Optimal design in PET data acquisition: a new approach using simulated annealing and component-wise Metropolis updating

Dynamic positron emission tomography is a powerful tool of measuring biological activities in vivo. Due to the inherent high noise level, there are always concerns about how to increase the signal to noise ratio. One possible approach is to optimize the experimental design. In this study we propose a discretized representation of the experimental design problem and transform it to a combinatorial problem. This combinatorial optimization problem then can be solved using simulated annealing with Component-wise Metropolis Monte Carlo simulation. We showed that using this novel approach one can deal with the problem of designing an optimal input function and an optimal sampling schedule efficiently. Our results show that the current dynamic scanning of approximate twenty frames doesn't give us much more information than an optimized four-frame schedule, and needlessly increases storage requirements. This is consistent with the conclusion given by Li et al. We also reproduced the optimal sampling schedule for FDG study. Moreover, we show that the single bolus ejection is almost optimal in the sense of D-optimal design as well as many other measures.