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: 8446307
• 负责人: Koen Van Leemput
• 金额: $ 45.3万
• 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/8446307
• 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.

描述（由申请人提供）：内侧颞叶（MTL）是各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的结构变化部位。构成MTL的不同子区域，包括海马的各个子区域，已经被牵连到不同的记忆子系统中，并且显示出在正常老化和AD中受到不同的影响。因此，使用体内神经成像可靠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。这样的程序将提供关键的见解MTL的功能和结构在活的人类大脑，以及它是如何在正常老化的影响。这也是寻求用于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扫描的分辨率有限一直是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单一实体。然而，磁共振数据采集技术的重大发展，已经开始产生的图像，显示解剖特征的MTL在一个前所未有的详细程度，提供了精细的功能和形态分析的MTL的各个子区域的基础。 磁共振成像研究的MTL在子场水平目前还没有广泛进行。这是因为它们需要深入的MRI专业知识，神经解剖学专业知识和仅在少数几个专业站点可用的人力资源的组合。为了使磁共振成像研究的MTL在更广泛的子场水平，本项目的总体目标是开发和验证一套广泛适用的计算工具，自动分割的MTL子区域的众多在体内MRI图像。具体来说，鉴于MRI的极端多功能性和缺乏标准的采集协议成像的MTL，我们将建立工具，可以强大地分析各种图像分辨率和组织对比度的扫描。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对MTL子区域的相对位置和形状进行预测的计算模型，（2）基于这些模型和MRI成像过程的模型，开发并验证用于超高分辨率MRI扫描中的全自动MTL子区域分割的贝叶斯框架，以及（3）通过明确地考虑部分体积效应，开发并验证用于在更广泛使用的系统上采集的较低分辨率图像的这种框架，其中几个结构有助于形成单个体素内的强度。 为了向科学界传播所开发的技术和地图集，我们计划将它们整合到一个开源软件包中，我们将作为FreeSurfer环境的一部分免费提供。