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Proceedings Paper

Assessing fiber tracking accuracy via diffusion tensor software models
Author(s): Sebastiano Barbieri; Jan Klein; Christopher Nimsky; Horst K. Hahn
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Paper Abstract

In the last few years, clinicians have started using fiber tracking algorithms for pre- and intraoperative neurosurgical planning. In the absence of a ground truth, it is often difficult to asses the validity and precision of these algorithms. To this end, we develop a realistic DTI software model in which multiple fiber bundles and their geometrical configuration may be specified, also allowing for scenarios in which fiber bundles cross or kiss and which are common bottlenecks of fiber tracking algorithms. Partial voluming, that is the contributions of multiple tissues to a voxel, is taken into account. The model gives us the possibility to compute the diffusion-weighted signal attenuation given certain tissue and scanner parameters. On the tissue side we can model the diffusion coefficients, the principal diffusion direction and the width of the fiber bundles. On the scanner side, we can model the diffusion time, the strength and direction of the applied diffusion gradient and the width of the diffusion pulse. We also include the possibility to add noise and various artifacts such as aliasing and N/2 ghosting to the model. Having generated the model of a fiber bundle, we determine the distance between the tracked fibers and the original model, thus being able to make assertions on the accuracy of the employed fiber tracking algorithm. Moreover, we can use this information to give an indication about an appropriate width of a safety margin around the tracked fiber bundle.

Paper Details

Date Published: 12 March 2010
PDF: 9 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762326 (12 March 2010); doi: 10.1117/12.844215
Show Author Affiliations
Sebastiano Barbieri, Fraunhofer MEVIS (Germany)
Jan Klein, Fraunhofer MEVIS (Germany)
Christopher Nimsky, Univ. of Marburg (Germany)
Horst K. Hahn, Fraunhofer MEVIS (Germany)

Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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