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

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arxiv 2406.15159 v1 pith:XUT2U5TP submitted 2024-06-21 math.NA cs.CVcs.NAeess.IVmath.OC

Evangelos Papoutsellis , Casper da Costa-Luis , Daniel Deidda , Claire Delplancke , Margaret Duff , Gemma Fardell , Ashley Gillman , Jakob S. J{o}rgensen
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Zeljko Kereta Evgueni Ovtchinnikov Edoardo Pasca Georg Schramm Kris Thielemans
This is my paper · ORCID
classification math.NAcs.CVcs.NAeess.IVmath.OC
keywords stochasticframeworkgradientreconstructionalgorithmalgorithmscoredeterministic
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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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  1. Fast PET Reconstruction with Variance Reduction and Prior-Aware Preconditioning

    physics.med-ph 2025-06 conditional novelty 5.0 of 10

    A harmonic-mean preconditioner that combines data-fidelity and prior Hessian information makes subset-based PET reconstruction with the relative difference prior converge faster and more reliably.

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