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).

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB008374-01A2
Application #
7691179
Study Section
Special Emphasis Panel (ZRG1-BST-Q (01))
Program Officer
Cohen, Zohara
Project Start
2009-09-15
Project End
2013-08-31
Budget Start
2009-09-15
Budget End
2010-08-31
Support Year
1
Fiscal Year
2009
Total Cost
$326,520
Indirect Cost
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
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
Jie, Biao; Liu, Mingxia; Shen, Dinggang (2018) Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease. Med Image Anal 47:81-94
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
Tang, Zhenyu; Ahmad, Sahar; Yap, Pew-Thian et al. (2018) Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery. IEEE Trans Med Imaging 37:2224-2235
Zhang, Yongqin; Shi, Feng; Cheng, Jian et al. (2018) Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images. IEEE Trans Cybern :
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
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

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