Accelerated list-mode EM algorithm

List-mode data preserves all sampling information in three-dimensional (3-D) PET imaging and can reduce storage requirements for short-time frame acquisitions. List-mode expectation maximization-maximum likelihood (EM-ML), which has been implemented in a number of forms (such as the EM algorithm for list-mode maximum likelihood, the FAIR algorithm and COSEM), is an obvious choice to reconstruct from such data sets when the statistics are low. However, these methods can be slow for large quantities of list-mode data and it is desirable to accelerate them. This work investigates the use of subsets in combination with a relaxation parameter for 3-D list-mode EM reconstructions. Results show that two iterations through the list-mode data are sufficient to yield good quality reconstructions. Furthermore, if counting statistics are good, just one iteration may prove sufficient, opening the way for real-time iterative reconstruction.