DT-MRI Connectivity and/or Tractography?: Two New Algorithms

Abstract Diffusion Tensor MRI (DTI) is a special MR imaging technique where the second order symmetric diffusion tensors that are correlated with the underlying fi-brous structure (eg. the nerves in brain), are computed based on DiffusionWeighted MR Images (DWI). DTI is the only in vivo imaging technique that provides information about the network of nerves in brain. The computed tensors describe the local diffusion pattern of water molecules via a 3D Gaussian distribution in space. The most common analysis and visualization technique is tractography, which is a numerical integration of the principal diffusion direction (PDD) that attempts to reconstruct fibers as streamlines. Despite its simplicity and ease of interpretation, tractography algorithms suffer from several drawbacks mainly due to ignoring the information in the underlying spatial distribution but using the PDD only. An alternative to tractography is connectivity which aims at computing probabilistic connectivity maps based on the above mentioned 3D Gaussian distribution as described by the DTI data. However, the computational cost is high and the resulting maps are usually hard to visualize and interpret. This chapter discusses these two approaches and introduces two new tractography techniques, namely

the Lattice-of-Springs (LoS)

method that exploits the connectivity approach and the

Split & Merge Tractography (SMT)

that attempts to combine the advantages of tractography and connectivity.