Evidence map›Paper›PMID 40951349›Full record

ArticleFrontiers in oncology2025

Comparative evaluation of Ordered Subset Expectation Maximization and Bayesian Penalized Likelihood algorithms for PET/CT image reconstruction in various malignancies using

Arvin Naeimi, Sara Harsini, Ramin Akbarian Aghdam, Ran Klein, Farzad Abbaspour

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Baseline PSMA PET/CT Biomarkers for Patient Selection in [Pharmaceuticals (Basel, Switzerland) · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Arvin NaeimiDepartment of Radiology, McGill University Health Center, McGill University, Montreal, QC, Canada.
Sara HarsiniDepartment of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Ramin Akbarian AghdamDivision of Nuclear Medicine, Department of Medicine, The Ottawa Hospital and University of Ottawa, Ottawa, ON, Canada.
Ran KleinDivision of Nuclear Medicine, Department of Medicine, The Ottawa Hospital and University of Ottawa, Ottawa, ON, Canada.
Farzad AbbaspourDivision of Nuclear Medicine, Department of Medicine, The Ottawa Hospital and University of Ottawa, Ottawa, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study compares the Ordered Subset Expectation Maximization (OSEM) and Bayesian Penalized Likelihood (BPL) algorithms for Positron Emission Tomography/Computed Tomography (PET/CT) image reconstruction using Methods: A retrospective analysis was conducted on 33 patients with various malignancies, including 25 undergoing Results: Thirty-three patients (mean age: 67.53 ± 11.78 years) with 100 lesions (80 FDG, 20 PSMA) were analyzed. In the FDG group, significant differences were observed in lesion SUVpeak, liver SUVpeak, SD of liver SUVmean, bladder SUVmean, SD of bladder SUVmean, and TBR, with BPL generally producing higher values except for liver SUVpeak, SD of liver SUVmean, and SD of bladder SUVmean. In the PSMA group, BPL enhanced most metrics except for the SD of liver SUVmean and the SD of bladder SUVmean. While strong linear correlations between BPL and OSEM metrics were observed (Pearson r>0.85 for most parameters), Bland-Altman analysis revealed wide limits of agreement, particularly for TBR in the PSMA group (-13.00 to 23.89), indicating substantial variability in absolute values between methods. Assessment of the relationship between lesion volume and SUVmax differences (BPL-OSEM) revealed a weak, non-significant negative correlation in the total cohort(r = - 0.14, p = 0.16) and in the FDG subgroup(r= - 0.14, p = 0.24). Conclusion: Both reconstruction methods demonstrate clinical utility, with BPL producing statistically higher values for several quantitative metrics, such as SUVmax and TBR, without markedly improving lesion detectability. While strong correlations were observed between BPL and OSEM values, the wide limits of agreement, particularly for TBR in PSMA imaging, suggest these methods may not be directly interchangeable in longitudinal studies. Harmonization strategies may help reduce inter-method variability and improve scan comparability in longitudinal or multicenter settings. Prospective approaches, such as reconstruction-specific reference ranges or scaling factors, could further support harmonization efforts in clinical trials. For longitudinal monitoring, consistent use of the same reconstruction method is recommended to ensure reliable quantification.

Indexed as

Bayesian Penalized LikelihoodBPLimage reconstructionOrdered Subset Expectation MaximizationOSEMPET/CTPositron Emission Tomography-Computed Tomographystandardized uptake value

Identifiers

PMID40951349
PMCPMC12428789

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.