Convolution-Based Forced Detection Monte Carlo Simulation Incorporating Septal Penetration Modeling

In single photon emission computed tomography (SPECT) imaging, photon transport effects such as scatter, attenuation, and septal penetration can negatively affect the quality of reconstructed image and quantitation. As such, it is useful to model these factors as careful as possible during image reconstruction in order to reduce the impact. Many of these effects can be included in Monte Carlo (MC) based image reconstruction using convolution-based forced detection (CFD-MC). However, in CFD-MC, often only the geometric response of collimators is modeled, thereby make the assumption that the collimator materials are thick enough to completely absorb photons traveling through them. However, in order to retain high collimator sensitivity and high spatial resolution, it is required that the septa be as thin as possible, thus resulting in a significant amount septal penetration for high energy radionuclides. A method for modeling the effects of collimator response including both collimator septal penetration and geometric response using ray tracing (RT) techniques has been performed and included into a CFD-MC program. Two lookup tables are precalculated based on the collimator parameters and radionu-clide, and subsequently incorporated into SIMIND MC program. One table consists of the cumulative septal thickness between any point on the collimator and the center, while the other table represents the resultant collimator response for a point source at differing distances from the collimator and for various energies. A series of RT simulations have been compared to experimental data for different radionuclides and collimators. The results of RT techniques matches the experimental data very well, producing correlation coefficients higher than 0.995. In order to achieve noise-free projection images from MC, it has been seen that the inclusion of the RT implementation for collimator response increases the speed of simulation by a factor of 7,500 compared to the conventional fo- rced detection (FD) SIMIND MC program.