Interoperable Software Platform for Reproducible Research and Clinical Translation of MRI

用于 MRI 可重复研究和临床转化的可互操作软件平台

• 批准号: 10491708
• 负责人: Michael Lustig
• 金额: $ 30.45万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-21 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10491708
• 关键词: Address; Advanced Development; Algorithms; Ally; Architecture; Back; Binding; Clinical; Clinical Research; Cloud Computing; Code; Communities; Complement; Computer software; Data; Development; Documentation; Ecosystem; Educational Materials; Educational workshop; Environment; Europe; Fostering; Funding Opportunities; Goals; Growth; High Performance Computing; Image; Industry; Infrastructure; Institution; International; Ionizing radiation; Libraries; Linux; MRI Scans; Magnetic Resonance Imaging; Maintenance; Memory; Methods; Modality; Modernization; Monitor; Motivation; Movement; Neurosciences Research; Operating System; Performance; Polishes; Publishing; Pythons; Renaissance; Reproducibility; Research; Resources; Scanning; Schedule; Science; Site; Software Framework; Software Tools; Standardization; Techniques; Technology; Testing; Time; Training Activity; Translating; United States; Update; Vendor; Visual; Work; X-Ray Computed Tomography; base; biomedical imaging; clinical practice; clinical translation; cloud based; cluster computing; computational platform; computerized data processing; data repository; experience; file format; graphical user interface; image reconstruction; imaging modality; improved; innovation; interest; interoperability; man; neuroimaging; non-invasive imaging; open source; parallel computer; parity; pre-clinical research; program dissemination; quality assurance; reconstruction; research clinical testing; response; soft tissue; software infrastructure; tool; usability; web page; web portal; webinar

Project Abstract Motivation: This proposal, titled Interoperable Software Platform for Reproducible Research and Clinical Translation of MRI, is in response to the U24 funding opportunity RFA-EB-18-002, Resources for Technology Dissemination. Magnetic resonance imaging (MRI) is non-invasive, non-ionizing, and offers superb soft tissue contrast, but is traditionally limited by long scan times. Recently, advances in numerical image reconstruction and availability of powerful hardware platforms have led to new MRI scanning techniques with dramatic reductions in scan times. However, the associated computational sophistication has posed a large barrier to reproducibil- ity and clinical translation. This proposal addresses this fundamental issue by establishing best practices and infrastructure for reproducible research in MRI. Initial work toward this goal spanning six years has led to the development of the BART software toolbox for computational MRI. BART implements advanced MRI reconstruction algorithms in an extensible manner so that new technological advances can build off of the collective progress in the field. Supported computational back- ends including multi-CPU and multi-GPU architectures afford efficient use in a clinical translation environment. Project dissemination has been met with strong interest from the international MRI research community, having grown a user-base spanning over 50 academic and industry sites. Nonetheless, current limitations in project in- frastructure and support have hindered more widespread dissemination. Therefore, the major emphasis here is expanding development to improve usability, creation of written and audio-visual educational material, integration with other tools, cloud-based support, and software reliability. This will (1) provide new users common ground for starting new projects, (2) allow them to use their existing workflows with BART, (3) move to more accessible computation platforms, and (4) reliably translate their work into clinical practice. Approach: The project will proceed with four interrelated aims, supported by user training activities. Aim 1 will focus on adding comprehensive documentation and creating example-based tutorials. Aim 2 will expand interop- erability with software platforms and vendor tools used by the MRI community. Aim 3 will complete infrastructure and backends for cloud and parallel computing. Aim 4 will improve software reliability and quality assurance. The work will be disseminated through online material, webinars and workshops. Significance: This work will enable development, creation and reproducibility of modern state-of-the art MRI reconstruction methods that rely on highly specialized data processing approaches. MRI development will be streamlined as new methods build off of reliable infrastructure and existing work. Improved sustainability and reliability will enable rapid dissemination of new work into clinical evaluation and practice while significantly reducing the technical burden normally associated with clinical translation.

项目摘要