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Bayesian analysis of dynamic MR breast images. P. Barone, F. de Pasquale, G. Sebastiani Istituto per le Applicazioni del Calcolo ‘‘M. Picone’’ CNR, Rome (Italy) J. Stander Department of Mathematics and Statistics University of Plymouth, UK M. Crecco, A. M. Di Nallo, F. P. Gentile
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Bayesian analysis of dynamic MR breast images P. Barone, F. de Pasquale, G. Sebastiani Istituto per le Applicazioni del Calcolo ‘‘M. Picone’’ CNR, Rome (Italy) J. Stander Department of Mathematics and Statistics University of Plymouth, UK M. Crecco, A. M. Di Nallo, F. P. Gentile ‘‘Istituto Regina Elena’’ Rome (Italy)
Introduction Data and aims of the analysis: • Dynamic breast MRI consists of a temporal sequence of images of the same slice acquired after the injection of a contrast agent into the blood stream • Typically, for breast studies, a few tens of 256 x 256 images are acquired consecutively • ‘In vivo’ tissue perfusion maps in an organ of interest (breast)
These data are typically affected by two main kind of degradation: random degradation due to the measurement noise; deterministic degradation due to the patient motion • Radiologists’s aim is to extract as much clinical information as possible from the image sequence as few images of easy interpretation and with low degradation (label each pixel of the image as non tumoral, benign tumoral and malign tumoral tissues) • ‘real time’ analysis
Data first sequence image last sequence image
Non-parametric approach • Bayesian estimation of true image intensity for each pixel of a selected R.O.I. and for each time without using a parametric spatio temporal model for the acquired signal • Relevant quantities (class attributes) describing image intensity variation are computed independently for each pixel from these estimated image intensity profiles • These are used to classify the underlying scene into a certain number of categories based on the temporal pattern of intensity
Original R.O.I Estimated R.O.I. Estimation results Patient 1 Patient 2 Patient 3
Non-parametric approach: estimated attributes First attribute Second attribute
Parametric approach • We adopted here a parametric spatio temporal model for the acquired signal to describe true image intensities at each pixel • Model parameters are then displayed as images and can be used for image classification • After paramater estimation, the model can be used to estimate true image intensities.
