FreeSurfer Development, Maintenance, and Hardening

FreeSurfer 开发、维护和强化

• 批准号: 10326397
• 负责人: Bruce Fischl
• 金额: $ 57.56万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10326397
• 关键词: Adolescent; Algorithms; Alzheimer' s Disease; Anatomy; Atlases; Bayesian Modeling; Biological Markers; Blood Vessels; Brain; Brain imaging; Cell Nucleus; Computer software; Data; Development; Diagnosis; Diffusion Magnetic Resonance Imaging; Disease; Documentation; Electroencephalography; Engineering; Evaluation; Fatty acid glycerol esters; Framingham Heart Study; Free Will; Functional Magnetic Resonance Imaging; Funding; Gaussian model; Grant; Growth; Hippocampus (Brain); Human; Image; Individual; Industrialization; Inherited; Lesion; Leukoaraiosis; Machine Learning; Magnetic Resonance Imaging; Maintenance; Manuscripts; Modality; Modeling; Morphologic artifacts; Multiple Sclerosis Lesions; Neurosciences; Operating Rooms; Optics; Output; Pharmacologic Substance; Positron-Emission Tomography; Probability; Publications; Research; Research Personnel; Series; Software Engineering; Stream; Structure; Surface; System; Testing; Thalamic Nuclei; Time; United States National Institutes of Health; Vision; Work; base; biobank; bone; cognitive development; connectome; cranium; data acquisition; deep learning; human imaging; innovation; large scale data; multimodality; neuroimaging; programs; success; synergism; tool; white matter

Abstract: Imaging of the human brain has seen explosive growth in the last two decades mainly through the various modalities of MRI. The massive amount of data requires automatic and robust tools for analysis. FreeSurfer (FS, surfer.nmr.mgh.harvard.edu) is one of the preeminent tools used for neuroimage analysis. FS has more than 44,000 downloads, and the core FS manuscripts have been cited more than 22,000 times. FS is part of the analysis core for many NIH-funded large-scale data acquisition projects such as the Human Connectome Project (HCP), Alzheimer's Disease Neuroimaging Initiative (ADNI), Framingham Heart Study (FHS), The Adolescent Brain Cognitive Development (ABCD), as well as the UK BioBank. One third of the 600+ ADNI-based publications cite FS. Simply put, much of the innovative research done in neuroimaging would not be possible without FS. Started in 1998, FS is best known for providing detailed and automated anatomical analysis of T1-weighted MRI images, especially for the cortical surface. However, FS anatomical analysis provides an ideal substrate for all modes of brain imaging including functional MRI, diffusion MRI, PET, optical/NIRS, as well as EEG/MEG. FS provides tools to perform these analyses as well as software to integrate with other analysis tools (e.g., SPM, FSL, AFNI). FS has been used for presurgical planning and even in the operating room. The original grant mostly centered around Sequence Adaptive Multimodal Segmentation (SAMSEG). SAMSEG uses parametric Bayesian generative modeling to segment brain images. The SAMSEG framework fits atlas priors and multivariate Gaussian intensity models to brain images (including MRI artifacts such as bias fields). SAMSEG can take any modality or combination of modalities as input. Since it adapts its intensity model, it is robust to differences in scanner. Since it is a generative model, it is easy to extend to encompass more segmentation details. For example, the SAMSEG framework has been used to segment hippocampal subfield, amygdalar nuclei, thalamic nuclei, and extracerebral structures. The main vision for the renewal is to extend the SAMSEG framework to accommodate longitudinal models, incorporate more anatomical details, and to use SAMSEG output as a basis for cortical surface placement that is, like SAMSEG, modality independent and capable of using any combination of modalities. In addition, we propose a series of new tools that will assist in the individual and group analysis of large studies by creating study-specific models. In addition to this new technical development, we are requesting support for software engineering, maintenance, and user support – mundane and not innovative, but high-impact this type of support is critical to the thousands of researchers who rely on FreeSurfer.

摘要： 在过去的二十年里，人类大脑的成像技术主要通过各种方法得到了爆炸性的发展。 MRI的模式。大量的数据需要自动和强大的分析工具。FreeSurfer (FS，surfer.nmr.mgh.harvard.edu）是用于神经图像分析的卓越工具之一。FS更多 下载量超过44，000次，核心文件系统手稿被引用超过22，000次。财政司司长是 许多NIH资助的大型数据采集项目的分析核心，如人类连接组项目 (HCP)，阿尔茨海默病神经影像学倡议（ADNI），心脏病研究（FHS），青少年 大脑认知发展（ABCD），以及英国生物银行。600多篇基于ADNI的出版物中的三分之一 引用FS。简而言之，如果没有FS，神经成像领域的许多创新研究都是不可能的。 从1998年开始，FS以提供T1加权MRI的详细和自动解剖分析而闻名 图像，尤其是皮质表面。然而，FS解剖分析为所有 脑成像模式包括功能性MRI、扩散MRI、PET、光学/NIRS以及EEG/MEG。FS 提供执行这些分析的工具以及与其他分析工具集成的软件（例如，SPM， FSL、AFNI）。FS已被用于术前计划，甚至在手术室。 最初的资助主要集中在序列自适应多模式分割（SAMSEG）。 SAMSEG使用参数贝叶斯生成建模来分割大脑图像。SAMSEG框架 将图谱先验和多变量高斯强度模型拟合到脑图像（包括MRI伪影，例如偏差 fields）。SAMSEG可以采用任何模态或模态组合作为输入。因为它调整了它的强度模型， 它对于扫描仪中差异是鲁棒的。由于它是一个生成模型，因此很容易扩展以包含更多 细分细节。例如，SAMSEG框架已经用于分割海马子区， 杏仁核、丘脑核和脑外结构。 更新的主要愿景是扩展SAMSEG框架，以适应纵向 模型，纳入更多的解剖细节，并使用SAMSEG输出作为皮质表面的基础 与SAMSEG一样，放置方式独立，能够使用任何方式组合。在 此外，我们还提出了一系列新的工具，这些工具将有助于对大型研究进行个体和群体分析， 创建研究专用模型。除了这一新的技术发展，我们还要求支持 软件工程，维护和用户支持-平凡而不创新，但这种类型的影响力很大 对于成千上万依赖FreeSurfer的研究人员来说，支持至关重要。