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Bayesian Diffusion Tensor Estimation with Spatial Priors
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0003-2193-6003
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
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2017 (engelsk)Inngår i: CAIP 2017: Computer Analysis of Images and Patterns, 2017, Vol. 10424, s. 372-383Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

sted, utgiver, år, opplag, sider
2017. Vol. 10424, s. 372-383
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
Emneord [en]
Spatial regularization, Diffusion tensor, Spatial priors Markov chain, Monte Carlo Fractional anisotropy
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-139844DOI: 10.1007/978-3-319-64689-3_30ISI: 000432085900030ISBN: 978-3-319-64689-3 (digital)ISBN: 978-3-319-64688-6 (tryckt)OAI: oai:DiVA.org:liu-139844DiVA, id: diva2:1133926
Konferanse
International Conference on Computer Analysis of Images and Patterns
Merknad

Funding agencies: Information Technology for European Advancement (ITEA) 3 Project BENEFIT (better effectiveness and efficiency by measuring and modelling of interventional therapy); Swedish Research Council [2015-05356, 2013-5229]; National Institute of Dental and Craniof

Tilgjengelig fra: 2017-08-17 Laget: 2017-08-17 Sist oppdatert: 2018-06-01

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Gu, XuanSidén, PerWegmann, BertilEklund, AndersVillani, MattiasKnutsson, Hans

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