Continued Development of 4-dimensional Image Warping and Registration Software Abstract: This project aims to continue the methodological development, testing and evaluation of a 4-dimensional (4D) image warping and registration algorithm, with emphasis on measurement of brain structure and its evolution over time. The explosive growth of medical imaging has created the need for the development of quantitative, highly automated, accurate, and robust software tools for analysis of medical imagery. Image registration has attracted particular scientific interest, since it is necessary for integration and comparison of data from individuals or groups, as well as for the development of statistical atlases that reflect anatomical and functional variability within a group of individuals. Moreover, it can be used to measure the temporal evolution of structure and function by co-registering serial scans from the same individual, and ultimately using them to construct 4D statistical atlases. Although a plethora of 3D image registration methods has been developed and used extensively, there is currently a scarcity of methods for robustly measuring subtle longitudinal change, i.e., change of structure and function with time. Currently, longitudinal change is typically measured by first applying a standard 3D registration method to individual scans in a series and then applying some sort of regression to them. However, the estimated longitudinal change is known both theoretically and experimentally to be significantly different from the actual one, due to the limited accuracy of the 3D registration methods, and more importantly to all sorts of noises that are amplified when evaluating change as a difference between two independent 3D measurements. We have developed a deformable registration algorithm, called HAMMER, and was among the first groups to pioneer its extension to a fully 4D deformable registration method (4D-HAMMER) for co- registering serial scans to an atlas simultaneously, and to demonstrate the significant improvement compared to analogous 3D registration in measuring subtle patterns of morphological change in serial scans. This approach analyzes all serial scans of an individual at the same time, thus it can assess longitudinal change and register serial scans to the atlas simultaneously. Effectively, this process reduces the detrimental effects of noise, and therefore is more accurate in measuring longitudinal changes. Clearly, designing an easy-to-use, robust software package for 4D-HAMMER and incorporating it into the ITK will benefit a large community of end-users that need access to this advanced image analysis method for longitudinal study. To increase the robustness of the 4D-HAMMER to the variable contrast and quality of longitudinal data, as well as to significantly improve its speed, further algorithm development is necessary. To increase ease of use by non-experts in computer analysis methods and to integrate this software into the ITK, significant software engineering efforts are also planned. Therefore, in this project, we will a) carry out novel methodological developments to integrate and optimize the 4D tissue segmentation and registration within a single framework for rendering the 4D-HAMMER more accurate and robust, and to significantly speed up the 4D-HAMMER by reducing the computational time from over 20 hours to several hours (Aim 1);b) perform a series of validation experiments and comparative studies, to optimize parameters in the new 4D-HAMMER and demonstrate its performance by comparing it with previous one, as well as with the two widely used alternative methods, e.g., SPM and B-spline based warping algorithms (Aim 2);c) develop and disseminate user-friendly and well-documented software, and also incorporate it into ITK (Aims 3-4).
This project aims to continue the methodological development, testing and evaluation of a 4-dimensional (4D) image warping and registration algorithm, with emphasis on measurement of brain structure and its evolution over time. To increase the robustness of the 4D image warping algorithm to the variable contrast and quality of longitudinal data, and to significantly improve its speed, further algorithm development is necessary. Significant software engineering efforts are also planned to increase ease of use by non-experts in computer analysis methods, and to integrate this software into the ITK to benefit a large community of end-users that need access to this advanced image analysis method for longitudinal study. With proposed methodological and software developments, a final user-friendly and well- documented software package, with incorporation into the ITK, will be available to the public.
|Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei et al. (2017) Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution. Pattern Recognit 63:531-541|
|Bahrami, Khosro; Shi, Feng; Rekik, Islem et al. (2017) 7T-guided super-resolution of 3T MRI. Med Phys 44:1661-1677|
|Jie, Biao; Liu, Mingxia; Liu, Jun et al. (2017) Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease. IEEE Trans Biomed Eng 64:238-249|
|Yu, Renping; Zhang, Han; An, Le et al. (2017) Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification. Hum Brain Mapp 38:2370-2383|
|Zhang, Lichi; Wang, Qian; Gao, Yaozong et al. (2017) Concatenated Spatially-localized Random Forests for Hippocampus Labeling in Adult and Infant MR Brain Images. Neurocomputing 229:3-12|
|Yang, Zhanlong; Shen, Dinggang; Yap, Pew-Thian (2017) Image mosaicking using SURF features of line segments. PLoS One 12:e0173627|
|Fang, Longwei; Zhang, Lichi; Nie, Dong et al. (2017) Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks. Patch Based Tech Med Imaging (2017) 10530:12-19|
|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|
|Liu, Mingxia; Gao, Yue; Yap, Pew-Thian et al. (2017) Multi-Hypergraph Learning for Incomplete Multi-Modality Data. IEEE J Biomed Health Inform :|
|Zhao, Feng; Qiao, Lishan; Shi, Feng et al. (2017) Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder. Brain Imaging Behav 11:1050-1060|
Showing the most recent 10 out of 303 publications