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High Throughput web-base Image Analysis of Mouse Brain MR Imaging Studies

小鼠脑 MR 成像研究的高通量网络图像分析

基本信息

批准号：
7446753
负责人：
Martin Andreas Styner
金额：
$ 19.98万
依托单位：
KITWARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-06-15 至 2009-07-01
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7446753
关键词：
Adult Algorithms Amygdaloid structure Animal Model Animals Archives Atlases Autistic Disorder Back Behavioral Brain Brain region California Cerebellum Classification Client Clinical Computer software Computing Methodologies Corpus striatum structure Data Data Collection Data Set Development Diffusion Magnetic Resonance Imaging Disease Documentation Electronic Mail Environment FMR1 Fragile X Syndrome Generations Generic Drugs Genetic Genetic screening method Genotype Graph Hippocampus (Brain)Human Image Image Analysis Imagery Information Systems Institutes Internet Knock-out MRI Scans Magnetic Resonance Imaging Manuals Measures Mental disorders Methods Modality Modeling Molecular Morphology Mus Nature Neuroanatomy Numbers Online Systems Outcome Pathology Patients Pharmaceutical Preparations Phase Phenotype Population Population Study Process Protocols documentation Psychiatry Publications Publishing Quality Control Rattus Reproducibility Research Research Infrastructure Research Personnel Resolution Resources Risk Running Scanning Schizophrenia Shapes Side Slice Slide Specific qualifier value Standards of Weights and Measures System Techniques Technology Tectum Mesencephali Testing Time Tissues Validation Variant base brain morphology cluster computing compliance behavior computerized data processing cost image processing interest morphometry mouse model size success web based interface web interface

项目摘要

DESCRIPTION (provided by applicant): In the last decade, MRI studies of human brain morphometry have been used to investigate a multitude of pathologies and drug-related effects in psychiatric research. The morphometric measures that differentiate patient populations or track longitudinal changes are often subtle and require a large number of subjects or repeated studies to detect and statistically model with significance. Cost, patient compliance, risks to the patients, and the rarity of certain diseases often limit traditional, clinical morphometric studies. These complications have motivated the use of model organisms such as of mice and rats. Animal studies are also very popular due to their small size and rapid development cycle, the wealth of genotype and phenotype data, as well as the maturity of the technology to manipulate their genetic information to induce disease. Brain morphometry models of rat and mice typically involve histological slides, behavioral data, genetic testing, and, increasingly, MRI scans. In particular, in addition to brain morphometry, MRI scans are being employed as a hypothesis generation method for focused histological and molecular examinations, and for strain comparisons. Effective methods have been developed for extracting brain morphometry from human MRI scans. We are leaders in the field for their development and their application. We have developed Legrendre polynomial methods for MRI bias correction methods, atlas-based methods for tissue classification, and spherical harmonics techniques for shape parameterization. We have applied these methods to correlate hippocampus shape variations that distinguish patients suffering from schizophrenia. By contrast, few automated quantitative analysis methods exist for small animal MRI. The standard is to manually outline brain features in MRI slices for a large number of animals, and such manual methods lack reproducibility and are extremely time consuming. The lack of automated MRI analysis methods is the limiting factor in many animal studies. We propose to develop automatic, reliable, high-throughput MR image analysis methods for small animal, brain morphometry studies. Additionally, we propose to develop an intuitive web-based interface for collecting and distributing the imaging data of small animal studies as well as initiating the processing of that data on a distributed processing network. The web-based data sharing and processing system also supports the inspection of the ongoing processing and the examination of the computed results. This web-based processing system is generic in nature and can be extended to host and process human MRI data as well as data from other modalities and other applications. To demonstrate and evaluate the data system, we will apply it to the study of the neuroanatomy of a fragile-X syndrome mouse model. This mouse model Is based on a knockout of the FMR1 mouse model, and it has shown behavioral deficits consistent with a Fragile X/autism human phenotype. The proposed software will advance murine MRI studies of morphometry and connectivity for neuro-developmental, and neuro-degenerative psychiatry diseases. The analysis of MR images of entire brain studies will become the matter of a few mouseclicks on a web-interface.

描述（由申请人提供）：在过去的十年中，人脑形态测量的 MRI 研究已被用于调查精神病学研究中的多种病理学和药物相关效应。区分患者群体或跟踪纵向变化的形态测量通常很微妙，需要大量受试者或重复研究才能检测和建立有意义的统计模型。成本、患者依从性、患者风险以及某些疾病的罕见性常常限制传统的临床形态测量研究。这些并发症促使人们使用模型生物，例如小鼠和大鼠。动物研究也因其规模小、开发周期快、基因型和表型数据丰富、以及操纵其遗传信息诱发疾病的技术成熟而非常受欢迎。大鼠和小鼠的脑形态测量模型通常涉及组织学切片、行为数据、基因测试以及越来越多的 MRI 扫描。特别是，除了脑形态测量之外，MRI 扫描还被用作假设生成方法，用于集中组织学和分子检查以及应变比较。已经开发出了从人体 MRI 扫描中提取大脑形态测量的有效方法。我们是其开发和应用领域的领导者。我们开发了用于 MRI 偏差校正方法的 Legrendre 多项式方法、用于组织分类的基于图谱的方法以及用于形状参数化的球谐函数技术。我们应用这些方法来关联海马形状变化，以区分患有精神分裂症的患者。相比之下，小动物 MRI 的自动定量分析方法很少。标准是在大量动物的 MRI 切片中手动勾画出大脑特征，这种手动方法缺乏重复性并且极其耗时。缺乏自动化 MRI 分析方法是许多动物研究的限制因素。我们建议开发自动、可靠、高通量的 MR 图像分析方法，用于小动物、大脑形态测量研究。此外，我们建议开发一个直观的基于网络的界面，用于收集和分发小动物研究的成像数据，并在分布式处理网络上启动该数据的处理。基于网络的数据共享和处理系统还支持对正在进行的处理的检查和对计算结果的检查。这种基于网络的处理系统本质上是通用的，可以扩展到托管和处理人体 MRI 数据以及来自其他模式和其他应用程序的数据。为了演示和评估该数据系统，我们将其应用于脆性 X 综合征小鼠模型的神经解剖学研究。该小鼠模型基于 FMR1 小鼠模型的敲除，并且表现出与脆性 X 病/自闭症人类表型一致的行为缺陷。拟议的软件将推进小鼠 MRI 对神经发育和神经退行性精神疾病的形态测量和连接的研究。对整个大脑研究的 MR 图像进行分析将只需在网络界面上点击几下鼠标即可。