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Generating a synthetic diffusion tensor dataset

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dc.contributor.author Bergmann, Ørjan eng
dc.contributor.author Lundervold, Arvid eng
dc.contributor.author Steihaug, Trond eng
dc.date.accessioned 2008-10-16T12:22:21Z
dc.date.available 2008-10-16T12:22:21Z
dc.date.issued 2005 eng
dc.identifier.citation Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on 2005: 277- 281 en
dc.identifier.isbn 0-7695-2355-2 eng
dc.identifier.issn 1063-7125 eng
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 eng
dc.publisher IEEE eng
dc.subject Biomedical MRI eng
dc.subject Brain eng
dc.subject Image denoising eng
dc.subject Image segmentation eng
dc.subject Medical image processing eng
dc.subject Phantoms eng
dc.title Generating a synthetic diffusion tensor dataset eng
dc.type Peer reviewed eng
dc.type Journal article eng
dc.subject.nsi VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420 nob
bora.peerreviewed Peer reviewed eng
bibo.doi http://dx.doi.org/10.1109/CBMS.2005.58 eng
dc.identifier.doi http://dx.doi.org/10.1109/CBMS.2005.58


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