Generalizing Diffusion Tensor Model using Probabilistic Inference in Markov Random Fields

Cagatay Demiralp¹, David H. Laidlaw

We provide a proof of concept for modeling configuration distributions in DTI and their practical estimations. The power of the MAP-MRF framework comes from its mathematical convenience in modeling prior distributions and the fact that it results in a global optimization driven by local patches (context).