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: 10183329
• 负责人: Aashish Chaudhary
• 金额: $ 79.75万
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10183329
• 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; 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个项目摘要 NIH通过最近的项目增加了对大型，多中心大脑MRI研究的投资 宣布的大脑计划。这些研究的成功取决于MRI的质量和结果 图像测量，无论样本量如何。即使MRI和相应的质量控制 可以将测量值外包，大多数神经科学研究都依赖于内部程序 通过手动指导检查（例如视觉检查）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- 国家酒精和神经发育联盟的资助数据分析资源（DAR） 青春期（NCANDA）使用XNAT巩固获得的结构，扩散和功能性MRI 在五个站点之间，还开发了自己的自定义软件包以遵守研究 多层，质量控制（QC）工作流程的要求。但是，这些自定义的一次性工具缺乏支持 对于MIQA代表的统一平台的多站点QC工作流程： 支持协作和共享，以及技术之间的强烈凝聚力。提高有效性 在特定于多中心神经影像学研究的QC努力中，我们将开发一个广泛访问和广泛的 兼容的软件平台简化了根据研究的符合自定义QC工作流的创建 要求，为执行医学图像的QC提供核心功能，并自动生成 文档符合公平原则，即使科学结果可访问，可访问， 可互操作，可重复使用。 具体而言，我们的多站点，基于网络的医学图像质量保证软件平台（MIQA） 通过利用开源，最先进的Web界面来实现高效且准确的QC处理 技术，例如基于Web的数据集缓存系统和机器学习，以帮助QC流程。 用户将能够配置工作流不仅反映医学成像的特定要求 研究，还可以最大程度地减少劳动密集型操作的时间，例如视觉审查扫描。问题 跟踪技术将增强地理分布的团队成员之间的沟通 可以轻松地共享图像注释并接收有关QC问题的自动通知。系统 它将很容易部署，因为它可以与各种成像存储后端接口，例如本地文件 系统和XNAT。虽然该功能的某些部分是在其他地方开发的，但MIQA是独一无二的 为有效的QC设置，维护和审核提供了一个统一的标准接口，用于分析项目 多个独立管理的数据源。 这种独特的QC系统的实用性将在提高各种效率方面证明 多中心NCANDA研究的QC团队。