2019/08/29 by Crisci, Serena, Piana, Michele, Ruggiero, Valeria +1
#34A55 #65J20 #65K05 #92C55 #FOS: Mathematics #Numerical Analysis (math.NA)
paper · doi:10.48550/arxiv.1908.11139
Parametric imaging of nuclear medicine data exploits dynamic functional images in order to reconstruct maps of kinetic parameters related to the metabolism of a specific tracer injected in the biological tissue. From a computational viewpoint, the realization of parametric images requires the pixel-wise numerical solution of compartmental inverse problems that are typically ill-posed and nonlinear. In the present paper we introduce a fast numerical optimization scheme for parametric imaging relying on a regularized version of the standard affine-scaling Trust Region method. The validation of this approach is realized in a simulation framework for brain imaging and comparison of performances is made with respect to a regularized Gauss-Newton scheme and a standard nonlinear least-squares algorithm.