Display options
Cite
Waterschoot R, D'Asseler Y, Goethals I. Comparison of an in-house acquired brain F-18 FDG PET normal database with commercially available normal data. Nucl Med Commun. 2021;42(9):1039-1044doi: 10.1097/MNM.0000000000001427.
Waterschoot, R., D'Asseler, Y., & Goethals, I. (2021). Comparison of an in-house acquired brain F-18 FDG PET normal database with commercially available normal data. Nuclear medicine communications, 42(9), 1039-1044. https://doi.org/10.1097/MNM.0000000000001427
Waterschoot, Robbe, et al. "Comparison of an in-house acquired brain F-18 FDG PET normal database with commercially available normal data." Nuclear medicine communications vol. 42,9 (2021): 1039-1044. doi: https://doi.org/10.1097/MNM.0000000000001427
Waterschoot R, D'Asseler Y, Goethals I. Comparison of an in-house acquired brain F-18 FDG PET normal database with commercially available normal data. Nucl Med Commun. 2021 Sep 01;42(9):1039-1044. doi: 10.1097/MNM.0000000000001427. PMID: 33867483.
Copy Download .nbib Format: NLM
Share it on

Nucl Med Commun. 2021 Sep 01;42(9):1039-1044. doi: 10.1097/MNM.0000000000001427.

Comparison of an in-house acquired brain F-18 FDG PET normal database with commercially available normal data.

Nuclear medicine communications

Robbe Waterschoot, Yves D'Asseler, Ingeborg Goethals

Affiliations

  1. Department of Nuclear Medicine, Ghent University Hospital, Gent, Belgium.

PMID: 33867483 DOI: 10.1097/MNM.0000000000001427

Abstract

INTRODUCTION: Current guidelines recommend the use of semiautomated assessment of F-18 FDG PET brain studies. Accuracy is influenced by the normal data, which requires knowledge of the included subjects and how they were acquired. Due to confidentiality, such information is often not completely disclosed. Our aim was to determine the variation in FDG uptake between several commercially available and our in-house normal database.

METHODS: Our database contains 83 healthy subjects. Outlier detection using SPM further ensured normality, resulting in exclusion of three subjects. The remaining 80 subjects were analyzed using three commercially available software packages. Z-score data per patient and per lobe were extracted and pooled in predefined age groups (18-40, 41-60 and 61-80 years old) with a calculation of mean Z-scores and SD. Correlation between Z-score output of different software was investigated.

RESULTS: In the 18-40 years age group, frontotemporal hypermetabolism was found with all software. Decreased cerebellar uptake was found with two software packages. Mean Z-scores are closer to zero in the 41-60 years age group compared to the younger group, and mostly within the normal range in the 61-80 years age group with all software. A moderate to high linear correlation between Z-score output was found, but individual Z-scores varied widely.

CONCLUSIONS: The three software packages yielded varying Z-score output, partially explained by an age mismatch between our subjects and subjects in their normal databases. A definitive explanation for the remaining differences is lacking. This emphasizes the importance of age-matched normal data and knowledge of the included databases to allow adequate preprocessing.

Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

References

  1. Varrone A, Asenbaum S, Vander Borght T, Booij J, Nobili F, Någren K, et al.; European Association of Nuclear Medicine Neuroimaging Committee. EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. Eur J Nucl Med Mol Imaging 2009; 36:2103–2110. - PubMed
  2. Nobili F, Arbizu J, Bouwman F, Drzezga A, Agosta F, Nestor P, et al.; EANM-EAN Task Force for the Prescription of FDG-PET for Dementing Neurodegenerative Disorders. European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus. Eur J Neurol 2018; 25:1201–1217. - PubMed
  3. Waterschoot R, Dobbeleir A, D’Asseler Y, Troch M, Lahoutte T, Goethals I. Brain 18F-FDG PET ‘normal database’: what’s in a name? Comparing an in-house acquired normal database with commercially and freely available normal data. Tijdschrift voor Nucleaire Geneeskunde 2015; 37:1456. - PubMed
  4. Gallivanone F, Della Rosa PA, Perani D, Gilardi MC, Castiglioni I; EADC-PET Consortium. The impact of different 18FDG PET healthy subject scans for comparison with single patient in SPM analysis. Q J Nucl Med Mol Imaging 2017; 61:115–132. - PubMed
  5. Buchholz H-G, Manthey T, Meckbach S, Schreckenberger M. Impact of different spatial normalization procedures on single subject testing in FDG brain PET: comparison between Hermes BRASS and statistical parametric mapping. J Nucl Med 2018; 59 (Suppl 1):1724–1724. - PubMed
  6. Lindström E, Oddstig J, Danfors T, Jögi J, Hansson O, Lubberink M. Image reconstruction methods affect software-aided assessment of pathologies of [18F]flutemetamol and [18F]FDG brain-PET examinations in patients with neurodegenerative diseases. Neuroimage Clin 2020; 28:102386. - PubMed
  7. Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I, et al.; EADC-PET Consortium. A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics 2014; 12:575–593. - PubMed
  8. Loessner A, Alavi A, Lewandrowski KU, Mozley D, Souder E, Gur RE. Regional cerebral function determined by FDG-PET in healthy volunteers: normal patterns and changes with age. J Nucl Med 1995; 36:1141–1149. - PubMed
  9. Dukart J, Mueller K, Horstmann A, Vogt B, Frisch S, Barthel H, et al. Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies. Neuroimage 2010; 49:1490–1495. - PubMed
  10. Chen WP, Samuraki M, Shima K, Yanase D, Takeda N, Miyazaki Y, et al. Effect of an age-mismatched and sex-mismatched normal database on the diagnostic performance of ¹8F-FDG PET for Alzheimer’s disease: the Ishikawa Brain Imaging Study. Nucl Med Commun 2011; 32:1128–1133. - PubMed
  11. Chen WP, Samuraki M, Yanase D, Shima K, Takeda N, Ono K, et al. Effect of sample size for normal database on diagnostic performance of brain FDG PET for the detection of Alzheimer’s disease using automated image analysis. Nucl Med Commun 2008; 29:270–276. - PubMed
  12. Presotto L, Ballarini T, Caminiti SP, Bettinardi V, Gianolli L, Perani D. Validation of 18F-FDG-PET single-subject optimized SPM procedure with different PET scanners. Neuroinformatics 2017; 15:151–163. - PubMed
  13. Montandon ML, Slosman DO, Zaidi H. Assessment of the impact of model-based scatter correction on [18F]-FDG 3D brain PET in healthy subjects using statistical parametric mapping. Neuroimage 2003; 20:1848–1856. - PubMed
  14. Thiele F, Young S, Buchert R, Wenzel F. Voxel-based classification of FDG PET in dementia using inter-scanner normalization. Neuroimage 2013; 77:62–69. - PubMed
  15. Van Laere K, Koole M, Versijpt J, Vandenberghe S, Brans B, D’Asseler Y, et al. Transfer of normal 99mTc-ECD brain SPET databases between different gamma cameras. Eur J Nucl Med 2001; 28:435–449. - PubMed
  16. Vunckx K, Dupont P, Goffin K, Van Paesschen W, Van Laere K, Nuyts J. Voxel-based comparison of state-of-the-art reconstruction algorithms for 18F-FDG PET brain imaging using simulated and clinical data. Neuroimage 2014; 102 (Pt 2):875–884. - PubMed

Publication Types