Modern imaging, such as MRI, can provide a safe, non-invasive measurement of the whole brain, and has been increasingly employed for large clinical and research studies of brain development, maturation, and aging, as well as for monitoring the effects of pharmacological interventions over time. This has created a great need for the development of highly automated, accurate, and robust measurement tools for analysis of large neuroimage dataset. Image registration as an important image measurement tool has attracted enormous scientific interest, since it is the key step for integration and comparison of data from different individuals or groups, as well as for the development of statistical atlases that reflect structural and functional variability within a group of individuals. However, most of the current registration algorithms are based on pair-wise registration of an individual brain with a selected template. This independent pair-wise registration and the subjective selection of template can introduce systematic registration error and bias to the aligned images, thus reducing the statistical power in detecting subtle brain changes, e.g., tiny longitudinal structural and functional changes which are important for early detection of Alzheimer's Disease (AD). To resolve these limitations, group-wise registration and inter-group comparison methods have been recently proposed to achieve consistent registration across all subjects by simultaneous registration of all individual subjects to their group mean directly. However, the accuracy and robustness of these group-wise registration methods are limited in identifying tiny brain differences, since the independent estimation of potentially large complex deformations from each subject to the group mean directly can make the initially very similar images (with tiny difference) become very different after registration, due to noise and uncertainty in the registration. Moreover, because of the required simultaneous registration of a large set of images and the limitation of computer memory capability, current group-wise registration methods can handle only a small number of images, e.g., several to dozens.
The first aim of this project is to develop a fast, robust, and accurate group-wise registration algorithm which is able to handle simultaneously a large set of images, e.g., hundreds or thousands of images, by a general computer. Our key idea is to partition a large-scale group-wise registration problem into a series of hierarchical small-scale registration problems, each of which can be handled efficiently by a general computer and can be solved robustly and accurately by simplification of the registration problem. Moreover, for effective comparison of two (or more) groups, i.e., obtained respectively from early-stage diseased patients and normal controls, or from genetically identical twins, we further propose a novel inter- group registration method to effectively align two groups by matching not only their means but also their statistical distributions at all corresponding locations. Thus, the statistical difference between the two groups can be greatly identified, which enables the detection of tiny brain atrophies due to diseases such as those found during the early stage of AD or tiny brain growth differences within twins. This inter-group registration and comparison method can also be extended for the registration of multiple groups, with application in longitudinal study of twins at early neonatal stage. The study of all these novel inter-group registration and comparison methods is the topic of the second aim. Finally, we will apply our developed group-wise registration method, as well as the inter-group registration and comparison method, to the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset for early detection of AD, and to the neonatal dataset for study of tiny brain growth differences within twins. The performance of the proposed method will be extensively validated and also compared with those obtained by pair-wise registration methods as well as by other group-wise registration methods. These studies are the topic of the third aim. The final developed algorithms will be made freely available to the whole research community through NITRC (Neuroimaging Informatics Tools and Resources Clearinghouse), as we did with our HAMMER registration algorithm (www.nitrc.org/projects/hammer/), which is one of the top downloaded tools in NITRC.
This project aims at the development, testing, and evaluation of fast, robust, and accurate group registration and statistical comparison algorithms for effective simultaneous processing of large sets of brain images;to enable the detection of tiny, complex group differences. This is important for early detection of brain diseases (e.g., Alzheimer's Disease) and for identification of tiny brain growth differences within genetically identical twins. The final developed algorithms will be made freely available to the whole research community through NITRC (Neuroimaging Informatics Tools and Resources Clearinghouse), as we did with our HAMMER registration algorithm (www.nitrc.org/projects/hammer/), which is currently one of the top download tools in NITRC.
|Li, Guannan; Liu, Mingxia; Sun, Quansen et al. (2018) Early Diagnosis of Autism Disease by Multi-channel CNNs. Mach Learn Med Imaging 11046:303-309|
|Liu, Mingxia; Gao, Yue; Yap, Pew-Thian et al. (2018) Multi-Hypergraph Learning for Incomplete Multimodality Data. IEEE J Biomed Health Inform 22:1197-1208|
|Lian, Chunfeng; Liu, Mingxia; Zhang, Jun et al. (2018) Automatic Segmentation of 3D Perivascular Spaces in 7T MR Images Using Multi-Channel Fully Convolutional Network. Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson M 2018:|
|Liu, Mingxia; Zhang, Jun; Adeli, Ehsan et al. (2018) Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal 43:157-168|
|Nie, Dong; Wang, Li; Adeli, Ehsan et al. (2018) 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation. IEEE Trans Cybern :|
|Wang, Li; Li, Gang; Adeli, Ehsan et al. (2018) Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism. Hum Brain Mapp 39:2609-2623|
|Chen, Geng; Dong, Bin; Zhang, Yong et al. (2018) Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space. Front Neuroinform 12:57|
|Yin, Q; Hung, S-C; Rathmell, W K et al. (2018) Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma. Clin Radiol 73:782-791|
|Zhang, Yu; Zhang, Han; Chen, Xiaobo et al. (2017) Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment. Connectomics Neuroimaging (2017) 10511:9-16|
|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|
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