Denis Peruzzo1, Gianluigi Pillonetto1, Alessandra Bertoldo1, Claudio Cobelli1
1Department of Information Engineering, University of Padova, Padova, Italy
A deconvolution operation must be performed to obtain the residue function (R(t)) in DSC-MRI images quantification. In this study, a kernel based deconvolution approach is proposed and validated on both simulated and clinical data. It tackles the problem in a fully Bayesian framework and includes information on both R(t) continuity and on the system BIBO stability. It has been shown to provide more physiological R(t) estimates and more accurate CBF values than SVD and cSVD. Furthermore, not only is DNP insensitive to the delay between the AIF and the C(t), but it can also estimate it.