DocumentCode
2464006
Title
Exploiting peak anisotropy for tracking through complex structures
Author
Seunarine, Kiran K. ; Cook, P.A. ; Hall, M.G. ; Embleton, K.V. ; Parker, G.J.M. ; Alexander, Daniel C.
Author_Institution
Univ. Coll. London, London
fYear
2007
fDate
14-21 Oct. 2007
Firstpage
1
Lastpage
8
Abstract
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 in fibre-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.
Keywords
biomedical MRI; image reconstruction; medical image processing; probability; Bingham parameters; MRI; bending structures; bootstrap experiment; complex structure tracking; discrete fibre orientations; multifibre reconstruction; orientation distribution function; peak anisotropy; persistent angular structure; probabilistic index of connectivity; tractography algorithms; Anisotropic magnetoresistance; Biomedical imaging; Calibration; Diffusion tensor imaging; Image reconstruction; Magnetic resonance imaging; Reconstruction algorithms; Shape; Streaming media; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
Conference_Location
Rio de Janeiro
ISSN
1550-5499
Print_ISBN
978-1-4244-1630-1
Electronic_ISBN
1550-5499
Type
conf
DOI
10.1109/ICCV.2007.4409168
Filename
4409168
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