Vis enkel innførsel

dc.contributor.authorBergmann, Ørjaneng
dc.date.accessioned2008-10-16T11:57:48Z
dc.date.available2008-10-16T11:57:48Z
dc.date.issued2008-02-27eng
dc.identifier.isbn978-82-308-0520-6 (print version)en_US
dc.identifier.urihttps://hdl.handle.net/1956/2798
dc.description.abstractFiber tracking is a relatively recent methodology, made possible by access to new highly advanced MR scanners able to produce high-quality diffusion tensor images (DTI), which promises clinicians a possibility to observe the actual trajectories of fibers and the connectivity of the living human brain. Analysis of such data provides clinicians with a unique tool for diagnosing and predicting disease which affect the “wiring” of the brain, such as stroke, tumors, multiple sclerosis and Alzheimer’s disease. The purpose of this PhD thesis has been to explore ways to improve the quality of DTI as well as the accuracy of the fiber tractography which can be estimated from such data. In order to achieve this purpose we have investigated two fundamentally different areas to improve DTI results; data pre-processing and more accurate data descriptors.en_US
dc.language.isoengeng
dc.publisherThe University of Bergenen_US
dc.relation.haspartPaper A: Journal of Digital Imaging, Bergmann, Ørjan; Christiansen, Oddvar; Lie, Johan and Arvid Lundervold, Shape-Adaptive DCT for Denoising of 3D Scalar and Tensor Valued Images. Preprint version accepted for publication. Copyright 2007 Society for Imaging Informatics in Medicine. Published by Springer New York. The published version is available at: <a href="http://dx.doi.org/10.1007/s10278-007-9088-6">http://dx.doi.org/10.1007/s10278-007-9088-6</a>en_US
dc.relation.haspartPaper B: Bergmann, Ørjan and Trond Steihaug, Solving a TRS which has linear inequality constraints. Preprint. Copyright 2007 Bergman et al.en_US
dc.relation.haspartPaper C: Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on, Bergmann, Ørjan; Lundervold, Arvid; Steihaug, Trond, Generating a synthetic diffusion tensor dataset, pp. 277- 281. Copyright 2005 IEEE. <a href="http://dx.doi.org/10.1109/CBMS.2005.58">http://dx.doi.org/10.1109/CBMS.2005.58</a>en_US
dc.relation.haspartPaper D: MICCAI, LNCS 4191, Bergmann, Ørjan; Kindlmann,Gordon; Lundervold, Arvid; Westin,Carl-Fredrik, Diffusion k-tensor Estimation from Q-ball Imaging using iscretized Principal Axes.published in the proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 268–275. Copyright 2006 Springer. Full-text not available due to publisher restrictions. The published version is available at: <a href="http://dx.doi.org/10.1007/11866763_33"> http://dx.doi.org/10.1007/11866763_33</a>en_US
dc.relation.haspartPaper E: Biomedical Imaging: From Nano to Macro, 2007. 4th IEEE International Symposium on, Bergmann, Ørjan; Kindlmann, Gordon; Peled, Sharon; Westin, Carl-Fredrik, Two-tensor Fiber Tractography, pp. 796 – 799. Copyright 2007 IEEE. <a href="http://dx.doi.org/10.1109/ISBI.2007.356972">http://dx.doi.org/10.1109/ISBI.2007.356972</a>en_US
dc.titleOptimization issues in medical imaging and fiber-trackingen_US
dc.typeDoctoral thesis
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420nob


Tilhørende fil(er)

Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel