TY - JOUR
T1 - An expectation-maximisation approach for simultaneous pixel classification and tracer kinetic modelling in dynamic contrast enhanced-magnetic resonance imaging
AU - Sansone, Mario
AU - Fusco, Roberta
AU - Petrillo, Antonella
AU - Petrillo, Mario
AU - Bracale, Marcello
PY - 2011/4
Y1 - 2011/4
N2 - 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.
AB - 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.
KW - Classification
KW - DCE-MRI
KW - Expectation-maximisation
KW - Tracer kinetic modelling
UR - http://www.scopus.com/inward/record.url?scp=79955602024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79955602024&partnerID=8YFLogxK
U2 - 10.1007/s11517-010-0695-x
DO - 10.1007/s11517-010-0695-x
M3 - Article
C2 - 21046274
AN - SCOPUS:79955602024
VL - 49
SP - 485
EP - 495
JO - Medical and Biological Engineering and Computing
JF - Medical and Biological Engineering and Computing
SN - 0140-0118
IS - 4
ER -