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. We show the correspondence of our segmentation results to anatomy by comparison with stereotactic atlas data. Importantly, we verify the consistency of our segmentation by evaluating the method on 63 healthy volunteers. Our method is fast, reliable, and independent of any segmentation before the classification of regions within the thalamus. It should, therefore, be useful in clinical applications.

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., the cumulant tensors or the moment tensors), such as the covariance matrix (the second-order cumulant tensor), the skewness tensor (the third-order cumulant tensor), and the kurtosis tensor (the fourth-order cumulant tensor). Previously, Monte Carlo simulations have been applied to verify the validity of this technique in reconstructing complicated fiber structures. However, no in vivo implementation of GDTI-HOT has been reported. The primary goal of this study is to establish GDTI-HOT as a feasible in vivo technique for imaging nongaussian diffusion. We show that probability distribution function of the molecular diffusion process can be measured in vivo with GDTI-HOT and be visualized with three-dimensional glyphs. By comparing GDTI-HOT to fiber structures that are revealed by the highest resolution diffusion-weighted imaging (DWI) possible in vivo, we show that the GDTI-HOT can accurately predict multiple fiber orientations within one white matter voxel. Furthermore, through bootstrap analysis we demonstrate that in vivo measurement of HOT elements is reproducible, with a small statistical variation that is similar to that of diffusion tensor imaging.

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. Second, a TOA map is created to store simulation results for the individual time steps that are required for the tract reconstruction. Third, the fiber pathway is reconstructed on the TOA map and concatenated between neighboring sub-regions. This new approach is compared with probabilistic and streamline tracking. All methods are applied to synthetic phantom data for an easier evaluation of their fiber reconstruction quality. RESULTS: The comparison of the tracking results did show severe problems for the streamline approach in the reconstruction of crossing fibers, for example. The probabilistic method was able to resolve the crossing, but could not handle strong curvature. The new diffusion simulation-based tracking could reconstruct all problematic fiber constellations. CONCLUSION: The proposed diffusion simulation-based tracking method used the whole tensor information of a neighborhood of voxels and is, therefore, able to handle problematic tracking situations better than established methods.

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. Here, we focus on the suitability of different encoding scheme types for higher order tensor models. Two quality measures for the gradient encoding schemes, the condition number of the estimation matrix and a new measure that evaluates the signal deviation on simulated data, are used to determine which gradient encoding is suited best for higher order tensor estimations. Six different gradient encoding scheme types were investigated. A certain force-minimizing scheme type gave the best results in the evaluations presented here.