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.