Computer Aided Diagnosis and Followup of Alzheimer's Disease

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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.