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Computer Aided Diagnosis and Followup of Alzheimer's Disease

阿尔茨海默病的计算机辅助诊断和随访

基本信息

批准号：
7898894
负责人：
Tianming Liu
金额：
$ 11.9万
依托单位：
UNIVERSITY OF GEORGIA
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-08-20 至 2012-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7898894
关键词：
Aging Algorithms Alzheimer&apos s Disease Automobile Driving Autopsy Award Biological Markers Biometry Brain Characteristics Clinical Cognitive Computer-Assisted Diagnosis Computing Methodologies Data Data Set Detection Development Diagnosis Diffusion Disease Early Diagnosis Emotional Evaluation Explosion Family Follow-Up Studies Future General Population Hand Healthcare Image Imagery Knowledge Least-Squares Analysis Magnetic Resonance Imaging Maps Measurement Measures Medical Imaging Methods Modeling Monitor Neuroanatomy Neurobiology Neurologic Neurology Patients Physicians Play Population Principal Investigator Process Property Public Health Reproducibility Research Role Sample Size Scanning Series Societies Solutions Specialist Surface Systems Analysis Thick Time Tissues Training Translating base career career development clinical Diagnosis clinical practice computer framework computerized tools cost disability experience follow-up gray matter neurogenetics neuroimaging neuropathology novel predictive modeling programs reconstruction social theories tool vector

项目摘要

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.

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新的计算机辅助诊断和AD随访的计算方法。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。具体来说，我们将1）开发并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）开发并验证新型基于皮质表面的定量方法，包括皮质表面重建、配准、皮质属性映射、统计推断和可视化;（3）开发并验证新型灰质扩散率定量方法。

项目成果

期刊论文数量（37）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A computational model of cerebral cortex folding.

DOI：
10.1007/978-3-642-04271-3_56
发表时间：
2009
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
0
作者：
通讯作者：

Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients.

用于表征和区分 PTSD 患者的动态功能连接组学特征

DOI：
10.1002/hbm.22290
发表时间：
2014-04
期刊：
HUMAN BRAIN MAPPING
影响因子：
4.8
作者：
Li, Xiang;Zhu, Dajiang;Jiang, Xi;Jin, Changfeng;Zhang, Xin;Guo, Lei;Zhang, Jing;Hu, Xiaoping;Li, Lingjiang;Liu, Tianming
通讯作者：
Liu, Tianming

Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles.

通过最大化结构连接配置文件的一致性来优化功能性大脑投资回报率。

DOI：
10.1016/j.neuroimage.2011.08.037
发表时间：
2012
期刊：
NeuroImage
影响因子：
5.7
作者：
Zhu,Dajiang;Li,Kaiming;Faraco,CarlosCesar;Deng,Fan;Zhang,Degang;Guo,Lei;Miller,LStephen;Liu,Tianming
通讯作者：
Liu,Tianming

Automatic cortical sulcal parcellation based on surface principal direction flow field tracking.