Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction
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arXiv
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| Format: | Preprint |
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2024
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| author | 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 |
| author_facet | 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 |
| contents | 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 (-Amélioré), (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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15159 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction 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 Numerical Analysis Computer Vision and Pattern Recognition Image and Video Processing Optimization and Control 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 (-Amélioré), (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. |
| title | Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction |
| topic | Numerical Analysis Computer Vision and Pattern Recognition Image and Video Processing Optimization and Control |
| url | https://arxiv.org/abs/2406.15159 |