Probabilistic Tractography for Complex Fiber Orientations with Automatic Model Selection: Probabilistic Tractography for Complex Fiber Orientations with Automatic Model Selection

Edwin Versteeg, Frans M. Vos, Gert Kwakkel, Frans C.T. van der Helm, Joor A.M. Arkesteijn, Olena Filatova*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Fiber tractography aims to reconstruct white matter (WM) connections in the brain. Challenges in these reconstructions include estimation of the fiber orientations in regions with multiple fiber populations, and the uncertainty in the fiber orientations as a result of noise. In this work, we use a range of multi-tensor models to cope with crossing fibers. The uncertainty in fiber orientation is captured using the Cramér-Rao lower bound. Furthermore, model selection is performed based on model complexity and goodness of fit. The performance of the framework on the fibercup phantom and human data was compared to the open source diffusion MRI toolkit Camino for a range of SNRs. Performance was quantified by using the Tractometer measures in the fibercup phantom and by comparing streamline counts of lateral projections of the corpus callosum (CC) in the human data. On the phantom data, the comparison showed that our method performs similar to Camino in crossing fiber regions, whilst performing better in a region with kissing fibers (median angular error of 0.73ı vs 2.7ı, valid connections of 57% vs 21% when seed is in the corresponding region of interest). Furthermore, the amount of counts in the lateral projections was found to be higher using our method (19–89% increase depending on a subject). Altogether, our method outperforms the reference method on both phantom and human data allowing for in-vivo probabilistic multi fiber tractography with an objective model selection procedure.

Original languageEnglish
Title of host publicationComputational Diffusion MRI - MICCAI Workshop, 2017
EditorsEnrico Kaden, Francesco Grussu, Lipeng Ning, Chantal M.W. Tax, Jelle Veraart
PublisherSpringer
Pages117-128
Number of pages12
ISBN (Print)9783319738383
DOIs
Publication statusPublished - 1 Jan 2018
EventMICCAI Workshop on Computational Diffusion MRI, CDMRI 2017 - Quebec, Canada
Duration: 10 Sep 201710 Sep 2017

Publication series

NameMathematics and Visualization
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X

Conference

ConferenceMICCAI Workshop on Computational Diffusion MRI, CDMRI 2017
CountryCanada
CityQuebec
Period10/09/201710/09/2017

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