TRD 4: Platforms for multi-modal and multi-scale imaging data

TRD 4：多模式和多尺度成像数据平台

• 批准号: 10270101
• 负责人: SUSUMU MORI
• 金额: $ 35.27万
• 依托单位: HUGO W. MOSER RES INST KENNEDY KRIEGER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10270101
• 关键词: Algorithms; Anatomy; Area; Atlases; Axon; Biological Markers; Biomedical Engineering; Biometry; Brain; Brain Diseases; Brain Mapping; Collaborations; Collection; Complex; Computer software; Data; Data Analyses; Data Science; Detection; Development; Diffusion; Dimensions; Engineering; Etiology; Image; Label; Lead; Learning; Lesion; Magnetic Resonance Imaging; Metabolic; Methods; Modality; Modeling; Modernization; Names; Output; Pathologic; Pathology; Performance; Perfusion; Physiological; Positioning Attribute; Predisposition; Principal Investigator; Prognosis; Radiology Specialty; Research Personnel; Resolution; Resources; Role; Technology; Testing; Time; base; contrast imaging; data acquisition; deep learning; experience; frontier; image archival system; image registration; imaging Segmentation; imaging modality; multimodality; new technology; prediction algorithm; predictive modeling; professor; prospective; reconstruction; signal processing; statistics; success; tool

TRD 4: Platforms for multi-modal and multi-scale imaging data Lead Principal investigator: Susumu Mori, Professor of Radiology; Co-Principal investigators: Brian Caffo, Professor of Biostatistics; Jeremias Sulam, Assistant professor of Biomedical Engineering Co-investigators: Andreia Faria, Michael Miller, Tilak Ratnanather, Laurent Younes The role of TRD4 is to develop new technologies and platforms to integrate and analyze complex multi-modal and multi-scale imaging data via collaboration with other TRDs and CPs. In the past two decades, we have witnessed remarkable advances in image acquisition, processing, and analysis technologies for brain MRI. As we enter a new decade, however, there remain several key areas in combining information across the macro- meso-micro scales, and discovering predictive models for which we require significant advances in existing tools and technologies. One of the significant opportunities going forward is to leverage the rapidly evolving data science technologies which are now emerging and opening new frontiers for researchers. This will require advances not only in collections of software to analyze each dimension but also a new generalized framework to integrate and explore the data. Another important development in recent years is the surge of heavily data- driven approaches such as deep learning. We believe this is a great time to invest time and resources to evaluate their capability by comparing them with conventional engineering approaches. More importantly, there is great potential in combining these two approaches to test improvements in precision, accuracy, and/or efficiency. We are uniquely positioned to take a lead in this sphere by leveraging our experiences and resources (tools and data) accumulated in the past 20 years. Our specific aims will be: (1) To integrate and test deep learning (DL) approaches in image data acquisitions and analyses to solve inverse problems for estimation of latent variables in brain MRI. These variables include enhancement of SNR, anatomical resolutions, and underlying anatomical features such as axonal alignments; (2) To develop technologies and platforms for characterizing models that predict the key factors determining brain diseases by integrating multiple imaging modalities, time-domain data, and non-imaging information through statistics. Our models will focus on etiology, pathology and prognosis considering retrospective, cross-sectional and prospective data; (3) To integrate DL approaches to brain mapping strategies such as registration, image segmentation, and lesion detections. Using our rich resources for annotated image libraries (atlases), conventional segmentation / detection tools, and expertise, we will develop DL-based approaches and evaluate their efficacy, including comparison with conventional approaches in terms of accuracy and efficiency. Improvement of the performance by combining these two approaches will be tested. Furthermore, we will develop a framework for diffeomorphic image registration that can incorporate extracted features (i.e. labels and lesions) to perform brain mapping of cases with severe pathological conditions.

TRD 4：多模式和多尺度成像数据平台 主要研究者：Susumu Mori，放射学教授;共同主要研究者：Brian Caffo， 生物统计学教授;生物医学工程助理教授杰里米苏拉姆 共同研究者：Andreia Faria、Michael米勒、Tilak Ratnanather、Laurent Younes TRD 4的作用是开发新的技术和平台，以集成和分析复杂的多模态 以及通过与其他TRD和CP协作获得多尺度成像数据。在过去的二十年里， 在脑MRI的图像采集、处理和分析技术方面取得了显著进展。作为 然而，我们进入了一个新的十年，在综合宏观信息方面仍有几个关键领域， 中微观尺度，并发现预测模型，我们需要在现有的 工具和技术。未来的重大机遇之一是利用快速发展的 数据科学技术正在兴起，为研究人员开辟了新的领域。这将需要 进步不仅在软件集合来分析每个维度，但也是一个新的广义框架 来整合和探索数据。近年来的另一个重要发展是大量数据的激增- 例如深度学习。我们认为，这是一个很好的时间投入时间和资源， 通过将其与传统工程方法进行比较来评估其能力。更重要的是 结合这两种方法来测试精度、准确度和/或 效率我们处于独特的地位，通过利用我们的经验， 过去20年积累的资源（工具和数据）。我们的具体目标是：（1）整合和测试 图像数据采集和分析中的深度学习（DL）方法，以解决 脑MRI中潜在变量的估计。这些变量包括SNR的增强、解剖结构 分辨率和基本解剖特征，如轴突排列;（2）开发技术和 用于表征模型的平台，这些模型通过整合 多种成像模态、时域数据和通过统计的非成像信息。我们的模特将 关注病因、病理和预后，考虑回顾性、横断面和前瞻性数据;（3） 将DL方法集成到脑映射策略中，如配准，图像分割和病变 侦查利用我们丰富的带注释图像库（地图集）资源， 检测工具和专业知识，我们将开发基于DL的方法并评估其有效性，包括 在精度和效率方面与传统方法进行比较。改善 将测试结合这两种方法的性能。此外，我们将制定一个框架， 可以合并提取的特征（即标签和病变）以执行的同构图像配准 严重病理情况下的脑地形图。