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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

基本信息

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
项目类别：
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

项目摘要

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项目概要美国国立卫生研究院正在通过最近的项目增加对大型多中心脑MRI研究的投资，宣布了BRAIN倡议。这些研究的成功取决于MRI的质量和结果。图像测量，无论样本大小。尽管MRI的质量控制和相应的虽然测量可以外包，但大多数神经科学研究都依赖于内部程序，这些程序将联合收割机自动生成的分数与手动引导的检查，如目视检查。实施这些过程通常需要组合几个软件系统。例如，NIH NIAAA-和BD 2K- 国家酒精和神经发育联盟资助的数据分析资源（DAR），青少年（NCANDA）使用XNAT巩固获得的结构、弥散和功能MRI 在五个网站，并已开发自己的定制软件包，以符合研究多层质量控制（QC）工作流程的要求。但是，这些定制的一次性工具缺乏支持对于MIQA所代表的统一平台所附带的多站点QC工作流程，支持协作和共享，以及技术之间的强大凝聚力。以进一步有效针对多中心神经影像学研究的QC工作，我们将开发一个广泛可及的，兼容的软件平台，简化了符合研究要求的自定义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件，即，使科学成果变得可发现、可获取，可互操作且可重复使用。具体而言，我们的多站点、基于Web的医学影像质量保证（MIQA）软件平台将通过利用开源、最先进的Web界面实现高效、准确的QC处理技术，如基于Web的数据集缓存系统和机器学习，以帮助质量控制过程。用户将能够配置工作流程，不仅反映医疗成像的具体要求，这不仅可以帮助您进行检查，而且还可以最大限度地减少劳动密集型操作（如目视检查扫描）所花费的时间。问题跟踪技术将加强地理分布的团队成员之间的沟通，因为他们可以轻松地共享图像注释并接收未解决的QC问题的自动通知。系统将易于部署，因为它将能够与各种映像存储后端（如本地文件系统和XNAT。虽然此功能的一部分已在其他地方开发，但MIQA是独一无二的，因为它为项目分析提供了一个统一的标准接口，用于高效的QC设置、维护和审核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被证明对提高效率的各种多中心NCANDA研究的QC团队。