A globally convergent regularized ordered-subset algorithm for list-mode reconstruction

List-mode (LM) acquisition allows collection of data attributes at higher levels of precision than is possible with binned (i.e. histogram-mode) data. Hence it is particularly attractive for low-count data in emission tomography. A LM likelihood and convergent EM algorithm for LM reconstruction was presented in (Parra et al., TMI, v17, 1998). Faster ordered subset (OS) reconstruction algorithms for LM 3-D PET were presented in (Reader et al., Phys. Med. Bio., v43, 1998). However, these OS algorithms are not globally convergent and they also do not include regularization using convex priors which can be beneficial in emission tomographic reconstruction. LM-OSEM algorithms incorporating regularization via inter-iteration filtering were presented in (Levkovilz et al., TMi, v20, 2001), but these are again not globally convergent. Convergent preconditioned conjugate gradient algorithms for spatio-temporal list-mode reconstruction incorporating regularization were presented in (NichoLs, et al., TMI, v21, 2002), but these do not use OS for speed-up. In this work, we present a globally convergent and regularized ordered-subset algorithm for LM reconstruction. Our algorithm is derived using an incremental EM approach. We demonstrate the speed-up of our LM OS algorithm (vs. a non-OS version) for a SPECT simulation.