Reducing Model Uncertainty in Crossing Fiber Tractography

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dc.contributor.authorGruen, Johannesen_US
dc.contributor.authorVoort, Gemma van deren_US
dc.contributor.authorSchultz, Thomasen_US
dc.contributor.editorOeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomasen_US
dc.date.accessioned2021-09-21T08:09:31Z
dc.date.available2021-09-21T08:09:31Z
dc.date.issued2021
dc.description.abstractDiffusion MRI (dMRI) tractography permits the non-invasive reconstruction of major white matter tracts, and is therefore widely used in neurosurgical planning and in neuroscience. However, it is affected by various sources of uncertainty. In this work, we consider the model uncertainty that arises in crossing fiber tractography, from having to select between alternative mathematical models for the estimation of multiple fiber orientations in a given voxel. This type of model uncertainty is a source of instability in dMRI tractography that has not received much attention so far. We develop a mathematical framework to quantify it, based on computing posterior probabilities of competing models, given the local dMRI data. Moreover, we explore a novel strategy for crossing fiber tractography, which computes tracking directions from a consensus of multiple mathematical models, each one contributing with a weight that is proportional to its probability. Experiments on different white matter tracts in multiple subjects indicate that reducing model uncertainty in this way increases the accuracy of crossing fiber tractography.en_US
dc.description.sectionheadersLet's look into your brains
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20211345
dc.identifier.isbn978-3-03868-140-3
dc.identifier.issn2070-5786
dc.identifier.pages55-64
dc.identifier.urihttps://doi.org/10.2312/vcbm.20211345
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20211345
dc.publisherThe Eurographics Associationen_US
dc.subjectApplied computing
dc.subjectLife and medical sciences
dc.subjectMathematics of computing
dc.subjectProbabilistic algorithms
dc.subjectHumancentered computing
dc.subjectVisualization techniques
dc.titleReducing Model Uncertainty in Crossing Fiber Tractographyen_US
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VCBM 2021: Eurographics Workshop on Visual Computing for Biology and Medicine