Core 2 - Analysis

核心 2 - 分析

• 批准号: 10005499
• 负责人: Christophe Lenglet
• 金额: $ 14.89万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005499
• 关键词: Aging; Animals; Architecture; Brain; Brain imaging; Complex; Computer software; Data; Data Analyses; Data Storage and Retrieval; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Equipment; Explosion; Fiber; Functional Magnetic Resonance Imaging; Goals; Human; Human Resources; Image; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Maps; Measures; Methodology; Modality; Modeling; Morphologic artifacts; National Institute of Neurological Disorders and Stroke; Population; Relaxation; Research; Research Personnel; Resolution; Resources; Site; Spectrum Analysis; Statistical Data Interpretation; Structure; Time; Visualization; analysis pipeline; computing resources; connectome; data visualization; improved; magnetic field; mathematical model; multimodal data; neuroimaging; quantitative imaging; reconstruction; spectroscopic imaging; tool; tractography; white matter

The analysis and visualization of high field magnetic resonance imaging (MRI) and spectroscopy (MRS) data require cutting-edge computational resources and expertise, because of their increasing size and complexity. Although these advances will undoubtedly improve our understanding of brain development, aging and disorders, it can also be a significant barrier for investigators. Therefore, the overall goal of this core is to provide unique and easily accessible processing resources for human and animal neuroimaging research, which will empower researchers to efficiently produce state-of-the-art results, and generate high-quality images from CMRR's high and ultrahigh field MR scanners. Aim 1: To provide MRI data reconstruction and pre-processing expertise and tools, including parallel imaging, artifacts and distortion correction. Aim 2: To provide expertise and computational resources for MRI data analysis, including registration, segmentation, diffusion models fitting (tensor, crossing fibers models, microstructure), tractography, and fMRI statistical analysis. Aim 3: To provide quantitative imaging analysis capabilities, including generating quantitative brain maps of relaxation times. Aim 4: To provide processing and metabolite quantification capabilities for MRS. Aim 5: To provide data storage, organization and visualization capabilities.

高场强磁共振成像（MRI）的分析和可视化， MRS数据需要尖端的计算资源和专业知识，因为 不断增长的规模和复杂性。尽管这些进步无疑会改善我们的 了解大脑发育，衰老和疾病，它也可能是一个重要的障碍， investigators.因此，该核心的总体目标是提供独特且易于访问的 处理人类和动物神经成像研究的资源，这将使 研究人员可以有效地产生最先进的结果，并生成高质量的图像， CMRR的高场和低场MR扫描仪。目的1：提供MRI数据重建和 预处理专业知识和工具，包括并行成像、伪影和失真校正。 目标2：为MRI数据分析提供专业知识和计算资源，包括 配准、分割、扩散模型拟合（张量，交叉纤维模型， 显微结构）、纤维束成像和fMRI统计分析。目标3：提供定量 成像分析能力，包括生成弛豫时间的定量大脑地图。 目标4：为MRS提供处理和代谢物定量能力。 提供数据存储、组织和可视化功能。