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) Stochas...

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Bibliographic Details
Main Authors Papoutsellis, Evangelos, da Costa-Luis, Casper, Deidda, Daniel, Delplancke, Claire, Duff, Margaret, Fardell, Gemma, Gillman, Ashley, Jørgensen, Jakob S, Kereta, Zeljko, Ovtchinnikov, Evgueni, Pasca, Edoardo, Schramm, Georg, Thielemans, Kris
Format Journal Article
LanguageEnglish
Published 21.06.2024
Subjects
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Numerical Analysis
Mathematics - Numerical Analysis
Mathematics - Optimization and Control
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Summary: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.
DOI:10.48550/arxiv.2406.15159