Development and Dissemination of Robust Brain MRI Measurement Tools Abstract: This application responds to RFA: PAR-07-249, """"""""Collaborations with National Centers for Biomedical Computing"""""""". The goal of this project is to develop and widely distribute a software package for robust measurement of brain structure in MR images by using computational neuroanatomy methods. The project will collaborate with the National Alliance for Medical Image Computing (NA-MIC) to develop the software using the NA-MIC Software Engineering Process, leverage the NA-MIC engineering infrastructure, and integrate this software into the 3D Slicer, a well-architected application environment being developed in NA-MIC. The particular software package will include both a brain image registration and warping algorithm, called HAMMER, and an algorithm for the segmentation of white matter lesions (WMLs), which can arise from a variety of pathologies including vascular pathology and multiple sclerosis. HAMMER received 2006 Best Paper Award from IEEE Signal Processing Society. HAMMER has been successfully applied to many large clinical research studies and clinical trials involving over 5,000 MR brain images and has been downloaded by 318 users from 102 institutions in over 20 countries. The WML segmentation algorithm has been successfully applied to """"""""Action to Control Cardiovascular Risk in Diabetes-Memory in Diabetes"""""""" (ACCORD-MIND) sub- study, with data acquired from 4 centers on 650 patients over a period of 8 years. Designing an easy-to-use, robust software package for these two algorithms and incorporating it into the 3D Slicer will benefit a large community of end-users that need access to advanced image analysis methods in various neuroimaging studies. To increase the robustness of the algorithms to the highly variable quality and characteristics of clinical image data, further algorithm development is necessary. To increase ease of use by non-experts in computer analysis methods and integrate this software into the Slicer platform, significant software engineering efforts are planned.
Three aims will be investigated.
The first aim i s to further develop and extend novel image analysis methods aiming at improving the robustness and performance of HAMMER registration and WML segmentation algorithms, so that they can be easily applied to various clinical research studies. The second and third aims are to design separate software modules for these two algorithms, and to incorporate them into the 3D Slicer. These two modules will be designed (1) with consistent cross-platform interactive and scripted interfaces, (2) allowing end-users to interactively explore the suitable parameters for their data, (3) enabling developers to add new functions. The robustness of these two modules will be extensively tested and improved by both software engineering tools and various clinical research data (acquired from different centers). The final software will be freely available in both source code and pre-compiled programs. PUBLIC HEALTH REVELANCE: The goal of this project is to develop and widely distribute a software package for robust measurement of brain structure in MR images by using computational neuroanatomy methods. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB006733-01A2
Application #
7556497
Study Section
Special Emphasis Panel (ZRG1-BST-E (50))
Program Officer
Cohen, Zohara
Project Start
2008-09-17
Project End
2011-08-31
Budget Start
2008-09-17
Budget End
2009-08-31
Support Year
1
Fiscal Year
2008
Total Cost
$402,999
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
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
Wu, Zhengwang; Gao, Yaozong; Shi, Feng et al. (2018) Segmenting hippocampal subfields from 3T MRI with multi-modality images. Med Image Anal 43:10-22
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 :

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