Multi-Fiber Reconstruction Using Probabilistic Mixture Models for Diffusion MRI Examinations of the Brain

摘要: In the field of MRI brain image analysis, Diffusion tensor imaging ( DTI) provides a description diffusion water through tissue and makes it possible to trace fiber connectivity in brain, yielding map how is wired. DTI employs second order model based on assumption Gaussian diffusion. The assumption, however, limits use solving intra-voxel heterogeneity as can be non-Gaussian several biological tissues including human brain. Several approaches modeling reconstruction have been proposed last decades. Among such are multi-compartmental probabilistic mixture models. These models include discrete or continuous mixtures probability distributions Gaussian, Wishart von Mises-Fisher distributions. Given weighted data, problem resolving multiple fibers within single voxel boils down estimating parameters