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dc.contributor.authorBergmann, Ørjaneng
dc.contributor.authorLundervold, Arvideng
dc.contributor.authorSteihaug, Trondeng
dc.date.accessioned2008-10-16T12:22:21Z
dc.date.available2008-10-16T12:22:21Z
dc.date.issued2005eng
dc.PublishedComputer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on 2005: 277- 281en
dc.identifier.isbn0-7695-2355-2en_US
dc.identifier.issn1063-7125en_US
dc.identifier.urihttps://hdl.handle.net/1956/2799
dc.description.abstractDuring the last years, many techniques for de-noising, segmentation and fiber-tracking have been applied to diffusion tensor MR image data (DTI) from human and animal brains. However, evaluating such methods may be difficult on these data since there is no gold standard regarding the true geometry of the brain anatomy or fiber bundles reconstructed in each particular case. In order to study, validate and compare various de-noising and fiber-tracking methods, there is a need for a (mathematical) phantom consisting of semi-realistic images with well-known properties. In this work we generate such a phantom and provide a description of the calculation process all the way up to voxel-wise diffusion tensor visualization.en_US
dc.language.isoengeng
dc.publisherIEEEen_US
dc.subjectBiomedical MRIeng
dc.subjectBraineng
dc.subjectImage denoisingeng
dc.subjectImage segmentationeng
dc.subjectMedical image processingeng
dc.subjectPhantomseng
dc.titleGenerating a synthetic diffusion tensor dataseten_US
dc.typePeer reviewed
dc.typeJournal article
dc.identifier.doihttps://doi.org/10.1109/cbms.2005.58
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420nob


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