Dynamic Inverse Solutions for Multimodal Imaging

多模态成像的动态逆解

• 批准号: 7870462
• 负责人: David A Boas
• 金额: $ 136.76万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-20 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7870462
• 关键词: Algorithms; Alzheimer' s Disease; Anatomy; Anesthesia procedures; Biomedical Engineering; Biophysics; Blood Volume; Boa; Brain; Brain imaging; Charge; Clinical Management; Communities; Computational algorithm; Computer software; Data; Data Analyses; Data Set; Development; Electroencephalography; Epilepsy; Functional Magnetic Resonance Imaging; General Hospitals; General anesthetic drugs; Goals; Image; Image Analysis; Imaging Device; Imaging technology; Individual; Intensive Care Units; Lead; Link; Magnetoencephalography; Massachusetts; Measurement; Methods; Modality; Modeling; Monitor; Multimodal Imaging; Parkinson Disease; Patients; Physiological; Physiology; Propofol; Research; Research Personnel; Resources; Series; Signal Transduction; Simulate; Sleep; Solutions; Source; Space Models; Speed; Stroke; Supercomputing; System; Testing; Time; Unconscious State; Validation; Vascular System; base; bioimaging; clinical application; computer framework; computing resources; design; diffuse optical tomography; imaging modality; novel; programs; relating to nervous system; research study; response; supercomputer

DESCRIPTION (provided by applicant): In recent years, there has been rapid progress in the development of imaging technologies to study brain function. An important focus of current research in many imaging centers is the development of functional neural imaging tools to carry out multimodal image fusion using various combinations of functional magnetic resonance imaging (fMRI), diffuse optical tomography (DOT), electroencephalography (EEG) and magnetoencephalography (MEG) measurements. This requires conducting experiments in which imaging is performed from two or more modalities simultaneously or in sequence so that the information from the different sources can be optimally combined. Using two or more imaging modalities simultaneously offers the exciting prospect of tracking the dynamics of brain activity on different spatial and time-scales. In response to > PAR-04-023, we propose to form a Bioengineering Research Partnership at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital to develop computational resources for fusing imaging measurements from two or more modalities. Using EEG, MEG, fMRI and DOT, this Partnership will develop an integrated state-space, computational framework based on the biophysics, physiology and anatomy of these imaging modalities. The model components for this computational framework will be identified and validated through a series of cross-modality experiments. High-speed supercomputing resources will be used to design and test the state-space data analysis algorithms on simulated and actual multimodal experimental imaging data. The data analysis algorithms developed as part of this Partnership and the data collected in the cross-modality experiments will be freely disseminated to the brain imaging community. The long-term goals of this project are to provide the brain imaging community with a unified computational framework for combining measurements from two or more imaging modalities that can be used in both real-time research studies and clinical management of patients. Real-time analysis of brain function will have important implications for understanding the dynamics of normal brain function, how these dynamics change in pathological conditions such as epilepsy, Alzheimer's disease, stroke and Parkinson's disease and for monitoring brain function during sleep, under anesthesia and in patients treated in the intensive care unit. The partnership is lead by Drs. Boas, Bonmassar, Brown, and Hamalainen. Collectively, they are experts in the multi-modal combination of fMRI (All), EEG and MEG (Bonmassar, Brown, and Hamalainen), and DOT (Boas), spannin experiments, analysis, and clinical application.

描述（由申请人提供）：近年来，成像技术的开发以研究大脑功能的发展迅速。 在许多成像中心中，当前研究的一个重要重点是使用功能磁共振成像（fMRI），弥散光学层析成像（DOT），电磁术（EEG）和磁脑电图（MEG）测量的各种功能磁共振成像（FMRI），扩散光学层析成像（DOT）进行多模式图像融合的开发。 这需要进行实验，其中同时或按顺序从两种或多种模态执行成像，以便可以最佳地组合来自不同来源的信息。 同时，使用两种或多个成像方式提供了令人兴奋的前景，即在不同的空间和时间尺度上跟踪大脑活动的动态。 为了响应> PAR-04-023，我们建议在马萨诸塞州艾蒂拉（Athinoula A. 使用EEG，MEG，fMRI和DOT，这种伙伴关系将基于这些成像方式的生物物理学，生理和解剖结构，开发一个综合状态空间，计算框架。 该计算框架的模型组件将通过一系列跨模式实验来识别和验证。 高速超级计算资源将用于设计和测试模拟和实际多模式实验成像数据的状态空间数据分析算法。 数据分析算法是作为该合作伙伴关系的一部分而开发的，交叉模式实验中收集的数据将自由传播到脑成像社区。 该项目的长期目标是为大脑成像社区提供一个统一的计算框架，以结合两种或多种成像方式的测量，这些框架可以在实时研究和患者的临床管理中使用。 大脑功能的实时分析将对理解正常脑功能的动态具有重要意义，这些动态如何在病理状况中发生变化，例如癫痫，阿尔茨海默氏病，中风和帕金森氏病，以及在睡眠期间，麻醉下以及在重症监护病房中治疗的患者在睡眠期间监测大脑功能。 该合作伙伴关系由Drs领导。 Boas，Bonmassar，Brown和Hamalainen。 总的来说，他们是fMRI（ALL），EEG和MEG（Bonmassar，Brown和Hamalainen）以及DOT（BOAS），SPANNIN实验，分析和临床应用的多模式组合的专家。