Computer Aided Early Detection and Diagnosis of Alzheimer's Disease

描述（由申请人提供）：本项目的目标是培养候选人在神经影像分析方面进行多学科研究的能力，重点是多变量神经影像分类及其在阿尔茨海默病（AD）计算机辅助早期检测和诊断中的应用。该研究项目将专注于开发新的神经图像分类算法，以便以全自动和集成的方式准确识别和测量大脑异常。候选人将通过集成特征提取，特征选择和分类来开发一个通用的集成神经图像分类框架。在这个总体框架内，将开发一种多模态模式分类方法，改进结构和功能图像的特征提取。这些多模态多变量分类方法将被验证并应用于基于神经影像的AD早期诊断和与AD相关的脑异常纵向测量研究。作为拟议的KOI申请的一部分，候选人寻求医学成像，神经科学，AD临床诊断和生物统计学方面的教学培训。拟议的培训和研究计划将促进候选人发展成为一名独立的科学家，使用神经成像和图像分析方法早期发现和诊断阿尔茨海默病。 相关性：改进的神经图像分类方法将有助于早期发现和诊断阿尔茨海默病。全自动神经影像分类软件的发布将显著提高用于神经影像分析的高维模式分类算法的互操作性和可采用性，并导致更广泛的神经影像研究社区增强此类工具和资源的传播、采用和发展。

DESCRIPTION (provided by applicant): The goal of this project is to develop the candidate's ability to perform multidisciplinary research in neuroimage analysis, with emphasis on multivariate neuroimage classification and its application to computer aided early detection and diagnosis of Alzheimer's disease (AD). The research project will focus on the development of novel neuroimage classification algorithms for accurately identifying and measuring brain abnormality in a fully automatic and integrated way. The candidate will develop a general integrated neuroimage classification framework by integrating feature extraction, feature selection, and classification. Within this general framework, a multimodality pattern classification method with improved feature extraction from both structural and functional images will be developed. These multimodality multivariate classification methods will be validated and applied to the neuroimage based studies of early AD diagnosis and longitudinal measurement of brain abnormality related to AD. As part of the proposed KOI application, the candidate seeks didactic training in medical imaging, neuroscience, clinical diagnosis of AD, and biostatistics. The proposed training and research plan will foster the candidate's development into an independent scientist, using neuroimaging and image analysis methods for early detection and diagnosis of Alzheimer's disease. RELEVANCE: The improved neuroimage classification methods will help early detection and diagnosis of Alzheimer's disease. The release of the fully automatic neuroimage classification software will significantly improve the interoperability and adoptability of high dimensional pattern classification algorithms for neuroimage analysis, and result in enhanced dissemination, adoption, and evolution of such tools and resources by the broader neuroimaging research community.