NDLI: Simultaneous smoothing and estimation of the tensor field from diffusion tensor MRI

Content Provider	IEEE Xplore Digital Library
Author	Wang, Z. Vemuri, B.C. Chen, Y. Mareci, T.
Copyright Year	2003
Description	Author affiliation: Dept. of CISE, Univ. of Florida, Gainesville, FL, USA (Wang, Z.; Vemuri, B.C.)
Abstract	Diffusion tensor magnetic resonance imaging (DT-MRI) is a relatively new imaging modality in the field of medical imaging. This modality of imaging allows one to capture the structural connectivity if any between functionally meaningful regions for example, in the brain. The data however can be noisy and requires restoration. In this paper, we present a unified model for simultaneous smoothing and estimation of diffusion tensor field from DT-MRI. The diffusion tensor field is estimated directly from the raw data with L/sup P/ smoothness and positive definiteness constraints. The data term we employ is from the original Stejskal-Tanner equation instead of the linearized version as usually done in literature. In addition, we use Cholesky decomposition to ensure positive definiteness of the diffusion tensor. The unified model is discretized and solved numerically using limited memory quasi-Newton method. Both synthetic and real data experiments are shown to depict the algorithm performance.
Sponsorship	IEEE Comput. Soc Tech. Committee on Pattern Anal. & Machine Intelligence (TCPAMI)
File Size	1648219
File Format	PDF
ISBN	0769519008
ISSN	10636919
DOI	10.1109/CVPR.2003.1211390
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-06-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Smoothing methods Tensile stress Magnetic resonance imaging Diffusion tensor imaging Equations Image restoration Anisotropic magnetoresistance Computed tomography Mathematics Biochemistry
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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