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Ensemble Kalman filters for reliability estimation in perfusion inference
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 4501234
Author(s) Zaspel, Peter
Author(s) at UniBasel Zaspel, Peter
Year 2019
Title Ensemble Kalman filters for reliability estimation in perfusion inference
Journal International Journal for Uncertainty Quantification
Volume 9
Number 1
Pages / Article-Number 15-32
Abstract We consider the solution of inverse problems in dynamic contrast–enhanced imaging by means of Ensemble Kalman Filters. Our quantity of interest is blood perfusion, i.e. blood flow rates in tissue. While existing approaches to compute blood perfusion parameters for given time series of radiological measurements mainly rely on deterministic, deconvolution–based methods, we aim at recovering probabilistic solution information for given noisy measurements. To this end, we model radiological image capturing as sequential data assimilation process and solve it by an Ensemble Kalman Filter. Thereby, we recover deterministic results as ensemble–based mean and are able to compute reliability information such as probabilities for the perfusion to be in a given range. Our target application is the inference of blood perfusion parameters in the human brain. A numerical study shows promising results for artificial measurements generated by a Digital Perfusion Phantom.
Publisher Begell House
ISSN/ISBN 2152-5080 ; 2152-5099
URL https://arxiv.org/abs/1810.09290
edoc-URL https://edoc.unibas.ch/70343/
Full Text on edoc No
Digital Object Identifier DOI 10.1615/Int.J.UncertaintyQuantification.2018024865
ISI-Number WOS:000465572600002
Document type (ISI) Article
 
   

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