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.
Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction
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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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Fast PET Reconstruction with Variance Reduction and Prior-Aware Preconditioning
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.