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dc.contributor.authorShakya, Snehlataen_US
dc.contributor.authorGu, Xuanen_US
dc.contributor.authorBatool, Nazreen_US
dc.contributor.authorÖzarslan, Evrenen_US
dc.contributor.authorKnutsson, Hansen_US
dc.contributor.editorStefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Riederen_US
dc.date.accessioned2017-09-06T07:12:41Z
dc.date.available2017-09-06T07:12:41Z
dc.date.issued2017
dc.identifier.isbn978-3-03868-036-9
dc.identifier.issn2070-5786
dc.identifier.urihttp://dx.doi.org/10.2312/vcbm.20171244
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20171244
dc.description.abstractMulti-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model to estimate the orientations of crossing fibers within a voxel based on mixture of non-central Wishart distributions. We present a thorough comparison of the results from other fiber reconstruction methods with this model. The comparative study includes experiments on a range of separation angles between crossing fibers, with different noise levels, and on real human brain diffusion MRI data. Furthermore, we present multi-fiber visualization results using tractography. Results on synthetic and real data as well as tractography visualization highlight the superior performance of the model specifically for small and middle ranges of separation angles among crossing fibers.en_US
dc.publisherThe Eurographics Associationen_US
dc.titleMulti-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributionsen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.description.sectionheadersShort Papers
dc.identifier.doi10.2312/vcbm.20171244
dc.identifier.pages119-123


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