A multivariate density estimator for contrast agent injection monitoring using a Bayesian sparse kernel approach

The administration of intravenous contrast media during CT examinations is routine, but carries with it a risk of extravasation. Hence, we define an injection state to be either intravenous or extravasated. With a new Doppler ultrasound monitoring technique, we propose a method for estimating the probability of an injection state during the various stages of an examination. A smoothed time-frequency representation of the Doppler signal is used to analyze at which frequencies there is the largest difference in response between signals from intravenous and extravasated injections. A vector of response values based on this analysis forms this study´s feature space. A Relevance Vector Machine is used to estimate the probability density for a particular injection state. We present preliminary results (n=5) showing the time-frequency representation of the Doppler ultrasound signal, the frequency analysis and the density estimation.