Ensemble Kalman Filters for reliability estimation in perfusion inference

TL;DR

This paper applies Ensemble Kalman Filters to infer blood perfusion in brain imaging, providing probabilistic estimates and reliability measures from noisy radiological data, advancing beyond traditional deterministic methods.

Contribution

It introduces a probabilistic approach using Ensemble Kalman Filters for blood perfusion estimation, enabling uncertainty quantification in dynamic contrast-enhanced imaging.

Findings

Promising results on artificial data from a Digital Perfusion Phantom.

Enables probabilistic inference and reliability assessment.

Improves upon deterministic deconvolution methods.

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.