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: 8642178
• 负责人: Koen Van Leemput
• 金额: $ 45.9万
• 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 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8642178
• 关键词: Accounting; Adult; Affect; Aging; Algorithms; Alzheimer' s Disease; Atlases; Bayesian Modeling; 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.

描述（由申请人提供）：内侧颞叶（MTL）是多种记忆功能的必要组成部分，也是衰老、阿尔茨海默病（AD）、精神分裂症和其他疾病中结构变化的场所。组成 MTL 的不同子区域，包括海马体的各个子区域，与不同的记忆子系统有关，并且在正常衰老和 AD 中受到不同的影响。因此，使用体内神经影像可靠且有效地检测这些子区域的能力对于基础神经科学和临床研究都具有巨大的潜在价值。这样的程序将为活人大脑中 MTL 的功能和结构以及它在正常衰老过程中如何受到影响提供重要的见解。这也是寻找用于 AD 早期诊断和治疗评估的敏感、非侵入性生物标志物的重要一步。 传统上，典型 MRI 扫描的有限分辨率一直是 MTL 成像研究的主要障碍，迫使研究人员将海马体和周围结构视为一个整体。然而，MR 数据采集技术的实质性发展已经开始产生以前所未有的细节水平显示 MTL 解剖特征的图像，为 MTL 各个子区域的精细功能和形态分析提供了基础。 MTL 亚场水平的 MRI 研究目前尚未广泛开展。这是因为它们需要结合深厚的 MRI 专业知识、神经解剖学专业知识以及仅在少数几个专业站点提供的人力资源。为了使 MTL 在子场水平上的 MRI 研究更容易获得，该项目的总体目标是开发和验证一套广泛适用的计算工具，以从体内 MRI 图像中自动分割多个 MTL 子区域。具体来说，鉴于 MRI 的多功能性以及缺乏用于 MTL 成像的标准采集协议，我们将构建能够稳健分析各种图像分辨率和组织对比度扫描的工具。为此，我们的目标是（1）在超高分辨率 MRI 扫描中使用手动描绘来导出计算模型，以预测 MTL 子区域的相对位置和形状，（2）基于这些模型和 MRI 成像过程模型，开发和验证超高分辨率 MRI 扫描中全自动 MTL 子区域分割的贝叶斯框架，以及（3）开发和验证此类框架 一种在更广泛使用的系统上获取较低分辨率图像的框架，通过明确考虑部分体积效应，其中多个结构有助于形成单个体素内的强度。 为了向科学界传播开发的技术和图集，我们计划将它们集成到一个开源包中，并将其作为 FreeSurfer 环境的一部分免费提供。