Ensemble Kalman Filters for reliability estimation in perfusion inference
For medical imaging researchers, this provides a probabilistic alternative to deterministic deconvolution methods for perfusion inference, though results are limited to synthetic data.
This paper applies Ensemble Kalman Filters to estimate blood perfusion parameters from dynamic contrast-enhanced imaging, recovering probabilistic reliability information (e.g., probabilities for perfusion in a given range) in addition to deterministic results. Numerical experiments on a Digital Perfusion Phantom show promising results.
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.