Thalamus segmentation based on the local diffusion direction: a group study.

Fast and accurate segmentation of deep gray matter regions in the brain is important for clinical applications such as surgical planning for the placement of deep brain stimulation implants. Mapping anatomy from stereotactic atlases to patient data is problematic because of individual differences in subject anatomy that are not accounted for by commonly used atlases. We present a segmentation method for individual subject diffusion tensor MR data that is based on local diffusion information to identify subregions of the thalamus.

Diffusion simulation-based fiber tracking using time-of-arrival maps: a comparison with standard methods.

Object: We propose a new tracking method based on time-of-arrival (TOA) maps derived from simulated diffusion processes.

Materials And Methods: The proposed diffusion simulation-based tracking consists of three steps that are successively evaluated on small overlapping sub-regions in a diffusion tensor field. First, the diffusion process is simulated for several time steps.

In vivo generalized diffusion tensor imaging (GDTI) using higher-order tensors (HOT).

Generalized diffusion tensor imaging (GDTI) using higher-order tensor (HOT) statistics generalizes the technique of diffusion tensor imaging by including the effect of nongaussian diffusion on the signal of MRI. In GDTI-HOT, the effect of nongaussian diffusion is characterized by higher-order tensor statistics (i.e.

Comparison of gradient encoding directions for higher order tensor diffusion data.

Recently, higher order tensors were proposed for a more advanced representation of non-Gaussian diffusion. These advanced diffusion models have new requirements for the gradient encoding schemes used in the diffusion weighted image acquisition. The influence of the gradient encoding schemes on the estimated standard second order diffusion tensor was previously investigated.