Development and Dissemination of Robust Brain MRI Measurement Tools

强大的脑 MRI 测量工具的开发和传播

• 批准号: 8725502
• 负责人: Dinggang Shen
• 金额: $ 49.94万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-17 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8725502
• 关键词: Address; Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease risk; Atlases; Biological Markers; Brain; Brain Diseases; Classification; Clinical; Clinical Research; Collaborations; Complex; Computer software; Data; Dementia; Development; Diagnosis; Disease; Documentation; Early Diagnosis; Engineering; Functional Magnetic Resonance Imaging; Goals; Image; Image Analysis; Individual; Intervention; Joints; Label; Lead; Machine Learning; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Medical Imaging; Methods; Modeling; Monitor; Multimodal Imaging; Nerve Degeneration; Outcome; Patients; Pattern; Performance; Phase; Positron-Emission Tomography; Research; Sampling; Schizophrenia; Simulate; Slice; Source Code; Staging; Structure; Testing; Time; Training; Trees; Update; Work; abstracting; base; clinical application; cognitive function; disease diagnosis; flexibility; image registration; image visualization; information classification; mild cognitive impairment; nervous system disorder; neuroimaging; neurological pathology; neuropsychiatry; novel; open source; programs; research study; software development; spatiotemporal; symposium; tool

DESCRIPTION (provided by applicant): Development and Dissemination of Robust Brain MRI Measurement Tools Abstract: Summary. Neuroimaging provides a safe, non-invasive measurement of the whole brain, and has enabled large clinical and research studies for brain development, aging, and disorders. However, many disorders, i.e., major neurodegenerative and neuropsychiatric disorders, cause complex spatiotemporal patterns of brain alteration, which are often difficult to identify visually and compare over time. To address this critical issu, in the renewal phase of this project, we will continue to work with GE Research to develop and disseminate a software package for brain measurement, comparison, and diagnosis. The new tools include 1) a novel tree-based registration and multi-atlases-based segmentation method for precise measurement of brain alteration patterns, and 2) novel pattern classification and regression methods for early detection and longitudinal monitoring of brain disorders. Aims. Currently, most existing atlas-based labeling methods simply warp each atlas independently to the individual brain for multi-atlases-based structural labeling. This could lead to 1) inaccurate labeling due to possible large registration error when the atlases are very different from the target individual brain, and 2) inconsistent labeling of the same brain structure across different individuals due to independent labeling of each individual brain. The first goal of this project is hence to develop a novel tree-based registration and multi-atlases -based segmentation method for simultaneous registration and joint labeling of all individual brains by concurrent consideration of all atlases. With measurements of brain structures and their alteration patterns, univariate analysis methods are often used to understand how the disease affects brain structure and function at a group level. Although this can lead to better understanding of neurological pathology of brain disorders, more sophisticated image analysis methods are urgently needed for quantitative assessment and early diagnosis of brain abnormality at an individual level. Thus, the second goal of this project is to develop various novel machine learning methods for early diagnosis of brain disorders and better quantification of brain abnormality at an individual level. Specifically, we will take Alzheimer's disease (AD), which is the most common form of dementia, as an example for demonstrating the performance of our proposed methods in early diagnosis of AD, as well as in prediction of long-term outcomes of individuals with mild cognitive impairment (MCI). The last goal of this project is to build, for ou developed methods, the respective software modules for the 3D Slicer (a free open-source software package with a flexible modular platform for medical image analysis and visualization, http://www.slicer.org/), to promote the potential clinical applications by using tools in 3D Slicer for preprocessing of patient data and our tools for diagnosis. Again, this software development work will be performed in collaboration with our current collaborator, GE Research, which is a part of the engineering core of the National Alliance for Medical Image Computing (NA-MIC) that is focused on developing 3D Slicer. Both source code and pre-compiled programs will be made freely available. Applications. These methods can find their applications in diverse fields, i.e., quantifying brain abnormality of neurological diseases (i.e., AD and schizophrenia), measuring effects of different pharmacological interventions on the brain, and finding associations between structural and cognitive function variables.

描述（申请人提供）：鲁棒脑MRI测量工具的发展和推广神经影像学为整个大脑提供了一种安全、无创的测量方法，并使大脑发育、衰老和疾病的大型临床和研究成为可能。然而，许多疾病，即主要神经退行性疾病和神经精神疾病，会导致复杂的大脑改变的时空模式，这些模式往往难以通过视觉识别和时间比较。为了解决这一关键问题，在该项目的更新阶段，我们将继续与GE研究公司合作，开发和传播一种用于大脑测量、比较和诊断的软件包。这些新工具包括：1)一种新的基于树的配准和基于多地图集的分割方法，用于精确测量大脑改变模式；2)一种新的模式分类和回归方法，用于大脑疾病的早期检测和纵向监测。目标。目前，大多数基于地图集的标记方法都是简单地将每个地图集独立地弯曲到单个大脑，以进行基于多地图集的结构标记。这可能导致1)当地图集与目标个体大脑非常不同时，由于可能存在较大的配准误差而导致标记不准确；2)由于每个个体大脑的独立标记，不同个体之间相同大脑结构的标记不一致。这个项目的第一个目标是