• DCE-MRI
• T1 measuring
• quantitative analysis of tumor perfusion
• bicompartimental model

The microenvironment of solid human tumors is characterized by heterogeneity in oxygenation and by proliferation of a network of blood vessels that provides oxygen and nutrients and removes waste products.
The capability of a tumor to metastasize is linked to the well known hyper permeability of tumor vessels; in fact angiogenic primary tumors possess a large number of micro-vessels through which the metastasizing cells are shed into the blood stream. Pioneering studies performed by Folkman in 1971 proposed an insightful anticancer therapy by starvation of blood supply, Folkman's intuition that tumor growth and metastasis strictly depend on angiogenesis led to the idea that blocking tumor nourishment could be one of the ways to avoid its spread. Many imaging strategies have been used to determine angiogenesis in vivo, among them Dynamic Contrast Enhanced MRI (DCE-MRI) which provides a powerful tool for the rapid evaluation of the acute pharmacodynamic effect of the most recent agents in clinical trials, most notably in the
case of mechanisms that affect tumor perfusion. One of the attractives of dynamic post contrast imaging is the insight it offers into the distribution kinetics of contrast agent in the tissue. These quantities are generally derived from simple models of the tissue as a compartmentalized system (usually a plasma-interstitial two-compartments model is used), using a kinetic analysis originally developed for use with nuclear medicine tracers. In this study a three parameters model has been used in order to describe the transport of tracer in the tissues. The first one takes into account the plasma volume in the voxel or in the region of interest being examined, the second is related to the amount of tracer that enters the EES and the last one determines the washout rate from EES back into the blood plasma. An artery input function (AIF), namely the concentration of contrast in the plasma, is provided as well. In order to obtain these parameters it is necessary to perform the fit of the chosen model on the concentration-curves vs time. It is possible to correlate the contrast agent concentration with the difference of relaxation rate (the inverse of longitudinal relaxation time); this way the concentration-curves can be achieved by means of T1-weighted images. In this work, an acquisition protocol has been optimized in order to provide the images from which extracting the data related to the tumor perfusion. Post processing tools, which carry out the fitting, the smoothing, and, the registration, have been developed and tested, as well. These tools can provide the oncologysts and radiologists a way to perform reproducible and quantitative estimation of the tumor perfusion in anti-angiogenic therapy.