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).

摘要 在这个为期5年的R01项目中，题为“协调数据采集、重建和分析以进行可重现的， 跨供应商的、开源的MRI，“我们解决了科学进步的重大障碍，这是由于 多点MRI数据中存在的扫描仪变异性(通常超过10%-20%)显著降低了 神经影像研究在神经精神疾病中发现细微病理的能力。扫描仪间偏差 是封闭源代码产品序列实现的差异的结果(例如，渐变和 射频脉冲形状和定时)、重建算法的选择以及固有的变化 到扫描仪硬件(例如，梯度强度)。另一个重大挑战是发展的重大障碍 分别为每个供应商提供新的序列。这阻碍了将新的磁共振成像技术转化为研究 实验室，作为供应商特定的序列开发环境，是封闭源代码的、专有的，并受到 从一个陡峭的学习曲线。 在这个项目中，我们通过提出一个“端到端”的协调框架来应对这些挑战。我们 建议开发和发布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算法，以实现多站点 可重复性研究，并减少扫描仪间的可变性和偏差。我们的量化MRI收购将是 高效(5分钟，而不是15分钟以上)，还包括快速、无失真的扩散磁共振成像 序列。标准对比度加权方案的性能和新定量方法的准确性 成像序列将在模体和从所有主要供应商获得的体内数据上进行严格验证 (西门子、飞利浦、通用电气)。此外，我们将开发和验证新的数据协调算法，以 消除由于硬件差异而导致的扫描仪导致的数据中的任何剩余差异。其中一个目标是 该项目的目的是将扫描仪间的可变性降低到扫描仪内观察到的水平。技术上的 这笔赠款中提议的开发将极大地提高跨站点的重现性，并允许无缝 执行多点神经成像研究。因此，多点研究增加的统计能力将 有助于发现神经精神障碍的细微变化。我们的开源首个此类平台将 还可以加快跨供应商的序列开发，并支持将新序列立即翻译为 研究性学习(目前需要几年的时间)。