Ensemble Kalman filters for reliability estimation in perfusion inference

Zaspel, Peter. (2019) Ensemble Kalman filters for reliability estimation in perfusion inference. International Journal for Uncertainty Quantification, 9 (1). pp. 15-32.

Full text not available from this repository.

Official URL: https://edoc.unibas.ch/70343/

Downloads: Statistics Overview


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.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Mathematik > Computational Mathematics (Harbrecht)
UniBasel Contributors:Zaspel, Peter
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Begell House
Note:Publication type according to Uni Basel Research Database: Journal article
Related URLs:
Last Modified:17 Aug 2020 06:25
Deposited On:14 Aug 2020 14:23

Repository Staff Only: item control page