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Cited by 107Medical Imaging Techniques and ApplicationsCardiac Imaging and DiagnosticsLung Cancer Diagnosis and Treatment

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Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients

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Quak, Elske, Le Roux, Pierre-Yves, Hofman, Michael S., Robin, Philippe, Bourhis, David, Callahan, Jason, Binns, David, Desmonts, Cédric, Salaun, Pierre-Yves, Hicks, Rodney J., Aide, Nicolas·2015·European Journal of Nuclear Medicine and Molecular Imaging
DOI 10.1007/s00259-015-3128-0PMC PMC4623085
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Abstract

PURPOSE: Point-spread function (PSF) or PSF + time-of-flight (TOF) reconstruction may improve lesion detection in oncologic PET, but can alter quantitation resulting in variable standardized uptake values (SUVs) between different PET systems. This study aims to validate a proprietary software tool (EQ.PET) to harmonize SUVs across different PET systems independent of the reconstruction algorithm used. METHODS: NEMA NU2 phantom data were used to calculate the appropriate filter for each PSF or PSF+TOF reconstruction from three different PET systems, in order to obtain EANM compliant recovery coefficients. PET data from 517 oncology patients were reconstructed with a PSF or PSF+TOF reconstruction for optimal tumour detection and an ordered subset expectation maximization (OSEM3D) reconstruction known to fulfil EANM guidelines. Post-reconstruction, the proprietary filter was applied to the PSF or PSF+TOF data (PSFEQ or PSF+TOFEQ). SUVs for PSF or PSF+TOF and PSFEQ or PSF+TOFEQ were compared to SUVs for the OSEM3D reconstruction. The impact of potential confounders on the EQ.PET methodology including lesion and patient characteristics was studied, as was the adherence to imaging guidelines. RESULTS: For the 1380 tumour lesions studied, Bland-Altman analysis showed a mean ratio between PSF or PSF+TOF and OSEM3D of 1.46 (95%CI: 0.86-2.06) and 1.23 (95%CI: 0.95-1.51) for SUVmax and SUVpeak, respectively. Application of the proprietary filter improved these ratios to 1.02 (95%CI: 0.88-1.16) and 1.04 (95%CI: 0.92-1.17) for SUVmax and SUVpeak, respectively. The influence of the different confounding factors studied (lesion size, location, radial offset and patient's BMI) was less than 5%. Adherence to the European Association of Nuclear Medicine (EANM) guidelines for tumour imaging was good. CONCLUSION: These data indicate that it is not necessary to sacrifice the superior lesion detection and image quality achieved by newer reconstruction techniques in the quest for harmonizing quantitative comparability between PET systems.

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Fig. 1 Recovery coefficients extracted from NEMA NU2 phantom acquisition for maximum values for (a) PSF or PSF+TOF and OSEM3D reconstructions, and (b) for PSFEQ or PSF+TOFEQ and OSEM3D
Fig. 1 Recovery coefficients extracted from NEMA NU2 phantom acquisition for maximum values for (a) PSF or PSF+TOF and OSEM3D reconstructions, and (b) for PSFEQ or PSF+TOFEQ and OSEM3DCC BY · PubMed Central
Fig. 2 Representative images of OSEM (a, b, c) and PSF or PSF+TOF (d, e, f) reconstructions for the three participating centres. All images have been scaled on the same maximum value (SUV = 5). All selected patients are patients with liver metastases from colorectal cancer. Note the increase in apparent tracer uptake in PSF or PSF+TOF images as compared to a conventional OSEM algorithm fulfilling the EANM requirements. This increase was particularly present in the PSF or PSF+TOF images without filter or when applying a Gaussian filter with a small kernel (centres 1 and 2)
Fig. 2 Representative images of OSEM (a, b, c) and PSF or PSF+TOF (d, e, f) reconstructions for the three participating centres. All images have been scaled on the same maximum value (SUV = 5). All selected patients are patients with liver metastases from colorectal cancer. Note the increase in apparent tracer uptake in PSF or PSF+TOF images as compared to a conventional OSEM algorithm fulfilling the EANM requirements. This increase was particularly present in the PSF or PSF+TOF images without filter or when applying a Gaussian filter with a small kernel (centres 1 and 2)CC BY · PubMed Central
Fig. 3 Relationship between quantitative values extracted from PSF/PSF+TOF or PSFEQ/PSF+TOFEQ and OSEM images, assessed using Bland-Altman plots for SUVmax (a) and SUVpeak (b) in tumour lesions
Fig. 3 Relationship between quantitative values extracted from PSF/PSF+TOF or PSFEQ/PSF+TOFEQ and OSEM images, assessed using Bland-Altman plots for SUVmax (a) and SUVpeak (b) in tumour lesionsCC BY · PubMed Central
Fig. 4 Relationship between quantitative values extracted from PSF/PSF+TOF or PSFEQ/PSF+TOFEQ and OSEM images, assessed using Bland-Altman plots for SUVmax, (a) SUVpeak (b) and SUVmean in the liver background (c)
Fig. 4 Relationship between quantitative values extracted from PSF/PSF+TOF or PSFEQ/PSF+TOFEQ and OSEM images, assessed using Bland-Altman plots for SUVmax, (a) SUVpeak (b) and SUVmean in the liver background (c)CC BY · PubMed Central
Fig. 5 Relative impact of the PET technology on quantitative values. Mean (SD) ratio of SUVmax (a) and SUVpeak (b) obtained with a conventional OSEM algorithm and those obtained with PSF or PSF+TOF reconstructions are shown before and after application of the EQ technology. Data are shown for the three participating centres. Reconstruction settings and EQ filter values are detailed in Table 1. Note the difference in ratio between PSF and OSEM data in centres 1 and 3 using the same PET system (PSF reconstruction), either with no post filtering (centre 1) or with a Gaussian filter (kernel ranging from 4 to 6 mm, centre 3)
Fig. 5 Relative impact of the PET technology on quantitative values. Mean (SD) ratio of SUVmax (a) and SUVpeak (b) obtained with a conventional OSEM algorithm and those obtained with PSF or PSF+TOF reconstructions are shown before and after application of the EQ technology. Data are shown for the three participating centres. Reconstruction settings and EQ filter values are detailed in Table 1. Note the difference in ratio between PSF and OSEM data in centres 1 and 3 using the same PET system (PSF reconstruction), either with no post filtering (centre 1) or with a Gaussian filter (kernel ranging from 4 to 6 mm, centre 3)CC BY · PubMed Central
Fig. 6 Impact of the size of the lesion (a), the location of the lesion across the field of view [radial offset (b)], the anatomical site of the lesion (c), and patient BMI (d) on the ratio between PSFEQ/PSF+TOFEQ and OSEM PET quantitative values (left panels SUVmax, right panels SUVpeak). Note that 213 lesions were not measurable and therefore not included in the “per size” analysis. The differences among different groups were tested with the Kruskal-Wallis test, and a post hoc test was performed with the Dunn test for multiple comparisons; *, **, and *** indicate two-tailed p < 0.05, p < 0.01, and p < 0.001, respectively
Fig. 6 Impact of the size of the lesion (a), the location of the lesion across the field of view [radial offset (b)], the anatomical site of the lesion (c), and patient BMI (d) on the ratio between PSFEQ/PSF+TOFEQ and OSEM PET quantitative values (left panels SUVmax, right panels SUVpeak). Note that 213 lesions were not measurable and therefore not included in the “per size” analysis. The differences among different groups were tested with the Kruskal-Wallis test, and a post hoc test was performed with the Dunn test for multiple comparisons; *, **, and *** indicate two-tailed p < 0.05, p < 0.01, and p < 0.001, respectivelyCC BY · PubMed Central

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Metadata

Authors
Quak, E., Le Roux, P.-Y., Hofman, M. S., Robin, P., Bourhis, D., Callahan, J., Binns, D., Desmonts, C., Salaun, P.-Y., Hicks, R. J., & Aide, N.
Affiliations
  • Centre François Baclesse (FR)
  • The University of Melbourne (AU)
  • Centre Hospitalier Régional Universitaire de Brest (FR)
  • Centre Hospitalier Universitaire de Caen Normandie (FR)
  • Inserm (FR)
Corresponding author
Aide, Nicolas
ORCID
Venue
European Journal of Nuclear Medicine and Molecular Imaging
Publisher
Springer Science and Business Media LLC
Published
2015-07-30
Volume
42
Issue
13
Pages
2072-2082
ISSN
  • 1619-7070 (ISSN-L)
  • 1619-7089
DOI
10.1007/s00259-015-3128-0
PMC
PMC4623085
OpenAlex
W941202616
Cited by
107
References
33
Citations by year
2026: 2 · 2025: 4 · 2024: 7 · 2023: 6 · 2022: 12 · 2021: 16 · 2020: 8 · 2019: 9 · 2018: 12 · 2017: 16 · 2016: 11 · 2015: 4
FWCI
11.62
Type
journal article
Language
en
Open access
Licence
Locations
  • European Journal of Nuclear Medicine and Molecular Imaging (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
Keywords
point spread function reconstruction, time-of-flight PET, SUVmax, SUVpeak, EANM guidelines, standardized uptake value, multicentre validation
MeSH terms
Aged, Carcinoma, Non-Small-Cell Lung, Female, Humans, Image Processing, Computer-Assisted, Liver Neoplasms, Lymphoma, Non-Hodgkin, Male, Melanoma, Middle Aged, Radiopharmaceuticals, Fluorodeoxyglucose F18, Positron-Emission Tomography

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