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.
Forfang
Morten
M
Faculty of Engineering, Vestfold University College, Norway. mjg@hive.no
Hoff
Lars
L
Berard-Andersen
Nicolay
N
Olsen
Gjermund F
GF
Brabrand
Knut
K
D000465:Algorithms; D001499:Bayes Theorem; D003287:Contrast Media / Q000494:pharmacology*; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D011336:Probability; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D018608:Ultrasonography, Doppler / Q000379:methods
D003287:Contrast Media