Dynamic Inverse Solutions for Multimodal Imaging

多模态成像的动态逆解

• 批准号: 7636751
• 负责人: David A Boas
• 金额: $ 135.61万
• 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/7636751
• 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(申请人提供)：近年来，研究脑功能的成像技术发展迅速。目前许多成像中心的一个重要研究重点是开发功能神经成像工具，以使用功能磁共振成像(FMRI)、漫反射光学层析成像(DOT)、脑电(EEG)和脑磁图(MEG)测量的各种组合来执行多模式图像融合。这需要进行实验，在这些实验中，同时或按顺序从两个或更多个医疗模式进行成像，以便可以最佳地组合来自不同来源的信息。同时使用两种或两种以上的成像方式提供了令人兴奋的前景，可以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&gt；PAR-04-023，我们建议在马萨诸塞州总医院的阿蒂努拉·A·马蒂诺斯生物医学成像中心建立生物工程研究伙伴关系，以开发用于融合来自两个或更多医疗设备的成像测量的计算资源。利用EEG、MEG、fMRI和DOT，这一合作伙伴关系将开发一个基于这些成像模式的生物物理、生理学和解剖学的综合状态空间计算框架。这一计算框架的模型组件将通过一系列跨通道实验来确定和验证。利用高速超级计算资源，对模拟和实际的多模实验成像数据进行状态空间数据分析算法的设计和测试。作为这一伙伴关系的一部分开发的数据分析算法和在跨模式实验中收集的数据将免费传播给大脑成像社区。该项目的长期目标是为脑成像界提供一个统一的计算框架，以结合来自两种或更多种成像模式的测量结果，这些成像模式可用于实时研究和患者的临床管理。脑功能的实时分析将对了解正常脑功能的动力学、这些动力学在癫痫、阿尔茨海默病、中风和帕金森病等病理条件下的变化以及监测睡眠、麻醉和重症监护病房患者的脑功能具有重要意义。该伙伴关系由博阿斯、邦马萨、布朗和哈马莱宁博士领导。总体而言，他们是fMRI(ALL)、EEG和MEG(Bonmasar、Brown和Hamalainen)以及DOT(BoaS)、Spannin实验、分析和临床应用的多模式组合专家。