Optimization of image reconstruction for the RatCAP (PET) tomograph: An analysis of the statistical quality of the system response matrix

A. highly accurate system model is the basis of statistical iterative reconstruction for the RatCAP. The model is used to generate a fully 3D Monte Carlo system response matrix (SRM) for maximum likelihood expectation maximization (MLEM) reconstruction. Significant efforts have been taken to ensure a faithful match to the actual tomograph. One of the main considerations with Monte Carlo SRMs is statistical accuracy, as any error in the matrix could propagate into the reconstructed image. In theory, it is possible to simulate an arbitrarily large number of events, making the statistical errors in the matrix insignificant compared to the data. However, at a certain point, errors in the data limit any further improvement in accuracy due to higher statistics in the matrix. An effort to achieve the best possible quantitative accuracy, while optimizing the tradeoff between model accuracy and computation time is presented. Realistic rat brain simulations have been reconstructed using multiple realizations of system matrices at varying count levels. The sensitivity of our methods to the errors in the data, as well as the reconstruction algorithm has been analyzed. The overall goal of this study is to find the SRM with the best tradeoff between resolution and noise for our reconstruction, and simultaneously validate the use of our model for quantitative analyses with the RatCAP.