Abstract
Positron Emission Tomography (PET) image reconstruction presents challenges related to the large scale of data to be processed, which affects reconstruction speed, and the need to include regularizers to improve image quality. Among the methods proposed to overcome these challenges, the recently introduced Stochastic Primal Dual Hybrid Gradient (SPDHG) algorithm combines the ability to deal with regularizers like Total Variation and to process large datasets by random subsampling. We present two contributions regarding the step-sizes of SPDHG: i) larger step-sizes facilitated by a new formula, and ii) a numerical method to calibrate, in the context of PET reconstruction, the tradeoff between primal and dual progression, which is common to all primal-dual algorithms. We validate improvements in speed reconstruction on real PET data from the Siemens Biograph mMR.
Original language | English |
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Title of host publication | 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference |
Publisher | IEEE |
ISBN (Electronic) | 9781728176932 |
DOIs | |
Publication status | Published - 12 Aug 2021 |
Event | 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020 - Boston, USA United States Duration: 31 Oct 2020 → 7 Nov 2020 |
Publication series
Name | 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020 |
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ISSN (Print) | 1082-3654 |
ISSN (Electronic) | 2577-0829 |
Conference
Conference | 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020 |
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Country/Territory | USA United States |
City | Boston |
Period | 31/10/20 → 7/11/20 |
Bibliographical note
Funding Information:Manuscript received December 19, 2020. This work was funded by the UK EPSRC grant PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation EP/S026045/1.
Publisher Copyright:
© 2020 IEEE
Funding
Manuscript received December 19, 2020. This work was funded by the UK EPSRC grant PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation EP/S026045/1.
ASJC Scopus subject areas
- Signal Processing
- Radiology Nuclear Medicine and imaging
- Nuclear and High Energy Physics