Beyond the Gaussian Model in Diffusion-Weighted Imaging: The Package dti
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Abstract
Diffusion weighted imaging (DWI) is a magnetic resonance (MR) based method to investigate water diffusion in tissue like the human brain. Inference focuses on integral properties of the tissue microstructure. The acquired data are usually modeled using the diffusion tensor model, a three-dimensional Gaussian model for the diffusion process. Since the homogeneity assumption behind this model is not valid in large portion of the brain voxel more sophisticated approaches have been developed.
This paper describes the R package dti. The package offers capabilities for the analysis of diffusion weighted MR experiments. Here, we focus on recent extensions of the package, for example models for high angular resolution diffusion weighted imaging (HARDI) data, including Q-ball imaging and tensor mixture models, and fiber tracking. We provide a detailed description of the package structure and functionality. Examples are used to guide the reader through a typical analysis using the package. Data sets and R scripts used are available as electronic supplements.
This paper describes the R package dti. The package offers capabilities for the analysis of diffusion weighted MR experiments. Here, we focus on recent extensions of the package, for example models for high angular resolution diffusion weighted imaging (HARDI) data, including Q-ball imaging and tensor mixture models, and fiber tracking. We provide a detailed description of the package structure and functionality. Examples are used to guide the reader through a typical analysis using the package. Data sets and R scripts used are available as electronic supplements.