Liver tumor assessment with DCE-MRI

Abstract

Dynamic-contrast enhanced MRI (DCE-MRI) is used in clinical practice to assess liver tumor malignancy. An algorithm to get information for automatic classification of tumors is presented. The Maximum value and WashIn and WashOut rates, obtained from the perfusion curves measured from the DCE-MRI images, are used in the classification process. The perfusion curves are described by a linear discrete pharmacokinetic (PK) model, based on multi-compartment paradigm where the input is the bolus injection. The arterial input function (AIF) that is usually estimated in the closest artery is assumed here to be the response of a second order linear system to the bolus injection. Therefore, the complete chain is modeled as a third order system with a single zero. The alignment procedure is performed by using the Mutual Information (MI) criterion with a non-rigid transformation to compensate the displacements occurred during the acquisition process. It is shown that the Maximum values and the WashIn and WashOut rates of the perfusion curves in malignant tumors are higher than in healthy tissues. This fact is used to classify them. Furthermore, it is also shown, that inside the tumor, the parameters associated with the perfusion curves for each pixel (time courses) present a higher variance than in the healthy tissues, which may also be used to increase the accuracy of the classifier. Examples using real data are presented.