Development and Dissemination of Robust Brain MRI Measurement Tools

描述(申请人提供)：开发和传播健壮的脑MRI测量工具摘要：摘要。神经成像提供了对整个大脑的安全、非侵入性测量，并使关于大脑发育、衰老和疾病的大型临床和研究研究成为可能。然而，许多疾病，即主要的神经退行性疾病和神经精神疾病，会导致复杂的脑改变时空模式，通常难以通过视觉识别和随着时间的推移进行比较。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 Research合作开发和传播用于大脑测量、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图谱的分割方法，用于精确测量大脑变化模式；2)新的模式分类和回归方法，用于早期检测和纵向监测大脑疾病。目标。目前，大多数现有的基于地图集的标记方法只是将每个地图集独立地扭曲到单个大脑，以用于基于多个地图集的结构标记。这可能会导致1)当图谱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，以及2)由于每个个体大脑的独立标记，不同个体之间对相同的大脑结构的标记不一致。这个项目的第一个目标是 因此，提出了一种新的基于树的配准和基于多图谱的分割方法，用于同时考虑所有地图集的所有个体大脑的同时配准和联合标记。通过测量大脑结构及其变化模式，单变量分析方法经常被用来了解疾病如何在群体水平上影响大脑结构和功能。虽然这可以更好地了解脑疾病的神经病理，但迫切需要更复杂的图像分析方法来在个体水平上对脑异常进行定量评估和早期诊断。因此，该项目的第二个目标是开发各种新的机器学习方法，用于早期诊断大脑疾病，并在个体水平上更好地量化大脑异常。具体地说，我们将以阿尔茨海默病(AD)为例，这是最常见的痴呆症形式，以展示我们提出的方法在AD早期诊断以及预测轻度认知障碍(MCI)患者长期结果方面的性能。该项目的最终目标是为OU开发的方法构建3D Slicer(一个免费的开源软件包，具有灵活的模块化平台用于医学图像分析和可视化，http://www.slicer.org/)，)的相应软件模块，通过使用3D Slicer中的工具来促进潜在的临床应用 用于患者数据的预处理和我们的诊断工具。同样，这项软件开发工作将与我们目前的合作者GE Research合作进行，GE Research是全国医学图像计算联盟(NA-MIC)专注于开发3D切片器的工程核心的一部分。源代码和预编译程序都将免费提供。申请。这些方法可以在不同的领域找到它们的应用，即量化神经疾病(如阿尔茨海默病和精神分裂症)的大脑异常，测量不同药物干预对大脑的影响，以及寻找结构变量和认知功能变量之间的关联。 公共卫生相关性：项目描述该项目旨在开发一种新的方法，通过GroupWise注册和通过多个手动标记图集对所有单独的图像进行联合标记来准确测量大脑结构和功能。此外，还将开发几种新的脑异常测量工具，以使用多模式成像和非成像数据对大脑疾病进行早期检测和进展监测。通过成功开发这些大脑测量工具，将开发相应的软件模块，并通过与GE Research的合作进一步将其整合到3D Slicer中，GE Research是国家医学图像计算联盟(NA-MIC)工程核心的一部分。源代码和预编译程序都将免费提供。

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. PUBLIC HEALTH RELEVANCE: Description of Project This project aims to develop a novel method for accurate measurement of brain structure and function by groupwise registration and joint labeling of all individual images via multiple manual-labeled atlases. Moreover, several novel tools for brain abnormality measurement will also be developed for early detection and progression monitoring of brain disorders using both multimodal imaging and non-imaging data. By successful development of these brain measurement tools, the respective software modules will be developed and further incorporated into 3D Slicer via collaboration with GE Research, which is a part of the engineering core of the National Alliance for Medical Image Computing (NA-MIC). Both source code and pre-compiled programs will be made freely available.