Computer Aided Diagnosis and Followup of Alzheimer's Disease

描述（由申请人提供）： 下一个世纪人口的老龄化将增加阿尔茨海默病已经相当大的个人、社会和政府成本。AD的早期诊断和治疗是AD保健的未来。神经影像学在研究和临床实践中发挥着越来越重要的作用，因为可以开发有效的早期标记物用于疾病检测和监测。这项研究将为阿尔茨海默病的计算机辅助诊断和随访提供新的计算工具，这对公众健康的一个重要问题做出了重大贡献。在获奖期间，申请人的职业发展重点是开发用于计算机辅助诊断和AD随访的新型计算方法。申请人的职业培训重点是1）获得医学影像学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）获得对AD临床诊断和随访的深入了解; 4）获得生物统计学方面的深入知识; 5）获得AD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。在这项为期4年的K 01提案中，申请人将开发用于阿尔茨海默病计算机辅助诊断和随访（CADFAD）的新型神经图像分析算法。具体而言，我们将1）开发和验证基于变形不变属性向量（DIAV）的新型高维体积配准方法;（2）开发和验证基于皮质表面的新型定量方法，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）开发和验证新型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.