Multidimensional B-spline parameterization of the detection probability of PET systems to improve the efficiency of Monte Carlo simulations

Accurate modeling of system response and scatter distribution is crucial for image reconstruction in emission tomography. Monte Carlo simulations are very well suited to calculate these quantities. However, Monte Carlo simulations are also slow and many simulated counts are needed to provide a sufficiently exact estimate of the detection probabilities. In order to overcome these problems, we propose to split the simulation into two parts, the detection system and the object to be imaged (the patient). A so-called 'virtual boundary' that separates these two parts is introduced. Within the patient, particles are simulated conventionally. Whenever a photon reaches the virtual boundary, its detection probability is calculated analytically by evaluating a multi-dimensional B-spline that depends on the photon position, direction and energy. The unknown B-spline knot values that define this B-spline are fixed by a prior 'pre-' simulation that needs to be run once for each scanner type. After this pre-simulation, the B-spline model can be used in any subsequent simulation with different patients. We show that this approach yields accurate results when simulating the Biograph 16 HiREZ PET scanner with Geant4 Application for Emission Tomography (GATE). The execution time is reduced by a factor of about 22 x (scanner with voxelized phantom) to 30 x (empty scanner) with respect to conventional GATE simulations of same statistical uncertainty. The pre-simulation and calculation of the B-spline knots values could be performed within half a day on a medium-sized cluster.