Effects of PET image reconstruction parameters and tumor-to-background uptake ratio on quantification of PET images from PET/MRI and PET/CT systems

Authors

  • Amena Ali Hussain University of Gothenburg
  • Eva Forssell-Aronsson University of Gothenburg
  • Tobias Rosholm Sahlgrenska University Hospital
  • Esmaeil Mehrara Sahlgrenska University Hospital

DOI:

https://doi.org/10.15392/2319-0612.2024.2487

Keywords:

OSEM, BPL, NEMA, Q-clear

Abstract

Introduction: PET/CT and PET/MRI are valuable multimodality imaging techniques for visualizing both functional and anatomical information. The most used PET reconstruction algorithm is Ordered Subset Expectation Maximization (OSEM). In OSEM, the image noise increases with increased number of iterations, and the reconstruction needs to be stopped before complete convergence. The Bayesian penalized likelihood (BPL) algorithm, recently introduced, uses a noise penalty factor (β) to achieve full convergence while controlling noise. This study aims to evaluate how reconstruction algorithms and lesion radioactivity levels affect PET image quality and quantitative accuracy across three different PET systems. Materials and Methods: A NEMA phantom was filled with 18F and scanned by one PET/MRI and two PET/CT systems with sphere-to-background concentration ratio (SBR) of 2:1, 4:1, or 10:1. PET images were reconstructed with OSEM or BPL with TOF. The number of iterations and β-values were varied, while the matrix size, number of subsets, and filter size remained constant. Contrast recovery (CR) and background variability (BV) were measured in images. Results: CR increased with increased sphere size and SBR. CR and BV decreased with increased β for the 10mm sphere. Increased number of iterations in OSEM showed increased BV with limited variation in CR. BPL gave higher CR and lower BV values than OSEM. The optimal reconstruction was BPL with β between 150 and 350, where BPL was available, and OSEM with two iterations and 21 subsets for the PET/CT without BPL. Conclusion: BPL outperforms OSEM, and SBR significantly influences tracer uptake quantification in small lesions. Future studies should explore the clinical implications of these findings on diagnosis, staging, prognosis, and treatment follow-up.

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References

Wahl, R.L., et al., From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors. J Nucl Med, 2009. 50 Suppl 1: p. 122S-50S. DOI: https://doi.org/10.2967/jnumed.108.057307

Ziegler, S., et al., NEMA image quality phantom measurements and attenuation correction in integrated PET/MR hybrid imaging. EJNMMI Phys, 2015. 2(1): p. 18. DOI: https://doi.org/10.1186/s40658-015-0122-3

Kurita, Y., et al., The value of Bayesian penalized likelihood reconstruction for improving lesion conspicuity of malignant lung tumors on (18)F-FDG PET/CT: comparison with ordered subset expectation maximization reconstruction incorporating time-of-flight model and point spread function correction. Ann Nucl Med, 2020. 34(4): p. 272-279. DOI: https://doi.org/10.1007/s12149-020-01446-x

Tsutsui, Y., et al., Edge Artifacts in Point Spread Function-based PET Reconstruction in Relation to Object Size and Reconstruction Parameters. Asia Ocean J Nucl Med Biol, 2017. 5(2): p. 134-143.

Teoh, E.J., et al., Phantom and Clinical Evaluation of the Bayesian Penalized Likelihood Reconstruction Algorithm Q.Clear on an LYSO PET/CT System. J Nucl Med, 2015. 56(9): p. 1447-52. DOI: https://doi.org/10.2967/jnumed.115.159301

Otani, T., et al., Evaluation and Optimization of a New PET Reconstruction Algorithm, Bayesian Penalized Likelihood Reconstruction, for Lung Cancer Assessment According to Lesion Size. AJR Am J Roentgenol, 2019. 213(2): p. W50-W56. DOI: https://doi.org/10.2214/AJR.18.20478

Vallot, D., et al., A clinical evaluation of the impact of the Bayesian penalized likelihood reconstruction algorithm on PET FDG metrics. Nucl Med Commun, 2017. 38(11): p. 979-984. DOI: https://doi.org/10.1097/MNM.0000000000000729

Association, N.E.M., NEMA Standards Publication NU 2-2007: Performance Measurements of Positron Emission Tomographs. 2007: National Electrical Manufacturers Association.

Cherry, S.R., J.A. Sorenson, and M.E. Phelps, Physics in Nuclear Medicine. 4th ed. 2012, Philadelphia: Elsevier/Saunders. DOI: https://doi.org/10.1016/B978-1-4160-5198-5.00001-0

Kosaka, N., et al., 18F-FDG PET of common enhancing malignant brain tumors. AJR Am J Roentgenol, 2008. 190(6): p. W365-9. DOI: https://doi.org/10.2214/AJR.07.2660

Liu, Y., Focal mass-like cardiac uptake on oncologic FDG PET/CT: Real lesion or atypical pattern of physiologic uptake? J Nucl Cardiol, 2019. 26(4): p. 1205-1211. DOI: https://doi.org/10.1007/s12350-018-01524-8

Rijnsdorp, S., M.J. Roef, and A.J. Arends, Impact of the Noise Penalty Factor on Quantification in Bayesian Penalized Likelihood (Q.Clear) Reconstructions of (68)Ga-PSMA PET/CT Scans. Diagnostics (Basel), 2021. 11(5). DOI: https://doi.org/10.3390/diagnostics11050847

Straver, M.E., et al., Feasibility of FDG PET/CT to monitor the response of axillary lymph node metastases to neoadjuvant chemotherapy in breast cancer patients. Eur J Nucl Med Mol Imaging, 2010. 37(6): p. 1069-76. DOI: https://doi.org/10.1007/s00259-009-1343-2

Caribe, P., et al., Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner. EJNMMI Phys, 2019. 6(1): p. 22. DOI: https://doi.org/10.1186/s40658-019-0264-9

Messerli, M., et al., Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors. EJNMMI Phys, 2018. 5(1): p. 27. DOI: https://doi.org/10.1186/s40658-018-0223-x

Drzezga, A., et al., First clinical experience with integrated whole-body PET/MR: comparison to PET/CT in patients with oncologic diagnoses. J Nucl Med, 2012. 53(6): p. 845-55. DOI: https://doi.org/10.2967/jnumed.111.098608

Al-Nabhani, K.Z., et al., Qualitative and quantitative comparison of PET/CT and PET/MR imaging in clinical practice. J Nucl Med, 2014. 55(1): p. 88-94. DOI: https://doi.org/10.2967/jnumed.113.123547

Ishii, S., et al., Comparison of integrated whole-body PET/MR and PET/CT: Is PET/MR alternative to PET/CT in routine clinical oncology? Ann Nucl Med, 2016. 30(3): p. 225-33. DOI: https://doi.org/10.1007/s12149-015-1050-y

Oen, S.K., et al., Image quality and detectability in Siemens Biograph PET/MRI and PET/CT systems-a phantom study. EJNMMI Phys, 2019. 6(1): p. 16. DOI: https://doi.org/10.1186/s40658-019-0251-1

Baun, C., et al., Quantification of FDG-PET/CT with delayed imaging in patients with newly diagnosed recurrent breast cancer. BMC Med Imaging, 2018. 18(1): p. 11. DOI: https://doi.org/10.1186/s12880-018-0254-8

Mayerhoefer, M.E., et al., PET/MRI versus PET/CT in oncology: a prospective single-center study of 330 examinations focusing on implications for patient management and cost considerations. Eur J Nucl Med Mol Imaging, 2020. 47(1): p. 51-60. DOI: https://doi.org/10.1007/s00259-019-04452-y

Lee, S.M., et al., Preoperative staging of non-small cell lung cancer: prospective comparison of PET/MR and PET/CT. Eur Radiol, 2016. 26(11): p. 3850-3857. DOI: https://doi.org/10.1007/s00330-016-4255-0

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Published

2024-09-27

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How to Cite

Effects of PET image reconstruction parameters and tumor-to-background uptake ratio on quantification of PET images from PET/MRI and PET/CT systems. Brazilian Journal of Radiation Sciences, Rio de Janeiro, Brazil, v. 12, n. 3, p. e2487, 2024. DOI: 10.15392/2319-0612.2024.2487. Disponível em: https://bjrs.org.br/revista/index.php/REVISTA/article/view/2487. Acesso em: 22 dec. 2024.

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