Improved simultaneous estimation of tracer kinetic models with artificial immune network based optimization method

• A method for simultaneous estimation of tracer kinetic models is presented.
• The proposed artificial immune network is scalable in dynamic FDG animal PET study.
• The proposed method is more effective to multimodal optimization of parameters.
• Without initial values, relaxed boundary constraints of parameters are required.

Tracer kinetic modeling (TKM) is a promising quantitative method for physiological and biochemical processes in vivo. In this paper, we investigated the applications of an immune-inspired method to better address the issues of Simultaneous Estimation (SIME) of TKM with multimodal optimization. Experiments of dynamic FDG PET imaging experiments and simulation studies were carried out. The proposed artificial immune network (TKM_AIN) shows more scalable and effective when compared with the gradient-based Marquardt–Levenberg algorithm and the scholastic-based simulated annealing method.