Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction
<|reference_start|>Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction: We introduce a stochastic framework into the open--source Core Imaging Library (CIL) which enables easy development of stochastic algorithms. Five such algorithms from the literature are developed, Stochastic Gradient Descent, Stochastic Average Gradient (-Am\'elior\'e), (Loopless) Stochastic Variance Reduced Gradient. We showcase the functionality of the framework with a comparative study against a deterministic algorithm on a simulated 2D PET dataset, with the use of the open-source Synergistic Image Reconstruction Framework. We observe that stochastic optimisation methods can converge in fewer passes of the data than a standard deterministic algorithm.<|reference_end|>
@article{papoutsellis2024stochastic,
    title={Stochastic Optimisation Framework using the Core Imaging Library and
  Synergistic Image Reconstruction Framework for PET Reconstruction},
    author={Evangelos Papoutsellis, Casper da Costa-Luis, Daniel Deidda, Claire
  Delplancke, Margaret Duff, Gemma Fardell, Ashley Gillman, Jakob S.
  J{o}rgensen, Zeljko Kereta, Evgueni Ovtchinnikov, Edoardo Pasca, Georg
  Schramm, Kris Thielemans},
    journal={arXiv preprint arXiv:2406.15159},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15159},
    primaryClass={math.NA cs.CV cs.NA eess.IV math.OC}
}