Preprint

An Investigation of Stochastic Variance Reduction Algorithms for 3D Penalised PET Image Reconstruction

Robert TwymanInstitute of Nuclear Medicine, UCL, UKSimon ArridgeInstitute of Nuclear Medicine, UCL,Željko KeretaGE Healthcare, Waukesha, WI 53188 USA. IBangti JinInstitute of Nuclear Medicine, UCL,Ludovica BrusaferriMartinos Center for Biomedical Imaging, Harvard Medical School. S. Ahn is with GE Research, Niskayuna, NY 12309 USA. C. W. Stearns and F. Kotasidis are withSangtae AhnInstitute of Nuclear Medicine, UCL,C.W. StearnsMartinos Center for Biomedical Imaging, Harvard Medical School. S. Ahn is with GE Research, Niskayuna, NY 12309 USA. C. W. Stearns and F. Kotasidis are withBrian HuttonGE Healthcare, Waukesha, WI 53188 USA. IIrene A. BurgerInstitute of Nuclear Medicine, UCL, UKFotis A. KotasidisMartinos Center for Biomedical Imaging, Harvard Medical School. S. Ahn is with GE Research, Niskayuna, NY 12309 USA. C. W. Stearns and F. Kotasidis are withKris ThielemansMartinos Center for Biomedical Imaging, Harvard Medical School. S. Ahn is with GE Research, Niskayuna, NY 12309 USA. C. W. Stearns and F. Kotasidis are with

2022en

Ochiq kirishYashil

Litsenziya: cc-by

ABI

Annotatsiya

Application of stochastic variance reduction algorithms to iterative PET reconstruction. We investigated the SAGA and SVRG algorithms for non-TOF PET image reconstruction. Both similated data and a patient data sets were used in the analysis. We found that the stochastic algorithms can improve convergence rate and eliminate behaviour, commonly know as limit cycle behaviour, from PET reconstruction within 5 epochs.

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Mavzular

Medical Imaging Techniques and Applications Radiomics and Machine Learning in Medical Imaging Advanced MRI Techniques and Applications

Identifikatorlar

DOI: 10.36227/techrxiv.19497680

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