An expectation-maximisation approach for simultaneous pixel classification and tracer kinetic modelling in dynamic contrast enhanced-magnetic resonance imaging.
Traditionally, tracer kinetic modelling and pixel classification of DCE-MRI studies are accomplished separately, although they could greatly benefit from each other. In this article, we propose an expectation-maximisation scheme for simultaneous pixel classification and compartmental modelling of DCE-MRI studies. The key point in the proposed scheme is the estimation of the kinetic parameters (K(trans) and K(ep)) of the two-compartmental model. Typically, they are estimated via nonlinear least-squares fitting. In our scheme, by exploiting the iterative nature of the EM algorithm, we use instead a Taylor expansion of the modelling equation. We developed the theoretical framework for the particular case of two classes and evaluated the performances of the algorithm by means of simulations. Results indicate that the accuracy of the proposed method supersedes the traditional pixel-by-pixel scheme and approaches the theoretical lower bound imposed by the Cramer-Rao theorem. Preliminary results on real data were also reported.
Department of Biomedical, Electronic and Telecommunication Engineering, University Federico II of Naples, via Claudio 21, 80131 Naples, Italy. msansone@unina.it
Mario
M
Sansone
Roberta
R
Fusco
Antonella
A
Petrillo
Mario
M
Petrillo
Marcello
M
Bracale
D000465:Algorithms; D003287:Contrast Media / Q000493:pharmacokinetics; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D009369:Neoplasms / Q000175:diagnosis*
D003287:Contrast Media