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Basis for Fibertracking

Fibertracking is based on diffusion tensor imaging (DTI) which is the measurement of diffusion anisotropy in the brain using diffusion weighted images that were scanned with magnetic field gradients applied in several directions. The Fibertracking software uses the scans to calculate the diffusion direction of the water molecules along potential white matter fibers for the entire data volume.

The algorithm tries to identify the white matter tracts by following the major diffusion direction from one step to the next as long as the FA value that reflects the amount of anisotropy is over a certain threshold.

Fibertracking allows you to track fiber structures in a defined region of interest, based on diffusion-weighted MR images. Tracking the direction of the local diffusion enables the reconstruction of a fiber as a line by connecting a number of points. Within this region, the algorithm tracks all fibers passing through that region of interest that fulfill the selected tracking parameters.

A set of default and customized tracking templates are provided and fibers can also be tracked interactively within defined parameters.

Getting Started

To perform Fibertracking, special MR data is needed. This data consists of diffusion weighted images, that are acquired with quick EPI sequences. At a minimum, there must be 7 scans that include:

  • One baseline scan without diffusion weighting (B0) or with homogeneous magnetic field.
  • At least six scans with magnetic field gradients (diffusion weighted) applied from different directions.

With these scans, a diffusion tensor can be calculated that provides information about the local diffusion in each image voxel. This is referred to as DTI data.

Load a DTI study to the current treatment plan to begin using Fibertracking

DTI Data

Fibertracking uses a service running in the background to automatically detect and preprocess valid DTI data. The software converts the data into a DTI study containing both:

  • The preregistered image sets: B0, ADC and FA maps.
  • The diffusion tensor that is required for Fibertracking.

The DTI study can be selected from Patient Selection (refer to the Content Manager or Patient Selection Software User Guide).

DTI Color Coding

Fibertracking is based on the measurement of diffusion anisotropy in the brain by using diffusion-weighted images acquired in several directions. DTI data provides the direction of local diffusion that can be visualized in 3D color-coded maps. These color maps provide information about the direction of water diffusion along potential fibers within the slices.

Multicolor fibers are colorized according to the following neurological convention:

Fiber Color

Diffusion Direction







DTI Preprocessing

The calculation of a diffusion tensor field from the DTI images and the calculation of the ADC and FA maps are based on methods and algorithms that are already peer reviewed and published:

  1. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001;13(4):534-546.

  2. Masutani Y, Aoki S, Abe O, Hayashi N, Otomo K. MR diffusion tensor imaging: recent advance and new techniques for diffusion tensor visualization. Eur J Radiol 2003;46(1):53-66.

  3. Peled S, Friman O, Jolesz F, Westin C. Geometrically constrained two-tensor model for crossing tracts in DWI: J Magn Reson Imaging 2006;24(9):1263-1270.

Fibertracking Algorithm

Fibertracking is based on the FACT (fiber assignment by continuous tracking) algorithm, which was first published by Mori, et al. in 2002 (Mori S, van Zijl PC. Fiber tracking: principles and strategies – A technical review. NMR Biomed 2002;15(7-8):468-480).

To achieve smooth results despite the low resolution of coventional DTI scans, tensors are interpolated from the surrounding voxels considering the incoming direction from the previous step. This attempt to pass areas with directional ambiguity was first described by Weinstein et all in 1999 and is called TEND (tensor deflection). Weinstein D, Kindlmann G, Lundberg E.: Tensorlines. Advection-diffusion based propagation through diffusion tensor fields. Center for Scientific Computing and Imaging, Department of Computer Science, University of Utah. Proceedings of the conference on visualization ’99 .

Art-No. 60919-74EN

Utstedelsesdato: 2020-02-26