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arXiv · 2406.15159

Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction

Evangelos Papoutsellis·Casper da Costa-Luis·Daniel Deidda·Claire Delplancke·Margaret Duff·Gemma Fardell·Ashley Gillman·Jakob S. Jørgensen·Zeljko Kereta·Evgueni Ovtchinnikov·Edoardo Pasca·Georg Schramm·Kris Thielemans
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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.

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Evangelos Papoutsellis, Casper da Costa-Luis, Daniel Deidda, Claire Delplancke, Margaret Duff, Gemma Fardell, Ashley Gillman, Jakob S. Jørgensen, Zeljko Kereta, Evgueni Ovtchinnikov, Edoardo Pasca, Georg Schramm, Kris Thielemans. 2024-06-21. Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction. https://arxiv.org/abs/2406.15159

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