Effect of a Bayesian penalized likelihood PET reconstruction compared with ordered subset expectation maximization on clinical image quality over a wide range of patient weights

Chilcott, Anna K., Bradley, Kevin M. ORCID: https://orcid.org/0000-0003-1911-3382 and McGowan, Daniel R. 2018. Effect of a Bayesian penalized likelihood PET reconstruction compared with ordered subset expectation maximization on clinical image quality over a wide range of patient weights. American Journal of Roentgenology 210 (1) , pp. 152-157. 10.2214/AJR.17.18060

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Abstract

OBJECTIVE. A study was performed to compare background liver signal-to-noise ratio (SNR) and visually assessed image quality of clinical PET/CT studies from the same PET acquisition data reconstructed by Bayesian penalized likelihood (BPL) and ordered subset expectation maximization (OSEM) over a range of patient weights. MATERIALS AND METHODS. The effect of a BPL PET reconstruction algorithm on liver SNR and visually assessed image quality over a range of patient weights (41–196 kg; n = 108) was retrospectively compared with standard-of-care OSEM reconstruction on the same PET acquisition data after IV administration of 18F-FDG (4 MBq/kg). RESULTS. BPL showed no significant change (p > 0.05) in liver SNR with increasing weight and body mass index (BMI), whereas OSEM showed increasing noise with increasing weight and BMI. The liver SNR was significantly higher using BPL than a standard OSEM reconstruction (p < 0.0002 for all BMI groups). Visually assessed image quality declined at a greater rate with increasing weight and BMI in the OSEM images than with BPL images. CONCLUSION. BPL provides a more consistent visually assessed image quality and liver background SNR than does OSEM, with the greatest benefit for the heaviest patients.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Publisher:	American Roentgen Ray Society (ARRS)
ISSN:	0361-803X
Date of Acceptance:	12 July 2017
Last Modified:	07 Nov 2022 10:21
URI:	https://orca.cardiff.ac.uk/id/eprint/132000

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