如基于网络的数据集缓存系统和机器学习，以帮助质量控制过程。 用户将能够配置不仅反映医学成像特定要求的工作流程 研究还可以最大限度地减少在劳动密集型操作（例如目视检查扫描）上花费的时间。问题 跟踪技术将增强地理上分散的团队成员之间的沟通，因为他们 可以轻松共享图像注释并接收未解决的质量控制问题的自动通知。系统 将易于部署，因为它将能够与各种图像存储后端（例如本地文件）连接 系统和 XNAT。虽然此功能的部分内容已在其他地方开发，但 MIQA 是独一无二的，因为它 为项目分析的高效 QC 设置、维护和审查提供统一、标准的界面 多个独立管理的数据源。 这种独特的质量控制系统的实用性将在提高各种效率方面得到证明 多中心 NCANDA 研究的 QC 团队。