Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction
Numerical Analysis
2024-06-24 v1 Computer Vision and Pattern Recognition
Numerical Analysis
Image and Video Processing
Optimization and Control
Abstract
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.
Cite
@article{arxiv.2406.15159,
title = {Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction},
author = {Evangelos Papoutsellis and Casper da Costa-Luis and Daniel Deidda and Claire Delplancke and Margaret Duff and Gemma Fardell and Ashley Gillman and Jakob S. Jørgensen and Zeljko Kereta and Evgueni Ovtchinnikov and Edoardo Pasca and Georg Schramm and Kris Thielemans},
journal= {arXiv preprint arXiv:2406.15159},
year = {2024}
}