Harmonizing data acquisition, reconstruction, and analysis for reproducible, cross-vendor, open source MRI

协调可重复、跨供应商、开源 MRI 的数据采集、重建和分析

• 批准号: 10704747
• 负责人: Berkin Bilgic
• 金额: $ 63.54万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10704747
• 关键词: 3-Dimensional; Acceleration; Address; Adoption; Algorithms; Anisotropy; Attenuated; Brain; Brain Diseases; Brain region; Calibration; Code; Coronavirus; Data; Detection; Development; Diffusion Magnetic Resonance Imaging; Disease; Disparate; Echo-Planar Imaging; Environment; Functional Magnetic Resonance Imaging; Goals; Grant; Health; Image; Knowledge; Laboratory Research; Learning; Liquid substance; Magnetic Resonance Imaging; Maps; Measures; Mental disorders; Neurobiology; Pathology; Performance; Physiologic pulse; Predisposition; Protocols documentation; Protons; Recommendation; Recovery; Relaxation; Reproducibility; Research; Resolution; Sample Size; Scanning; Scheme; Schizophrenia; Shapes; Site; Slice; Source; System; Techniques; Technology; Time; Tissues; Translational Repression; Translations; Travel; Variant; Vendor; Work; data acquisition; data harmonization; deep learning; density; gray matter; human subject; image reconstruction; improved; in vivo; neuroimaging; neuropsychiatric disorder; novel; open source; quantitative imaging; radio frequency; radiologist; reconstruction; research study; simulation; transmission process; white matter

Abstract In this 5-year R01 project entitled “Harmonizing data acquisition, reconstruction, and analysis for reproducible, cross-vendor, open-source MRI,” we address the significant barriers to scientific progress due to the large inter- scanner variability (often more than 10-20%) present in multi-site MRI data which substantially diminishes the power of neuroimaging studies to detect subtle pathologies in neuropsychiatric disorders. Inter-scanner biases are a result of differences in implementation of closed-source product sequences (e.g., gradient and radiofrequency pulse shapes and timing), the choice of reconstruction algorithms, as well as variations inherent to the scanner hardware (e.g., gradient strength). Another major challenge is the significant barrier to develop new sequences for each vendor separately. This inhibits the translation of new MRI technologies to research laboratories, as vendor-specific sequence development environments are closed-source, proprietary, and suffer from a steep learning curve. In this project, we address these challenges by proposing an “end-to-end” harmonization framework. We propose to develop and disseminate a single open-source vendor-neutral MRI pulse sequence development environment containing both standard MRI protocols (e.g., T1-weighted, T2-weighted, and diffusion MRI) and cutting-edge quantitative acquisitions (T1, T2, T2*, and quantitative susceptibility maps (QSM)), a unified image reconstruction framework, and novel algorithms for post-acquisition data harmonization to enable multi-site reproducible research and mitigate inter-scanner variability and bias. Our quantitative MRI acquisitions will be efficient (5 min as opposed to more than 15 min) and also comprise of fast, distortion-free diffusion MRI sequences. The performance of standard contrast-weighted protocols and the accuracy of novel quantitative imaging sequences will be rigorously validated on phantoms and in-vivo data acquired from all major vendors (Siemens, Philips, GE). Further, we will develop and validate novel data harmonization algorithms that will remove any remaining scanner-induced discrepancies in the data due to hardware differences. One of the goals of this project is to reduce inter-scanner variability to the level of those observed within-scanner. The technical developments proposed in this grant will dramatically increase reproducibility across sites and allow for seamless execution of multi-site neuroimaging studies. Thus, the increased statistical power of multi-site studies will facilitate detection of subtle changes in neuropsychiatric disorders. Our open-source first-of-its-kind platform will also accelerate cross-vendor sequence development and enable immediate translation of new sequences into research studies (which currently takes several years).

抽象的 在这个为期 5 年的 R01 项目中，题为“协调数据采集、重建和分析以实现可重复、 跨供应商、开源 MRI”，我们解决了由于大量的内部研究而导致的科学进步的重大障碍。 多站点 MRI 数据中存在扫描仪变异性（通常超过 10-20%），这大大降低了 神经影像学研究检测神经精神疾病的微妙病理的力量。扫描仪间偏差 是闭源产品序列实现差异的结果（例如，梯度和 射频脉冲形状和定时）、重建算法的选择以及固有的变化 扫描仪硬件（例如梯度强度）。另一个主要挑战是发展的重大障碍 分别为每个供应商提供新序列。这阻碍了新的 MRI 技术向研究的转化 实验室，因为供应商特定的序列开发环境是闭源的、专有的，并且受到影响 从陡峭的学习曲线。 在这个项目中，我们通过提出“端到端”协调框架来应对这些挑战。我们 建议开发和传播单一开源供应商中立的 MRI 脉冲序列开发 包含标准 MRI 协议（例如 T1 加权、T2 加权和扩散 MRI）和 尖端定量采集（T1、T2、T2* 和定量磁化率图 (QSM)）、统一图像 重建框架和用于采集后数据协调的新颖算法，以实现多站点 可重复的研究并减少扫描仪之间的差异和偏差。我们的定量 MRI 采集将是 高效（5 分钟而不是超过 15 分钟），并且还包括快速、无失真的扩散 MRI 序列。标准对比加权方案的性能和新型定量方法的准确性 成像序列将在从所有主要供应商获取的模型和体内数据上进行严格验证 （西门子、飞利浦、GE）。此外，我们将开发和验证新颖的数据协调算法，这些算法将 消除由于硬件差异而导致的任何剩余扫描仪引起的数据差异。目标之一 该项目的目的是将扫描仪之间的变异性降低到扫描仪内观察到的水平。技术方面 这笔赠款中提出的发展将极大地提高跨站点的可重复性，并实现无缝 执行多部位神经影像研究。因此，多中心研究统计能力的增强将 有助于检测神经精神疾病的细微变化。我们的首个开源平台将 还加速跨供应商序列开发，并能够立即将新序列翻译成 研究（目前需要数年时间）。