3D Bayesian Regularization of Diffusion Tensor MRI using Multivariate Gaussian Markov Random Fields


M. Martin-Fernandez, C.-F. Westin, C. Alberola-Lopez
Seventh International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'04)
Pages 351-359
September, 2004

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Abstract

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.

(a) Tractography of the original DT-MRI data. (b) Tractography of the regularized data.



Reference

Martin-Fernandez M, Westin CF, Alberola-Lopez C. 3D bayesian regularization of diffusion tensor MRI using multivariate gaussian markov random fields. In Seventh International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'04), Lecture Notes in Computer Science. Rennes - Saint Malo, France, 2004;351-359.

Bibtex entry

@InProceedings{martin-fernandezMICCAI04,
  author         = {M. Martin-Fernandez and C.-F. Westin and C.                
                   Alberola-Lopez},                                            
  title          = {{3D} Bayesian Regularization of Diffusion Tensor {MRI}     
                   using Multivariate Gaussian Markov Random Fields},          
  booktitle      = {Seventh International Conference on Medical Image Computing
                   and  Computer-Assisted Intervention (MICCAI'04)},           
  series         = {Lecture Notes in Computer Science},                        
  pages          = {351--359},                                                 
  year           = {2004},                                                     
  address        = {Rennes - Saint Malo, France},                              
  month          = {September}
}                                                

Grants

NIH P41-RR13218 (NAC), CIMIT, TIC2001-3808-C02, Fulbright FU2003-0968

Research area

DTMRI

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