TechRxiv
Main Body.pdf (1.74 MB)
Download file

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

Download (1.74 MB)
preprint
posted on 07.04.2022, 03:56 authored by Robert TwymanRobert Twyman, Simon ArridgeSimon Arridge, Zeljko KeretaZeljko Kereta, Bangti JinBangti Jin, Ludovica Brusaferri, Sangtae Ahn, Charles Stearns, Brian Hutton, Irene A. Burger, Fotis Kotasidis, Kris ThielemansKris Thielemans
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.

Funding

GE Healthcare

EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging In Healthcare (i4health)

Engineering and Physical Sciences Research Council

Find out more...

Stochastic iterative regularization: theory, algorithms and applications

Engineering and Physical Sciences Research Council

Find out more...

CCP in Synergistic Reconstruction for Biomedical Imaging

Engineering and Physical Sciences Research Council

Find out more...

NIHR UCLH BRC

National Institute for Health Research

Find out more...

History

Email Address of Submitting Author

robert.twyman.18@ucl.ac.uk

ORCID of Submitting Author

0000-0001-8871-9586

Submitting Author's Institution

University College London

Submitting Author's Country

United Kingdom