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.
Frandsen
Jesper
J
Department of Neuroradiology, Centre for Functionally Integrative Neuroscience, Arhus University Hospital, Arhus, Denmark.
Hobolth
Asger
A
Ostergaard
Leif
L
Vestergaard-Poulsen
Peter
P
Vedel Jensen
Eva B
EB
D000328:Adult; D001499:Bayes Theorem; D001699:Biometry; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D038524:Diffusion Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008297:Male; D008390:Markov Chains; D008959:Models, Neurological; D009010:Monte Carlo Method; D009412:Nerve Fibers / Q000502:physiology