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dc.contributor.authorPiskořová, Z.
dc.date.accessioned2015-08-25T08:42:52Z
dc.date.available2015-08-25T08:42:52Z
dc.date.issued2015cs
dc.identifier.citationProceedings of the 21st Conference STUDENT EEICT 2015. s. 137-139. ISBN 978-80-214-5148-3cs
dc.identifier.isbn978-80-214-5148-3
dc.identifier.urihttp://hdl.handle.net/11012/42955
dc.description.abstractThe aim of this work is to design tracking algorithm which will be able to track brain axonal bundles in diffusion weighted MRI data. Estimation of anisotropic diffusion profile inside voxels was performed by diffusion tensor imaging model (DTI). Tracing is based on the 4th order Runge-Kutta method. Algorithm is implemented in the MATLAB computing environment and is tested on real data biological phantom.en
dc.formattextcs
dc.format.extent137-139cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 21st Conference STUDENT EEICT 2015en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectDiffusion MRIen
dc.subjecttractographyen
dc.subjectdiffusion tensor imagingen
dc.subjectDTIen
dc.subjectRunge-Kutta methoden
dc.subjectdeterministic tracking algorithmen
dc.titleTracking of Axonal Bundles in Diffusion MRI Brain Imagesen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date23.04.2015cs
but.event.titleStudent EEICT 2015cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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