Automated Analysis of Healthy and Diseased Brain Tissue

健康和患病脑组织的自动分析

• 批准号: 6936484
• 负责人: Bruce Fischl
• 金额: $ 42.66万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2008-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6936484
• 关键词: bioimaging /biomedical imaging; biomedical automation; brain imaging /visualization /scanning; brain mapping; brain morphology; clinical research; computer assisted diagnosis; human subject; image enhancement; magnetic resonance imaging; morphometry

DESCRIPTION (provided by applicant): Neurodegenerative and psychiatric disorders, as well as healthy aging, are all frequently associated with structural changes in the brain. These changes can cause alterations in the imaging properties of brain tissue, as well as changes in morphometric properties of the brain, such as volume, folding and surface area. This can be problematic, as analysis techniques that quantify morphometric changes without accounting for variability in the imaging properties of the tissue are liable to generate erroneous results in situations in which the tissue parameters have changed. In this grant application support is sought to construct a set of accurate and automated tools for the analysis of structural neuroimaging data. These tools will quantify alterations in brain morphometry, as well as changes in the tissue parameters that give rise to image contrast in magnetic resonance images. It is hypothesized that explicitly basing the analysis tools upon knowledge of the underlying physical principles that govern the imaging process will allow the characterization of subtle structural changes that have previously gone undetected. Aim 1 of this application is to develop a set of scans and optimization techniques that will allow for the accurate estimation of the underlying tissue parameters (i.e., T1, T2, proton density). Additional effort will be focused on removing various sources of inaccuracies in the data acquisition. This includes using optimization techniques to derive MR protocols with optimal contrast-to-noise, as well as the correction of various sources of distortion that arise in MR images such as gradient nonlinearities and real-time online correction of within-scan subject motion. This latter technique is of particular importance, as it will allow the tools to be applied to patient populations for which within-scan motion is frequently problematic. Aim 2 is to use a database of manually labeled datasets as the basis for the construction of an automated whole-brain segmentation procedure designed to assign a neuroanatomical label to every voxel in the brain (e.g. thalamus, caudate, putamen, etc). The segmentation procedure will disambiguate structures with similar tissue properties based on their location within the brain, as well as their spatial relationships to neighboring structures, encoded using an anisotropic markov random field. It is important to note that basing the segmentation upon the intrinsic tissue parameters renders the procedure largely insensitive to the details of a particular pulse sequence. Aim 3 is to employ the multi-spectral tissue parameters as the basis for surface-based morphometric analysis. The final aim is to validate the accuracy of the procedures, as well as their robustness to changes in scanner protocol. Upon completion of tool development they will be applied to the study of a variety of disorders, focusing on schizophrenia. Alzheimer's and Huntington's disease.

描述（由申请人提供）：神经退行性疾病和精神疾病以及健康衰老都经常与大脑结构变化相关。这些变化会导致脑组织成像特性的改变，以及大脑形态特性的变化，例如体积、折叠和表面积。这可能是有问题的，因为在不考虑组织成像特性变化的情况下量化形态变化的分析技术很容易在组织参数发生变化的情况下产生错误的结果。在本次拨款申请中，寻求支持构建一套用于分析结构神经影像数据的准确且自动化的工具。这些工具将量化大脑形态测量的变化，以及引起磁共振图像图像对比度的组织参数的变化。据推测，明确地将分析工具建立在控制成像过程的基本物理原理的知识之上，将允许表征以前未被检测到的微妙结构变化。 该应用的目标 1 是开发一套扫描和优化技术，以准确估计基础组织参数（即 T1、T2、质子密度）。额外的工作将集中于消除数据采集中各种不准确的来源。这包括使用优化技术来导出具有最佳噪声对比度的 MR 协议，以及校正 MR 图像中出现的各种失真源，例如梯度非线性和扫描内对象运动的实时在线校正。 后一种技术特别重要，因为它将允许将工具应用于扫描内运动经常出现问题的患者群体。 目标 2 是使用手动标记数据集的数据库作为构建自动化全脑分割程序的基础，该程序旨在为大脑中的每个体素（例如丘脑、尾状核、壳核等）分配神经解剖学标签。分割过程将根据具有相似组织特性的结构在大脑中的位置以及它们与相邻结构的空间关系消除歧义，并使用各向异性马尔可夫随机场进行编码。值得注意的是，基于内在组织参数的分割使得该过程对特定脉冲序列的细节很大程度上不敏感。 目标 3 是采用多光谱组织参数作为基于表面的形态测量分析的基础。最终目的是验证程序的准确性及其对扫描仪协议变化的稳健性。工具开发完成后，它们将应用于各种疾病的研究，重点是精神分裂症。阿尔茨海默氏症和亨廷顿舞蹈症。

项目成果

A hybrid approach to the skull stripping problem in MRI.

MRI 中颅骨剥离问题的混合方法。

• 发表时间: 2004-07
• 影响因子: 5.7
• 作者: Ségonne, F;Dale, A M;Busa, E;Glessner, M;Salat, D;Hahn, H K;Fischl, B
• 通讯作者: Fischl, B

On-line automatic slice positioning for brain MR imaging.

用于脑部 MR 成像的在线自动切片定位。

• 发表时间: 2005-08-01
• 作者: van der Kouwe, Andre J W;Benner, Thomas;Fischl, Bruce;Schmitt, Franz;Salat, David H;Harder, Martin;Sorensen, A Gregory;Dale, Anders M
• 通讯作者: Dale, Anders M

Focal thinning of the cerebral cortex in multiple sclerosis.

多发性硬化症中大脑皮层的局灶性变薄。

• 发表时间: 2003-08
• 作者: Sailer, Michael;Fischl, Bruce;Salat, David;Tempelmann, Claus;Schonfeld, Mircea Ariel;Busa, Evelina;Bodammer, Nils;Heinze, Hans;Dale, Anders
• 通讯作者: Dale, Anders

Sequence-independent segmentation of magnetic resonance images.

磁共振图像的序列独立分割。

• 发表时间: 2004
• 影响因子: 5.7
• 作者: Fischl, Bruce;Salat, David H;van der Kouwe, André J W;Makris, Nikos;Ségonne, Florent;Quinn, Brian T;Dale, Anders M
• 通讯作者: Dale, Anders M

Regional cortical thinning in preclinical Huntington disease and its relationship to cognition.

临床前亨廷顿病的区域皮质变薄及其与认知的关系。

• 发表时间: 2005-09-13
• 影响因子: 9.9
• 作者: Rosas, H D;Hevelone, N D;Zaleta, A K;Greve, D N;Salat, D H;Fischl, B
• 通讯作者: Fischl, B