Exploiting peak anisotropy for tracking through complex structures.
2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6.
(pp. 2649 - 2656).
This work shows that multi-fibre reconstruction techniques, such as Persistent Angular Structure (PAS) MRI or QBall Imaging, provide much more information than just discrete fibre orientations, which is all that previous tractography algorithms exploit from them. We show that the shapes of the peaks of the functions output by multiple-fibre reconstruction algorithms reflect the underlying distribution of fibres. Furthermore, we show how to exploit this extra information to improve Probabilistic Index of Connectivity (PICo) tractography. The method uses the Bingham distribution to model the uncertainty infibre-orientation estimates obtained from peaks in the PAS or QBall Orientation Distribution Function (ODF). The Bingham model captures anisotropy in the uncertainty, allowing the method to track through fanning and bending structures, which previous methods do not recover reliably. We devise a new calibration procedure to construct a mapping from peak shape to Bingham parameters. We test the accuracy of the calibration using a bootstrap experiment. Finally, we show that exploiting the peak shape in this way can provide improved PICo tractography results.
|Title:||Exploiting peak anisotropy for tracking through complex structures|
|Event:||11th IEEE International Conference on Computer Vision|
|Location:||Rio de Janeiro, BRAZIL|
|Dates:||2007-10-14 - 2007-10-21|
|Keywords:||PERSISTENT ANGULAR STRUCTURE, MULTIPLE FIBER ORIENTATIONS, HUMAN BRAIN, DIFFUSION, TRACTOGRAPHY, MRI, RECONSTRUCTION, UNCERTAINTY, PATHWAYS|
|UCL classification:||UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Child Health
UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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