Automated Segmentation of Subregions of the Medial Temporal Lobe in in vivo MRI

体内 MRI 内侧颞叶子区域的自动分割

• 批准号: 8101752
• 负责人: Koen Van Leemput
• 金额: $ 48.02万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8101752
• 关键词: Accounting; Adult; Affect; Aging; Algorithms; Alzheimer' s Disease; Atlases; Biological Markers; Brain; Brain region; Clinical; Clinical Research; Communities; Computer Simulation; Computer software; Computing Methodologies; Development; Early Diagnosis; Early treatment; Environment; Evaluation; Goals; Hippocampus (Brain); Human; Image; Individual; Life; Longevity; MRI Scans; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Medial; Memory; Methods; Modeling; Monitor; Neurosciences Research; Positioning Attribute; Procedures; Protocols documentation; Relative (related person); Research Personnel; Resolution; Resources; Scanning; Schizophrenia; Shapes; Site; Staging; Structure; System; Techniques; Technology; Temporal Lobe; Therapeutic Intervention; Tissues; base; community planning; computerized tools; data acquisition; image processing; in vivo; insight; neuroimaging; normal aging; open source; tool; ultra high resolution

DESCRIPTION (provided by applicant): The medial temporal lobe (MTL) is a necessary component in a variety of memory functions, as well as the locus of structural change in aging, Alzheimer's disease (AD), schizophrenia, and other conditions. The distinct subregions composing the MTL, including various subfields of the hippocampus, have been implicated in different memory subsystems, and shown to be differentially affected in normal aging and AD. The ability to reliably and efficiently detect these subregions using in vivo neuroimaging would therefore be of great potential value for both basic neuroscience and clinical research. Such a procedure will provide critical insights into the function and structure of the MTL in the living human brain, and how it is affected in normal aging. It is also an important step in the quest for sensitive, non-invasive biomarkers for early diagnosis and treatment evaluation in AD. The limited resolution of typical MRI scans has traditionally been a major hindrance in imaging studies of the MTL, forcing investigators to treat the hippocampus and surrounding structures as a single entity. Substantial developments in MR data acquisition technology, however, have started to yield images that show anatomical features of the MTL at an unprecedented level of detail, providing the basis for fine-scaled functional and morphological analyses of individual subregions of the MTL. MRI studies of the MTL at the subfield level are currently not widely performed. This is because they require a combination of deep MRI know-how, neuroanatomical expertise, and staffing resources available only at a select few specialized sites. In order to make MRI studies of the MTL at the subfield level more widely accessible, the overall goal of this project is to develop and validate a broadly applicable set of computational tools to automatically segment a multitude of MTL subregions from in vivo MRI images. Specifically, given the extreme versatility of MRI and the lack of standard acquisition protocols for imaging the MTL, we will build tools that can robustly analyze scans of various image resolutions and tissue contrasts. Towards this end, we aim to (1) use manual delineations in ultra-high resolution MRI scans to derive computational models that make predictions about the relative position and shape of MTL subregions, (2) based on these models and on a model of the MRI imaging process, develop and validate a Bayesian framework for fully-automated MTL subregion segmentation in ultra-high resolution MRI scans, and (3) develop and validate such a framework for lower resolution images acquired on systems in more widespread use, by explicitly accounting for the partial volume effect where several structures contribute to form the intensity within a single voxel. In order to disseminate the developed techniques and atlases to the scientific community, we plan to integrate them into an open source package that we will make freely available as part of the FreeSurfer environment. PUBLIC HEALTH RELEVANCE: The ability to reliably measure subtle degenerative changes in small medial temporal lobe (MTL) substructures through in vivo MRI would be an important step towards early diagnosis and staging of Alzheimer's disease, and towards monitoring therapeutic interventions. Such a procedure could also provide unprecedented insights into changes in the MTL structure in the living human brain that accompany normal aging, a crucial clinical and neuroscientific objective as the MTL is a brain region known to be critical in the human memory system.

描述（由申请人提供）：内侧颞叶（MTL）是多种记忆函数中的必要组成部分，也是衰老，阿尔茨海默氏病（AD），精神分裂症和其他疾病的结构变化的基因座。组成MTL的不同子区域，包括海马的各个子场，已与不同的记忆子系统有关，并在正常衰老和AD中受到差异影响。因此，使用体内神经影像学可靠，有效地检测这些子区域的能力对于基本的神经科学和临床研究都具有巨大的潜在价值。这样的程序将为生物中MTL的功能和结构提供关键的见解，以及在正常衰老中如何影响它。这也是寻求敏感的，非侵入性生物标志物的重要一步，用于AD中的早期诊断和治疗评估。 传统上，典型的MRI扫描的分辨率有限，这是MTL成像研究的主要障碍，迫使研究人员将海马和周围结构视为单个实体。但是，MR数据采集技术的实质发展已开始产生图像，这些图像以前所未有的细节级别显示MTL的解剖学特征，为MTL各个子区域的精细缩放功能和形态分析提供了基础。 目前尚未广泛执行MTL的MRI研究。这是因为它们需要深入的MRI知识，神经解剖专业知识和仅在精选的专业网站上可用的人员配备资源的组合。为了更广泛地对子场级别的MTL进行MRI研究，该项目的总体目标是开发和验证一组广泛的计算工具集，以自动从体内MRI图像中自动划分多个MTL子区域。具体而言，鉴于MRI的极端多功能性以及缺乏对MTL进行成像的标准采集方案，我们将构建可以强力分析各种图像分辨率和组织对比的扫描的工具。 Towards this end, we aim to (1) use manual delineations in ultra-high resolution MRI scans to derive computational models that make predictions about the relative position and shape of MTL subregions, (2) based on these models and on a model of the MRI imaging process, develop and validate a Bayesian framework for fully-automated MTL subregion segmentation in ultra-high resolution MRI scans, and (3) develop and validate such通过明确考虑部分体积效应，在系统中获得的较低分辨率图像的框架，其中几个结构有助于在单个体素内形成强度。 为了将开发的技术和地图集传播到科学界，我们计划将它们整合到开源包中，我们将作为FreeSurfer环境的一部分免费提供。 公共卫生相关性：通过体内MRI可靠地衡量小型内侧颞叶（MTL）子结构的细微变性变化的能力将是朝着早期诊断和分期分期的重要一步，以及监测治疗干预措施。这样的程序还可以为伴随正常衰老的MTL结构的变化提供前所未有的见解，因为MTL是人类记忆系统至关重要的大脑区域，这是一个关键的临床和神经科学物镜。