Monte-Carlo based statistical SPECT reconstruction: Influence of number of photon tracks

Accuracy of SPECT images improves when photon scatter is modeled more realistically during iterative reconstruction. Monte-Carlo simulation (MCS) is a general and efficient, but slow, method for detailed modeling and correction of scatter. Recently, we proposed an efficient strategy for fully 3D statistical reconstruction using accelerated MCS as a forward projector. The acceleration method used was convolution forced detection (CFD). The question that remains is how many calculated photon histories during CFD based MCS are sufficient in order to ensure that the noise in the reconstructed image does not increase to an unacceptable level. In this work, we attempt to answer this question based on experimental data mimicking a Tc-99m cardiac perfusion study. We generated dual matrix ordered subset reconstructions with different numbers of photon histories during MCS forward projection. The noise content caused by the MCS re-projection was compared with that of the noise induced by measured noise in the myocardial area. Profiles taken through the central myocardium slice were compared for reconstructions with a different number of photon tracks, for reconstructions from different noise realizations using a repeat measurement and for reconstructions with different random seeds for MCS re-projection. A minimum of 10 5 photon histories per subset was required to produce accurate images. For this case the typical reconstruction time required for a 64 3 grid is about 5 minutes on a dual-cpu PC (2.66 GHz). Therefore, for this amount of photons the reconstruction time is sufficiently short to apply in clinical routine