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Contrast agent quantification by using spatial information in dynamic contrast enhanced MRI
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
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2016 (English)Manuscript (preprint) (Other academic)
Abstract [en]

The purpose of this study is to investigate a method, using simulations, toimprove contrast agent quantication in Dynamic Contrast Enhanced MRI.Bayesian hierarchical models (BHMs) are applied to smaller images such that spatial information can be incorporated. Then exploratory analysisis done for larger images by using maximum a posteriori (MAP).

For smaller images: the estimators of proposed BHMs show improvementsin terms of the root mean squared error compared to the estimators in existingmethod for a noise level equivalent of a 12-channel head coil at 3T. Moreover,Leroux model outperforms Besag models. For larger images: MAP estimatorsalso show improvements by assigning Leroux prior.

Place, publisher, year, edition, pages
2016. , p. 12
Keywords [en]
Contrast agent quantication, BHM, Besag, Leroux, INLA, MAP
National Category
Probability Theory and Statistics Medical Image Processing
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-141525OAI: oai:DiVA.org:umu-141525DiVA, id: diva2:1155144
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2019-11-19
In thesis
1. Enhanced block sparse signal recovery and bayesian hierarchical models with applications
Open this publication in new window or tab >>Enhanced block sparse signal recovery and bayesian hierarchical models with applications
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis is carried out within two projects ‘Statistical modelling and intelligentdata sampling in Magnetic resonance imaging (MRI) and positron-emission tomography(PET) measurements for cancer therapy assessment’ and ‘WindCoE -Nordic Wind Energy Center’ during my PhD study. It mainly focuses on applicationsof Bayesian hierarchical models (BHMs) and theoretical developments ofcompressive sensing (CS). Under the first project, Paper I improves the quantityestimation of MRI parametric imaging by utilizing inherent dependent structure inthe image through BHMs; Paper III constructs a theoretically unbiased and asymptoticallynormal estimator of sparsity of a sparsified MR image by using a BHM;Paper IV extends block sparsity estimation from real-valued signal recovery tocomplex-valued signal recovery. It also demonstrates the importance of accuratelyestimating the block sparsity through a sensitivity analysis; Paper V proposes anew measure, i.e. q-ratio block constrained minimal singular value, of measurementmatrix for block sparse signal recovery. An algorithm for computing thisnew measure is also presented. In the second project, Paper II estimates the uncertaintyof Weather Research and Forecasting (WRF) model’s daily-mean 2-metertemperature in a cold region by using a BHM. It is a computationally cheaper andfaster alternative to traditional ensemble approach. In summary, this thesis makessignificant contributions in improving and optimizing the estimation proceduresof parameters of interest in MRI and WRF in practice, and developing the novelestimators and measure under the framework of CS in theory.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2019. p. 35
Series
Research report in mathematical statistics, ISSN 1653-0829 ; 69
Keywords
Magnetic resonance imaging, Bayesian hierarchical models, Weather Research and Forecasting, Compressive sensing, Block sparsity, Multivariate isotropic symmetric a-stable distribution, q-ratio block constrained minimal singular value
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-165285 (URN)978-91-7855-148-4 (ISBN)
Public defence
2019-12-17, N450, Naturvetarhuset, Umeå University, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2019-11-26 Created: 2019-11-19 Last updated: 2019-11-26Bibliographically approved

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arXiv:1701.06445

Authority records BETA

Wang, JianfengGarpebring, AndersBrynolfsson, PatrikLiu, XijiaYu, Jun

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Wang, JianfengGarpebring, AndersBrynolfsson, PatrikLiu, XijiaYu, Jun
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