An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction-Reference-Cited by-同舟云学术

An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction

Published:2023-01 Issue:1 Volume:42 Page:29-41
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Twyman Robert¹^ORCID,Arridge Simon²^ORCID,Kereta Zeljko²,Jin Bangti²^ORCID,Brusaferri Ludovica¹,Ahn Sangtae³^ORCID,Stearns Charles W.⁴,Hutton Brian F.¹^ORCID,Burger Irene A.⁵^ORCID,Kotasidis Fotis⁴,Thielemans Kris¹^ORCID

Affiliation:

1. Institute of Nuclear Medicine, UCL, London, U.K.

2. Department of Computer Science, UCL, London, U.K.

3. GE Research, Niskayuna, NY, USA

4. GE Healthcare, Waukesha, WI, USA

5. Nuclear Medicine Department, University Hospital Zurich, University of Zurich, Zürich, Switzerland

Funder

General Electric (GE) Healthcare

National Institute for Health and Care Research (NIHR) University College London Hospital (UCLH) Biomedical Research Centre

Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training i4Health

EPSRC Project

CCP SyneRBI through EPSRC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10003062/09872020.pdf?arnumber=9872020

Reference56 articles.

1. Accelerating stochastic gradient descent using predictive variance reduction;johnson;Proc Adv Neural Inf Process Syst,2013

2. Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET

3. Accelerated convergent motion compensated image reconstruction;delplancke;Proc IEEE Nucl Sci Symp Med Imag Conf (NSS/MIC),2021

4. A stochastic gradient method with an exponential convergence rate for finite training sets;roux;NIPS,2012

5. SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives;defazio;Proc Adv Neural Inf Process Syst,2014

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