Alzheimer's disease (AD) is the most common cause of dementia in elderly people worldwide. Due to the fact that the longitudinal abnormality associated with AD can be detected in vivo, neuroimaging measures have been playing an increasingly important role in searching for biomarkers of AD that can be used for early diagnosis, progression monitoring, and therapy responses measurement. The goal of this project is to create a set of cutting-edge computational tools for identifying very early biomarkers of AD and alert AD progression via longitudinal image analysis. The key to achieve high sensitivity and specificity AD diagnosis of individuals is the stable longitudinal measurements. In light of this, the candidate will first develop a novel learning-based 4D (3D+t) landmark detector (Aim 1) to find the landmark locations at all time points simultaneously. The trajectories of landmark set form a compact 4D shape representation to achieve temporal consistency in registering longitudinal image sequence to the template (Aim 2). As a result, the consistency of low level features extracted from MR or PET images can be significantly improved. Since the subject-specific patterns of structural/functional changes, although more relevant to AD diagnosis of individuals, is very subtle compared to huge variation across subjects, the candidate will further develop a supervised deep neural network to learn the latent high-level spatial-temporal patterns by coupled stacked auto-encoder and temporal max pooling (Aim 2). The learned spatial-temporal morphological patterns consist of (1) cross- sectional features from MR and PET images, and (2) dynamic short/long term longitudinal patterns w.r.t. different number of historical time points. In a clinical setting, not all patients have a large and complete set of neuroimaging data, or an equal number of imaging scans. In order to eventually translate to clinic arena, a novel AD detector that uses a spatial-temporal hyper-graph learning framework (Aim 3) is proposed to not only provide high sensitivity and specificity in AD diagnosis but also solve the above difficulties. To evaluate the diagnostic value, we will apply our trained AD detector to ADNI dataset and patient data collected in UNC (Aim 3). Finally, we will package all our developed methods into a software package and release it freely to the neuroimaging community, to facilitate other AD-biomarker-exploration projects performed in other institutes.
This proposal aims for the development of novel neuroimaging analysis methods for early diagnosis of Alzheimer's disease using longitudinal image data. In particular, a set of automated algorithms will be created to aid the difficult task of analyzing subtle and complex morphological change patterns of Alzheimer's disease during the disease progression. The successful completion of this project will represent an import milestone in neuroimaging research by bringing forth the fruition of a powerful and practical system for early diagnosis of Alzheimer's disease at individual level, for potential effective treatment.
|Sanroma, Gerard; Benkarim, Oualid M; Piella, Gemma et al. (2018) Learning non-linear patch embeddings with neural networks for label fusion. Med Image Anal 44:143-155|
|Adeli, Ehsan; Thung, Kim-Han; An, Le et al. (2018) Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises. IEEE Trans Pattern Anal Mach Intell :|
|Zhu, Yingying; Zhu, Xiaofeng; Kim, Minjeong et al. (2017) A Novel Dynamic Hyper-Graph Inference Framework for Computer Assisted Diagnosis of Neuro-Diseases. Inf Process Med Imaging 10265:158-169|
|Zu, C; Gao, Y; Munsell, B et al. (2017) Learning Subnetwork Biomarkers via Hypergraph for Classification of Autism Disease. Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson M 2017:1719|
|Wei, Lifang; Cao, Xiaohuan; Wang, Zhensong et al. (2017) Learning-based deformable registration for infant MRI by integrating random forest with auto-context model. Med Phys 44:6289-6303|
|Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz et al. (2017) Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson's Disease. Sci Rep 7:41069|
|Song, Yantao; Wu, Guorong; Bahrami, Khosro et al. (2017) Progressive multi-atlas label fusion by dictionary evolution. Med Image Anal 36:162-171|
|Shen, Dinggang; Wu, Guorong; Suk, Heung-Il (2017) Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng 19:221-248|
|Hu, Shunbo; Wei, Lifang; Gao, Yaozong et al. (2017) Learning-based deformable image registration for infant MR images in the first year of life. Med Phys 44:158-170|
|Zhu, Yingying; Zhu, Xiaofeng; Kim, Minjeong et al. (2017) A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity. Inf Process Med Imaging 10265:398-410|
Showing the most recent 10 out of 23 publications