Literature DB >> 29091008

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.

Anna K Chilcott1,2, Kevin M Bradley3, Daniel R McGowan1,4.   

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.

Entities:  

Keywords:  Bayesian penalized likelihood; FDG; PET/CT; ordered subset expectation maximization; signal-to-noise ratio

Mesh:

Substances:

Year:  2017        PMID: 29091008     DOI: 10.2214/AJR.17.18060

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  8 in total

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3.  Optimising quantitative 90Y PET imaging: an investigation into the effects of scan length and Bayesian penalised likelihood reconstruction.

Authors:  Nathaniel P Scott; Daniel R McGowan
Journal:  EJNMMI Res       Date:  2019-05-10       Impact factor: 3.138

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Journal:  EJNMMI Phys       Date:  2022-03-03

5.  Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.

Authors:  Abolfazl Mehranian; Scott D Wollenweber; Matthew D Walker; Kevin M Bradley; Patrick A Fielding; Martin Huellner; Fotis Kotasidis; Kuan-Hao Su; Robert Johnsen; Floris P Jansen; Daniel R McGowan
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-05-04       Impact factor: 10.057

6.  Impact of the Noise Penalty Factor on Quantification in Bayesian Penalized Likelihood (Q.Clear) Reconstructions of 68Ga-PSMA PET/CT Scans.

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7.  New PET technologies - embracing progress and pushing the limits.

Authors:  Nicolas Aide; Charline Lasnon; Adam Kesner; Craig S Levin; Irene Buvat; Andrei Iagaru; Ken Hermann; Ramsey D Badawi; Simon R Cherry; Kevin M Bradley; Daniel R McGowan
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-06-03       Impact factor: 9.236

8.  Comparison of Regularized Reconstruction and Ordered Subset Expectation Maximization Reconstruction in the Diagnostics of Prostate Cancer Using Digital Time-of-Flight 68Ga-PSMA-11 PET/CT Imaging.

Authors:  Olof Jonmarker; Rimma Axelsson; Ted Nilsson; Stefan Gabrielson
Journal:  Diagnostics (Basel)       Date:  2021-03-31
  8 in total

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