Bayesian regularization of diffusion tensor images.

Diffusion tensor imaging (DTI) is a powerful tool in the study of the course of nerve fiber bundles in the human brain. Using DTI, the local fiber orientation in each image voxel can be described by a diffusion tensor which is constructed from local measurements of diffusion coefficients along several directions. The measured diffusion coefficients and thereby the diffusion tensors are subject to noise, leading to possibly flawed representations of the 3-dimensional (3D) fiber bundles. In this paper, we develop a Bayesian procedure for regularizing the diffusion tensor field, fully utilizing the available 3D information of fiber orientation. The use of the procedure is exemplified on synthetic and in vivo data.

[1]  C. Poupon,et al.  Regularization of Diffusion-Based Direction Maps for the Tracking of Brain White Matter Fascicles , 2000, NeuroImage.

[2]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[3]  Peter Green,et al.  Highly Structured Stochastic Systems , 2003 .

[4]  Graham W. Horgan,et al.  Image analysis for the biological sciences , 1997 .

[5]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[6]  D G Gadian,et al.  Limitations and requirements of diffusion tensor fiber tracking: An assessment using simulations , 2002, Magnetic resonance in medicine.

[7]  A. Anderson.  Theoretical analysis of the effects of noise on diffusion tensor imaging , 2001, Magnetic resonance in medicine.

[8]  P. Basser,et al.  Parametric and non-parametric statistical analysis of DT-MRI data. , 2003, Journal of magnetic resonance.

[9]  M. Horsfield,et al.  Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[10]  Pratik Mukherjee,et al.  Diffusion-tensor imaging-guided tracking of fibers of the pyramidal tract combined with intraoperative cortical stimulation mapping in patients with gliomas. , 2004, Journal of neurosurgery.

[11]  J. Besag.  Statistical analysis of dirty pictures , 1993 .

[12]  Dana H. Brooks,et al.  Electrical imaging of the heart , 1997, IEEE Signal Process. Mag..

[13]  Zhizhou Wang,et al.  A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI , 2004, IEEE Transactions on Medical Imaging.

[14]  S. Bramble.  Image analysis for the biological sciences , 1996 .

[15]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[16]  P. Guttorp.  Stochastic modeling of scientific data , 1995 .

[17]  B W Kreher,et al.  Multitensor approach for analysis and tracking of complex fiber configurations , 2005, Magnetic resonance in medicine.

[18]  J. Besag,et al.  Bayesian image restoration, with two applications in spatial statistics , 1991 .

[19]  David J. Spiegelhalter,et al.  Introducing Markov chain Monte Carlo , 1995 .

[20]  Sylvia Richardson,et al.  Markov chain concepts related to sampling algorithms , 1995 .

[21]  L. Frank.  Characterization of anisotropy in high angular resolution diffusion‐weighted MRI , 2002, Magnetic resonance in medicine.

[22]  Antonio Possolo,et al.  Spatial Statistics and Imaging , 1992 .

[23]  B. D. Ripley,et al.  The use of spatial models as image priors , 1991 .

[24]  L. Frank.  Anisotropy in high angular resolution diffusion‐weighted MRI , 2001, Magnetic resonance in medicine.

[25]  N. Swindale,et al.  Diffusion tensor fiber tracking shows distinct corticostriatal circuits in humans , 2004, Annals of neurology.

[26]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[27]  Zhizhou Wang,et al.  A Constrained Variational Principle for Direct Estimation and Smoothing of the Diffusion Tensor Field from DWI , 2003, IPMI.

[28]  S. Arridge,et al.  Detection and modeling of non‐Gaussian apparent diffusion coefficient profiles in human brain data , 2002, Magnetic resonance in medicine.

[29]  J Sijbers,et al.  Estimation of the noise in magnitude MR images. , 1998, Magnetic resonance imaging.

[30]  M. Raichle,et al.  Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Håvard Rue,et al.  Advances in Bayesian Image Analysis , 2003 .

[32]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[33]  若菜 勢津.  Fiber tract-based atlas of human white matter anatomy , 2006 .

[34]  Bin Chen,et al.  Noise removal in magnetic resonance diffusion tensor imaging , 2005, Magnetic resonance in medicine.

[35]  H. Moser,et al.  Diffusion tensor–based imaging reveals occult abnormalities in adrenomyeloneuropathy , 2005, Annals of neurology.

[36]  J. F. C. Kingman,et al.  Information and Exponential Families in Statistical Theory , 1980 .

[37]  Yunmei Chen,et al.  DT-MRI denoising and neuronal fiber tracking , 2004, Medical Image Anal..

[38]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

[39]  S. Wakana,et al.  Fiber tract-based atlas of human white matter anatomy. , 2004, Radiology.