3D Bayesian regularization applied to diffusion tensor MRI is presented here. The approach uses Markov Random Field ideas and is based upon the definition of a 3D neighborhood system in which the spatial interactions of the tensors are modeled. As for the prior, we model the behavior of the tensor fields by means of a 6D multivariate Gaussian local characteristic. As for the likelihood, we model the noise process by means of conditionally independent 6D multivariate Gaussian variables. Those models include inter-tensor correlations, intra-tensor correlations and colored noise. The solution tensor field is obtained by using the simulated annealing algorithm to achieve the maximum a posteriori estimation. Several experiments both on synthetic and real data are presented, and performance is assessed with mean square error measure. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Martín-Fernández, M., Westin, C. F., & Alberola-López, C. (2004). 3D bayesian regularization of diffusion tensor MRI using multivariate gaussian markov random fields. In Lecture Notes in Computer Science (Vol. 3216, pp. 351–359). Springer Verlag. https://doi.org/10.1007/978-3-540-30135-6_43
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