DICOM PS3.17 2018d - Explanatory Information

WWW Tractography Results (Informative) (Current)
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# WWW Tractography Results (Informative)

## WWW.1 Introduction

MRI diffusion imaging is able to quantify diffusion of water along certain directions. The diffusion tensor model is a simple model that is able to describe the statistical diffusion process accurately at most white matter positions. To calculate diffusion tensors, a base-line MRI without diffusion-weighting and at least six differently weighted diffusion MRIs have to be acquired. After some preprocessing of the data, at each grid point, a diffusion tensor can be calculated. This gives rise to a tensor volume that is the basis for tracking. Refinements to the diffusion model and acquisition method such as HARDI, Q-Ball, diffusion spectrum imaging (DSI) and diffusion kurtosis imaging (DKI) are expanding the directionality information available beyond the simple tensor model, enhancing tracking through crossings, adjacent fibers, sharp turns, and other difficult scenarios.

A tracking algorithm produces tracks (i.e., fibers), which are collected into track sets. A track contains the set of x, y and z coordinates of each point making up the track. Depending upon the algorithm and software used, additional quantities such as Fractional Anisotropy (FA) values or color etc. may be associated with the data, by track set, track or point, either to facilitate further filtering or for clinical use. Descriptive statistics of quantities such as FA may be associated with the data by track set or track.

Examples of tractography applications include:

- Visualization of white matter tracks to aid in resection planning or to support image guided (neuro) surgery;

- Determination of proximity and/or displacement versus infiltration of white matter by tumor processes;

- Assessment of white matter health in neurodegenerative disorders, both axonal and myelin integrity, through sampling of derived diffusion parameters along the white matter tracks.

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VVV Segmentation of Images of Groups of Animals (Informative) | Home | WWW.2 Encoding Example

DICOM PS3.17 2018d - Explanatory Information