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: 10677036
• 负责人: Michael Lustig
• 金额: $ 29.8万
• 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/10677036
• 关键词: Address; Advanced Development; Algorithms; Architecture; Back; Clinical; Clinical Research; Cloud Computing; Code; Communities; Complement; Computer software; Data; Dedications; 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; Operating System; Output; Performance; Polishes; Publishing; Pythons; Renaissance; Reproducibility; Research; Resources; Scanning; Schedule; Science; Site; Software Framework; Software Tools; Standardization; Techniques; Technology; Testing; Training Activity; Translating; United States; Update; Vendor; Visualization; Work; Writing; 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; neuroimaging; new technology; 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 动机：该提案名为“用于可重复研究和临床的互操作软件平台” MRI 的翻译，是为了响应 U24 资助机会 RFA-EB-18-002，技术资源 磁共振成像 (MRI) 是非侵入性、非电离性的，可提供优质的软组织。 对比度，但传统上受到长扫描时间的限制。最近，数值图像重建和成像方面取得了进展。 强大的硬件平台的可用性带来了新的 MRI 扫描技术，并大幅减少了扫描成本 然而，相关的计算复杂性对再现性造成了很大的障碍。 该提案通过建立最佳实践和临床翻译来解决这一基本问题。 MRI 可重复研究的基础设施。 为实现这一目标而进行的初步工作历时六年，开发了 BART 软件工具箱，用于 计算 MRI。BART 以可扩展的方式实现先进的 MRI 重建算法，以便 新的技术进步可以建立在该领域的集体进步的基础上。 包括多 CPU 和多 GPU 架构在内的终端可在临床翻译环境中高效使用。 项目传播引起了国际 MRI 研究界的浓厚兴趣， 尽管如此，目前项目的局限性仍然存在。 基础设施和支持阻碍了更广泛的传播。因此，这里的重点是。 扩大开发以提高可用性、创建书面和视听教育材料、集成 与其他工具、基于云的支持和软件可靠性相结合，这将 (1) 为新用户提供共同点。 为了启动新项目，(2) 允许他们通过 BART 使用现有的工作负载，(3) 转向更容易访问的 计算平台，(4) 可靠地将其工作转化为临床实践。 方法：该项目将在用户培训活动的支持下实现四个相互关联的目标。 重点是添加全面的文档和创建基于示例的教程，目标 2 将扩展互操作性。 MRI 社区使用的软件平台和供应商工具的可操作性将完善基础设施。 目标 4 将提高软件可靠性和质量保证。 工作将通过在线材料、网络研讨会和讲习班进行传播。 意义：这项工作将使现代最先进的 MRI 的开发、创造和再现成为可能 依赖于高度专业化的 MRI 数据处理方法的重建方法将成为现实。 随着新方法建立在可靠的基础设施和现有工作的基础上而得到简化，并提高了可持续性。 可靠性将使新工作能够快速传播到临床评估和实践中，同时显着 减少通常与临床翻译相关的技术负担。

项目成果

Free-Breathing Liver Fat, R₂* and B₀ Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-Based Reconstruction.

使用多回波径向闪光和基于正则化模型的重建进行自由呼吸肝脏脂肪、R* 和 B 场映射。

• 发表时间: 2023-05
• 影响因子: 10.6
• 作者: Tan, Zhengguo;Unterberg;Blumenthal, Moritz;Scholand, Nick;Schaten, Philip;Holme, Christian;Wang, Xiaoqing;Raddatz, Dirk;Uecker, Martin
• 通讯作者: Uecker, Martin

Deep, deep learning with BART.

使用 BART 进行深度学习。

• 发表时间: 2023-02
• 影响因子: 3.3
• 作者: Blumenthal, Moritz;Luo, Guanxiong;Schilling, Martin;Holme, H Christian M;Uecker, Martin
• 通讯作者: Uecker, Martin

Physics-based reconstruction methods for magnetic resonance imaging.

基于物理的磁共振成像重建方法。

• 发表时间: 2021-06-28
• 作者: Wang, Xiaoqing;Tan, Zhengguo;Scholand, Nick;Roeloffs, Volkert;Uecker, Martin
• 通讯作者: Uecker, Martin

Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization.

Deep J-Sense：通过展开交替优化加速 MRI 重建。

• 发表时间: 2021
• 作者: Arvinte, Marius;Vishwanath, Sriram;Tewfik, Ahmed H;Tamir, Jonathan I
• 通讯作者: Tamir, Jonathan I

Free-breathing myocardial T1 mapping using inversion-recovery radial FLASH and motion-resolved model-based reconstruction.

使用反转恢复径向 FLASH 和基于运动分辨模型的重建进行自由呼吸心肌 T1 映射。

• 发表时间: 2023-04
• 影响因子: 3.3
• 作者: Wang, Xiaoqing;Rosenzweig, Sebastian;Roeloffs, Volkert;Blumenthal, Moritz;Scholand, Nick;Tan, Zhengguo;Holme, H Christian M;Unterberg;Hinkel, Rabea;Uecker, Martin
• 通讯作者: Uecker, Martin