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Generating a synthetic diffusion tensor dataset

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dc.contributor.author Bergmann, Ørjan
dc.contributor.author Lundervold, Arvid
dc.contributor.author Steihaug, Trond
dc.date.accessioned 2008-10-16T12:22:21Z
dc.date.available 2008-10-16T12:22:21Z
dc.date.issued 2005
dc.identifier.citation Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on 2005: 277- 281 en
dc.identifier.isbn 0-7695-2355-2
dc.identifier.issn 1063-7125
dc.identifier.uri http://dx.doi.org/10.1109/CBMS.2005.58
dc.identifier.uri http://hdl.handle.net/1956/2799
dc.description.abstract During 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
dc.language.iso eng en
dc.publisher IEEE en
dc.subject Biomedical MRI en
dc.subject Brain en
dc.subject Image denoising en
dc.subject Image segmentation en
dc.subject Medical image processing en
dc.subject Phantoms en
dc.title Generating a synthetic diffusion tensor dataset en
dc.type Peer reviewed en
dc.type Journal article en
dc.subject.nsi VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420 no


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