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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 (Engelska)Ingår i: CAIP 2017: Computer Analysis of Images and Patterns, 2017, Vol. 10424, s. 372-383Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
2017. Vol. 10424, s. 372-383
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
Nyckelord [en]
Spatial regularization, Diffusion tensor, Spatial priors Markov chain, Monte Carlo Fractional anisotropy
Nationell ämneskategori
Medicinteknik
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
Konferens
International Conference on Computer Analysis of Images and Patterns
Anmärkning

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

Tillgänglig från: 2017-08-17 Skapad: 2017-08-17 Senast uppdaterad: 2019-11-19
Ingår i avhandling
1. Advanced analysis of diffusion MRI data
Öppna denna publikation i ny flik eller fönster >>Advanced analysis of diffusion MRI data
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive imaging modality which can measure diffusion of water molecules, by making the MRI acquisition sensitive to diffusion. Diffusion MRI provides unique possibilities to study structural connectivity of the human brain, e.g. how the white matter connects different parts of the brain. Diffusion MRI enables a range of tools that permit qualitative and quantitative assessments of many neurological disorders, such as stroke and Parkinson.

This thesis introduces novel methods for diffusion MRI data analysis. Prior to estimating a diffusion model in each location (voxel) of the brain, the diffusion data needs to be preprocessed to correct for geometric distortions and head motion. A deep learning approach to synthesize diffusion scalar maps from a T1-weighted MR image is proposed, and it is shown that the distortion-free synthesized images can be used for distortion correction. An evaluation, involving both simulated data and real data, of six methods for susceptibility distortion correction is also presented in this thesis.

A common problem in diffusion MRI is to estimate the uncertainty of a diffusion model. An empirical evaluation of tractography, a technique that permits reconstruction of white matter pathways in the human brain, is presented in this thesis. The evaluation is based on analyzing 32 diffusion datasets from a single healthy subject, to study how reliable tractography is. In most cases only a single dataset is available for each subject. This thesis presents methods based on frequentistic (bootstrap) as well as Bayesian inference, which can provide uncertainty estimates when only a single dataset is available. These uncertainty measures can then, for example, be used in a group analysis to downweight subjects with a higher uncertainty.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2019. s. 93
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2017
Nyckelord
Diffusion MRI, Distortion Correction, Deep Learning, Uncertainty Estimation
Nationell ämneskategori
Medicinsk bildbehandling
Identifikatorer
urn:nbn:se:liu:diva-161288 (URN)10.3384/diss.diva-161288 (DOI)9789175190037 (ISBN)
Disputation
2019-12-06, Hugo Theorell, Building 448, Campus US, Linköping, 13:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-11-19 Skapad: 2019-11-19 Senast uppdaterad: 2019-11-19Bibliografiskt granskad

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

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