The ability to provide images with exquisite anatomical details has made magnetic resonance imaging (MRI) one of the most suitable imaging methods for the study of brain structure and brain development in pediatric subjects. However, MR is highly sensitive to motion artifacts. Without sedation, it will be a daunting task to obtain high quality brain images from the pediatric population. While sedation is commonly used for clinical imaging in pediatric patients, it is clearly not a choice for imaging children in research studies. Therefore, investigations of brain structure in normal children and in children with, or at high risk for neurodevelopmental disorders will require developing new MR methodologies. Recent advances on parallel imaging offer an ideal solution for imaging very young children without sedation by taking the unique features associated with the pediatric brains into consideration for the designs of imaging methods and coils. For example, it is conceivable to use a much smaller surface coil as that typically employed for adult imaging to maximize signal-to-noise (SNR) gains without worrying about the reduced coil sensitivity for deep brain structures and in conjunction with the parallel imaging to reduce data acquisition time. In addition, new image analysis tools specifically designed for the pediatric brains can further augment our ability to quantitatively measure normal brain developments. Therefore, the ultimate goal of this proposal is to develop dedicated imaging hardware and software for imaging very young children so as to allowing detailed characterizations of normal brain development. Specifically, this application consists of two major components: 1) technical developments for the required hardware (dedicated multi-channel phase array coils) and software (imaging sequences, reconstruction methods, and image analysis tools) and 2) the application of this novel methodology to longitudinally study normal brain development in an age range that is highly critical for functional and cognitive development, yet poorly understood currently (2wk - 2 years old). The proposed studies bring investigators with well-versed, yet complementary expertise, including investigators from Massachusetts General Hospitals who have extensive technical expertise for hardware developments of the dedicated rf coils and imaging expertise or parallel imaging methods and motion correction schemes as well as investigators at UNC-Chapel Hill who have extensive experience on imaging pediatric subjects, developing imaging methods, and developing novel image analysis tools. Together, it is highly likely that we will be able to recruit a large cohort of pediatric subjects for conducting a longitudinal imaging study, to reduce data acquisition time within 10 min, to obtain high quality MR images for quantitative characterizing normal brain developments.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS055754-03
Application #
7615584
Study Section
Special Emphasis Panel (ZRG1-BDCN-K (50))
Program Officer
Chen, Daofen
Project Start
2007-05-01
Project End
2012-04-30
Budget Start
2009-05-01
Budget End
2010-04-30
Support Year
3
Fiscal Year
2009
Total Cost
$503,685
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
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
Hu, Shunbo; Wei, Lifang; Gao, Yaozong et al. (2017) Learning-based deformable image registration for infant MR images in the first year of life. Med Phys 44:158-170
Wang, Yan; Ma, Guangkai; An, Le et al. (2017) Semisupervised Tripled Dictionary Learning for Standard-Dose PET Image Prediction Using Low-Dose PET and Multimodal MRI. IEEE Trans Biomed Eng 64:569-579
Wang, Yan; Zhang, Pei; An, Le et al. (2016) Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation. Phys Med Biol 61:791-812
Rekik, Islem; Li, Gang; Lin, Weili et al. (2016) Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. Med Image Anal 28:1-12
Li, Gang; Wang, Li; Shi, Feng et al. (2016) Cortical thickness and surface area in neonates at high risk for schizophrenia. Brain Struct Funct 221:447-61
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
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
Eldeniz, Cihat; Finsterbusch, Jürgen; Lin, Weili et al. (2016) TOWERS: T-One with Enhanced Robustness and Speed. Magn Reson Med 76:118-26
Kang, Jiayin; Gao, Yaozong; Shi, Feng et al. (2015) Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images. Med Phys 42:5301-9

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