To understand evolving pathology in the central nervous system (CNS) and develop effective treatments, ways are needed to correlate the nerve fiber connectivity with the visualization of function. Such structure-function information is fundamental in CNS processes since anatomical connections determine where information is passed and processed. Recent methods of magnetic resonance diffusion tensor imaging (DTI) can provide the fundamental information required for viewing structural connectivity and can visualize fiber bundles in the brain in vivo. However, robust and accurate acquisition and processing algorithms are needed to accurately map the nerve connectivity. Automatic fiber tract mapping in the central nervous system (CNS) is a challenging problem for image processing since the data is noisy, making reliable estimation of the fiber tracts difficult. DTI data sets are large and present a formidable challenge in the design of efficient algorithms. In this proposal, we will develop novel, statistically robust and efficient algorithms for automatic fiber tract mapping in the CNS. The automatic fiber tract mapping problem will be solved in two phases, namely a data smoothing phase and a fiber tract mapping phase. In the former, smoothing will be achieved via a new nonlinear anisotropic diffusion algorithm which smooths the data while striving to retain all relevant detail. In the latter, a smooth 3D vector field indicating the dominant anisotropic direction at each spatial location is computed from the smoothed data. Fiber tracts will then be determined as the regularized integral curves of this vector field using efficient numerical methods. To validate the automatically estimated fiber tracts, we will establish the correlation between fiber tracts in fluorescence microscopy images of stained and excised rat spinal cord/brain and the estimated fiber tracts from the DTI data obtained in vivo. The utility of the method for pathology will then be tested on injured spinal cords and on previously acquired data sets of whole mouse, rat brains and isolated hearts.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS042075-04
Application #
6879127
Study Section
Diagnostic Imaging Study Section (DMG)
Program Officer
Chen, Daofen
Project Start
2002-04-01
Project End
2007-03-31
Budget Start
2005-04-01
Budget End
2007-03-31
Support Year
4
Fiscal Year
2005
Total Cost
$344,977
Indirect Cost
Name
University of Florida
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
969663814
City
Gainesville
State
FL
Country
United States
Zip Code
32611
Kim, Hyunwoo J; Xu, Jia; Vemuri, Baba C et al. (2015) Manifold-valued Dirichlet Processes. Proc Int Conf Mach Learn 2015:1199-1208
Kim, Hyunwoo J; Adluru, Nagesh; Banerjee, Monami et al. (2015) Interpolation on the manifold of K component GMMs. Proc IEEE Int Conf Comput Vis 2015:2884-2892
Liu, Meizhu; Vemuri, Baba C; Amari, Shun-Ichi et al. (2010) Total Bregman Divergence and its Applications to Shape Retrieval. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit :3463-3468
Wang, Fei; Vemuri, Baba C; Rangarajan, Anand et al. (2008) Simultaneous nonrigid registration of multiple point sets and atlas construction. IEEE Trans Pattern Anal Mach Intell 30:2011-22
Ramirez-Manzanares, Alonso; Rivera, Mariano; Vemuri, Baba C et al. (2007) Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry. IEEE Trans Med Imaging 26:1091-102
Perrin, George Q; Li, Hua; Fishbein, Lauren et al. (2007) An orthotopic xenograft model of intraneural NF1 MPNST suggests a potential association between steroid hormones and tumor cell proliferation. Lab Invest 87:1092-102
Jian, Bing; Vemuri, Baba C (2007) Multi-fiber reconstruction from diffusion MRI using mixture of Wisharts and sparse deconvolution. Inf Process Med Imaging 20:384-95
Barmpoutis, Angelos; Vemuri, Baba C; Forder, John R (2007) Registration of high angular resolution diffusion MRI images using 4th order tensors. Med Image Comput Comput Assist Interv 10:908-15
Jian, Bing; Vemuri, Baba C; Ozarslan, Evren et al. (2007) A CONTINUOUS MIXTURE OF TENSORS MODEL FOR DIFFUSION-WEIGHTED MR SIGNAL RECONSTRUCTION. Proc IEEE Int Symp Biomed Imaging 4:772-775
Barmpoutis, Angelos; Vemuri, Baba C; Shepherd, Timothy M et al. (2007) Tensor splines for interpolation and approximation of DT-MRI with applications to segmentation of isolated rat hippocampi. IEEE Trans Med Imaging 26:1537-46

Showing the most recent 10 out of 30 publications