CMR Perfusion Imaging proved its role in patient triage, identifying visually ischemia and its capability in quantifying heart perfusion1,2, but failed to transfer this technology to clinical routine and to show how this worth information could be used to improve tissue lesions comprehension. Deconvolution techniques are sensitive to noise present on time intensity curves S(t), when observation scale decreases. Automated segmentation prior modelling would be a powerful adjunct. Indeed, prior tissue classification would optimize perfusion quantification accuracy since enabling advanced modelling leading to additional markers while reducing processing time. Such automated method is proposed here.