Bayesian analysis of dynamic Magnetic Resonance breast images

Dynamic magnetic resonance imaging with contrast agent is a very promising technique for mammography. A temporal sequence of magnetic resonance images of the same slice are acquired following the injection of a contrast agent in the blood stream. The image intensity depends on the local concentration of the contrast agent so that tissue perfusion can be studied using the image sequence. A new statistical method of analyzing such sequences is presented. The method is developed within the Bayesian framework. A specific statistical model is used to take into account image degradation. In addition, a suitable Markov random field allows us to model some relevant ``a priori'' information on the quantities to be estimated. Inference is based on simulations from the posterior distribution obtained by means of Markov chain algorithms. The issue of hyper-parameter estimation is also addressed. Image classification is also performed by means of a new Bayesian method. Some results obtained from sequences of dynamic magnetic resonance images of human breasts will be illustrated.