DocumentCode :
1518993
Title :
Estimation of Diffusion Properties in Crossing Fiber Bundles
Author :
Caan, Matthan W A ; Khedoe, H. Ganesh ; Poot, Dirk H J ; Dekker, Arjan J Den ; Olabarriaga, Silvia D. ; Grimbergen, Kees A. ; Van Vliet, Lucas J. ; Vos, Frans M.
Author_Institution :
Imaging Sci. & Technol., Delft Univ. of Technol., Delft, Netherlands
Volume :
29
Issue :
8
fYear :
2010
Firstpage :
1504
Lastpage :
1515
Abstract :
There is an ongoing debate on how to model diffusivity in fiber crossings. We propose an optimization framework for the selection of a dual tensor model and the set of diffusion weighting parameters b, such that both the diffusion shape and orientation parameters can be precisely as well as accurately estimated. For that, we have adopted the Cramér-Rao lower bound (CRLB) on the variance of the model parameters, and performed Monte Carlo simulations. We have found that the axial diffusion λ|| needs to be constrained, while an isotropic fraction can be modeled by a single parameter fiso. Under these circumstances, the Fractional Anisotropy (FA) of both tensors can theoretically be independently estimated with a precision of 9% (at SNR=25 ). Levenberg-Marquardt optimization of the Maximum Likelihood function with a Rician noise model approached this precision while the bias was insignificant. A two-element b-vector b = [ 1.0 amp; 3.5 ] · 103 mm-2 s was found to be sufficient for estimating parameters of heterogeneous tissue with low error. This has allowed us to estimate consistent FA-profiles along crossing tracts. This work defines fundamental limits for comparative studies to correctly analyze crossing white matter structures.
Keywords :
Monte Carlo methods; biodiffusion; biological tissues; biomedical MRI; brain; maximum likelihood estimation; neurophysiology; optimisation; Cramer-Rao lower bound; DW-MRI; Levenberg-Marquardt optimization; Monte Carlo simulations; Rician noise model; axial diffusion; crossing fiber bundles; crossing white matter structures; diffusion orientation; diffusion properties; diffusion shape; diffusion weighted magnetic resonance imaging; diffusion weighting parameters; dual tensor model; fractional anisotropy; heterogeneous tissue; isotropic fraction; maximum likelihood function; optimization; Anisotropic magnetoresistance; Biomedical imaging; Estimation theory; Magnetic analysis; Magnetic resonance imaging; Monte Carlo methods; Parameter estimation; Radiology; Shape; Tensile stress; Cramér-Rao analysis; Monte Carlo simulations; diffusion properties; diffusion weighted magnetic resonance imaging; dual tensor model; Brain; Computer Simulation; Diffusion Tensor Imaging; Humans; Models, Neurological; Models, Statistical; Monte Carlo Method; Nerve Fibers; Normal Distribution; Reproducibility of Results; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2010.2049577
Filename :
5487395
Link To Document :
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