Parallelization and Runtime Prediction of the ListMode OSEM Algorithm for 3D PET Reconstruction

For high-resolution PET (Positron Emission Tomography) image reconstructions, the LM OSEM (ListMode Ordered Subset Expectation Maximization) algorithm proves to be quite appropriate, but it is very time-consuming. In order to improve its runtime, we parallelized the algorithm and implemented it on different classes of parallel computer architectures: with shared, distributed and hybrid memory. These implementations reduce the reconstruction time from more than two hours to six minutes. We suggest an analytical model for predicting parallel LM OSEM runtimes on distributed-memory machines, and verify our model in runtime experiments on different reconstruction problems, which demonstrate a prediction error of less than 10 %. The model allows the user to achieve a desired reconstruction quality while minimizing resource usage.