Development and Dissemination of Robust Brain MRI Measurement Tools Abstract: Summary. Neuroimaging provides a safe, non-invasive measurement of the whole brain, and has enabled large clinical and research studies for brain development, aging, and disorders. However, many disorders, i.e., major neurodegenerative and neuropsychiatric disorders, cause complex spatiotemporal patterns of brain alteration, which are often difficult to identify visually and compare over time. To address this critical issu, in the renewal phase of this project, we will continue to work with GE Research to develop and disseminate a software package for brain measurement, comparison, and diagnosis. The new tools include 1) a novel tree-based registration and multi-atlases-based segmentation method for precise measurement of brain alteration patterns, and 2) novel pattern classification and regression methods for early detection and longitudinal monitoring of brain disorders.
Aims. Currently, most existing atlas-based labeling methods simply warp each atlas independently to the individual brain for multi-atlases-based structural labeling. This could lead to 1) inaccurate labeling due to possible large registration error when the atlases are very different from the target individual brain, and 2) inconsistent labeling of the same brain structure across different individuals due to independent labeling of each individual brain. The first goal of this project is hence to develop a novel tree-based registration and multi-atlases -based segmentation method for simultaneous registration and joint labeling of all individual brains by concurrent consideration of all atlases. With measurements of brain structures and their alteration patterns, univariate analysis methods are often used to understand how the disease affects brain structure and function at a group level. Although this can lead to better understanding of neurological pathology of brain disorders, more sophisticated image analysis methods are urgently needed for quantitative assessment and early diagnosis of brain abnormality at an individual level. Thus, the second goal of this project is to develop various novel machine learning methods for early diagnosis of brain disorders and better quantification of brain abnormality at an individual level. Specifically, we will take Alzheimer's disease (AD), which is the most common form of dementia, as an example for demonstrating the performance of our proposed methods in early diagnosis of AD, as well as in prediction of long-term outcomes of individuals with mild cognitive impairment (MCI). The last goal of this project is to build, for ou developed methods, the respective software modules for the 3D Slicer (a free open-source software package with a flexible modular platform for medical image analysis and visualization, www.slicer.org/), to promote the potential clinical applications by using tools in 3D Slicer for preprocessing of patient data and our tools for diagnosis. Again, this software development work will be performed in collaboration with our current collaborator, GE Research, which is a part of the engineering core of the National Alliance for Medical Image Computing (NA-MIC) that is focused on developing 3D Slicer. Both source code and pre-compiled programs will be made freely available. Applications. These methods can find their applications in diverse fields, i.e., quantifying brain abnormality of neurological diseases (i.e., AD and schizophrenia), measuring effects of different pharmacological interventions on the brain, and finding associations between structural and cognitive function variables.

Public Health Relevance

Description of Project This project aims to develop a novel method for accurate measurement of brain structure and function by groupwise registration and joint labeling of all individual images via multiple manual-labeled atlases. Moreover, several novel tools for brain abnormality measurement will also be developed for early detection and progression monitoring of brain disorders using both multimodal imaging and non-imaging data. By successful development of these brain measurement tools, the respective software modules will be developed and further incorporated into 3D Slicer via collaboration with GE Research, which is a part of the engineering core of the National Alliance for Medical Image Computing (NA-MIC). Both source code and pre-compiled programs will be made freely available.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
2R01EB006733-04A1
Application #
8390370
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Pai, Vinay Manjunath
Project Start
2006-12-01
Project End
2016-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
4
Fiscal Year
2012
Total Cost
$531,904
Indirect Cost
$164,681
Name
University of North Carolina Chapel Hill
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Zhang, Lichi; Wang, Qian; Gao, Yaozong et al. (2016) Automatic labeling of MR brain images by hierarchical learning of atlas forests. Med Phys 43:1175-86
Rekik, Islem; Li, Gang; Lin, Weili et al. (2016) Multidirectional and Topography-based Dynamic-scale Varifold Representations with Application to Matching Developing Cortical Surfaces. Neuroimage 135:152-62
Huang, Lei; Jin, Yan; Gao, Yaozong et al. (2016) Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest. Neurobiol Aging 46:180-91
Chen, Geng; Zhang, Pei; Wu, Yafeng et al. (2016) Denoising Magnetic Resonance Images Using Collaborative Non-Local Means. Neurocomputing 177:215-227
Chen, Geng; Zhang, Pei; Li, Ke et al. (2016) Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing. Sci Rep 6:37847
Wu, Guorong; Peng, Xuewei; Ying, Shihui et al. (2016) eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration. PLoS One 11:e0146870
Jiang, Weixiong; Li, Gang; Liu, Huasheng et al. (2016) Reduced cortical thickness and increased surface area in antisocial personality disorder. Neuroscience 337:143-152
Bahrami, Khosro; Shi, Feng; Zong, Xiaopeng et al. (2016) Reconstruction of 7T-Like Images From 3T MRI. IEEE Trans Med Imaging 35:2085-97
Huynh, Tri; Gao, Yaozong; Kang, Jiayin et al. (2016) Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model. IEEE Trans Med Imaging 35:174-83
Meng, Yu; Li, Gang; Gao, Yaozong et al. (2016) Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies. Hum Brain Mapp 37:4129-4147

Showing the most recent 10 out of 232 publications