Computer Aided Diagnosis and Followup of Alzheimer's Disease

描述(由申请人提供)： 在接下来的四分之一个世纪里，人口老龄化将增加阿尔茨海默氏症已经相当可观的个人、社会和政府成本。AD医疗保健的未来在于AD的早期诊断和治疗。神经影像在研究和临床实践中发挥着越来越重要的作用，因为可以开发出有效的早期标志物来检测和监测疾病。这项研究将为阿尔茨海默病的计算机辅助诊断和跟踪提供新的计算工具，这是对一般公共卫生的一个重要问题的重大贡献。在获奖期间，申请者的职业发展重点是为AD的计算机辅助诊断和随访开发新的计算方法。申请者的职业培训重点是1)获得医学影像方面的深入知识和实践经验；2)获得临床神经解剖学方面的深入知识；3)对AD的临床诊断和随访有深入的了解；4)获得深入的生物统计学知识；5)获得AD的神经病理学、神经生物学、神经学、神经遗传学方面的中等知识。在这份为期4年的K01计划中，申请者将开发新的神经图像分析算法，用于阿尔茨海默病的计算机辅助诊断和后续治疗(CADFAD)。具体地说，我们将1)开发和验证基于形变不变属性向量(DIAV)的新的高维体配准方法；(2)开发和验证基于皮质表面的新的定量方法，包括皮质表面重建、配准、皮质属性映射、统计推断和可视化；以及(3)开发和验证新的灰质扩散率定量方法。

DESCRIPTION (provided by applicant): The aging of the population over the next quarter century will increase the already substantial personal, social and governmental costs of Alzheimer's disease. The future of healthcare of AD lies in the early diagnosis and treatment of AD. Neuroimaging is playing an increasingly critical role in research and clinical practice as valid early markers could be developed for both disease detection and monitoring. This research will come up with novel computational tools for computer aided diagnosis and followup of Alzheimer's disease, which is a substantial contribution to an important problem of general public health. During the award period, the applicant's career development focuses on developing novel computational methods for computer aided diagnosis and follow-up of AD. The applicant's career training focuses on 1) obtaining in-depth knowledge and hands-on experience in medical imaging; 2) obtaining in- depth knowledge in clinical neuroanatomy; 3) obtaining in-depth understanding of clinical diagnosis and follow-up of AD; 4) obtaining in-depth knowledge of biostatistics; 5) obtaining moderate knowledge in neuropathology, neurobiology, neurology, neurogenetics of AD. In this 4-year K01 proposal, the applicant will develop novel neuroimage analysis algorithms for Computer Aided Diagnosis and Follow-up of Alzheimer's Diseases (CADFAD). Specifically, we will 1) Develop and validate novel high-dimensional volume registration method based on deformation invariant attribute vectors (DIAV); (2) Develop and validate novel cortical surface based quantitation methods, including cortical surface reconstruction, registration, cortical attributes mapping, statistical inference, and visualization; and (3) Develop and validate novel gray matter diffusivity quantitation methods.