System response matrix denoising in measurement space in positron emission tomography

The system response matrix (SRM) maps measurement space with image space in emission tomography. The quality of the SRM is of significant importance in statistical iterative methods. Different approaches are available to compute the SRM. Generically, high image quality and accurate quantitative information depends on the accuracy and precision of the SRM elements. There is growing interest on Monte Carlo (MC) methods to compute the SRM due to the high accuracy achieved. The main drawback is that the SRM elements precision is strongly related with the number of events simulated, which depends on the simulation time. Long MC simulations produce low noise/high precision SRM elements. In this work we investigate the impact of a denoising filter applied to the SRM elements of a short-time simulated SRM on the image quality and quantitative information accuracy. Results show that a ID mean filter applied longitudinally to profiles extracted from every volume of response produces better noise performance than the unfiltered SRM with ten times higher number of simulated events, at the cost of slightly degraded resolution. These results are further compared with post-reconstruction filtered images obtained with the unaltered SRM, demonstrating a larger level of spatial resolution degradation compared to those obtained with the filtered SRM.