Nonlinear stochastic deconvolution of bolus-tracking MRI: assessment and comparison on simulated data vs other methods in presence/absence of dispersion

F. Zanderigo, A. Bertoldo, G. Pillonetto, C. Cobelli Information Engineering, University of Padova, Padova, Italy, Italy, Information Engineering, University of Padova, Padova, ITALY, Italy Introduction. Bolus tracking MRI allows to quantify cerebral blood flow (CBF), volume (CBV) and mean transit time (MTT) by deconvolution from arterial input function, AIF(t), and tissue concentration, C(t), measures: C(t)=CBF٠[AIF(t)⊗R(t)] (eq.1), where R(t) is the tissue residue function. In particular, one obtains R*(t)=CBF·R(t) and estimates CBF from its maximum value. The most commonly used deconvolution method is singular value decomposition (SVD) [1], a nonparametric technique which is extremely fast, but shows some limitations: presence of nonphysiological oscillations in R(t) and dependence of estimated CBF values on selected threshold value as well as delay and dispersion in AIF [2,3]. More recently, other improved deconvolution approaches have been proposed including SVD using a Block-Circulant matrix (BC) [4] and Tikhonov Regularization (TIKH) [5]. In [6] we proposed a new nonlinear stochastic regularization method (NSR) able to account for smoothness and non-negativity of R(t) and to handle dispersion of AIF. Here NSR is tested and validated on simulated data and results compared to those obtained by using SVD, BC, and TIKH.