Literature DB >> 25467224

Is the standard uptake value (SUV) appropriate for quantification in clinical PET imaging? - Variability induced by different SUV measurements and varying reconstruction methods.

Cornelia Brendle1, Jürgen Kupferschläger2, Konstantin Nikolaou3, Christian la Fougère4, Sergios Gatidis5, Christina Pfannenberg6.   

Abstract

INTRODUCTION: PET quantification using the standard uptake value (SUV) is very prone to variations by technical factors of the scanner system and patient specific characteristics. Aim of the study was to investigate the reproducibility of SUV values between different measures and different reconstruction algorithms in a PET/CT scanner of the newest generation.
METHODS: The time-of-flight PET datasets of 27 consecutive oncological patients were reconstructed with OSEM3D in two different matrix sizes (200 × 200 and 400 × 400) as well as in a matrix size of 400 × 400 and additional point-spread-reconstruction. The standardized uptake values SUVmax, SUVmean and SUVpeak in 60 lesions were compared concerning their variability in the three reconstructions.
RESULTS: The addition of point-spread-reconstruction causes a significant increase of SUV values in comparison to OSEM 3D. SUVpeak showed the highest reproducibility between the different reconstruction algorithms. The variability of SUVmax and SUVmean increases in small lesions <5 ml, while SUVpeak remains more stable.
CONCLUSION: SUVmax, SUVmean and SUVpeak can be used for PET quantification in principle. However, quantification of small lesions is difficult. SUVpeak is the most robust method when using varying reconstruction methods, especially in small lesions.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  PET quantification; PET/CT; Point spread function; ROI; SUV

Mesh:

Substances:

Year:  2014        PMID: 25467224     DOI: 10.1016/j.ejrad.2014.10.018

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  14 in total

1.  qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68Ga-PSMA11 PET/CT.

Authors:  Andrei Gafita; Marie Bieth; Markus Krönke; Giles Tetteh; Fernando Navarro; Hui Wang; Elisabeth Günther; Bjoern Menze; Wolfgang A Weber; Matthias Eiber
Journal:  J Nucl Med       Date:  2019-03-08       Impact factor: 10.057

2.  Comparison of 68Ga-labelled PSMA-11 and 11C-choline in the detection of prostate cancer metastases by PET/CT.

Authors:  Johannes Schwenck; Hansjoerg Rempp; Gerald Reischl; Stephan Kruck; Arnulf Stenzl; Konstantin Nikolaou; Christina Pfannenberg; Christian la Fougère
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-08-24       Impact factor: 9.236

3.  Impact of PET/CT image reconstruction methods and liver uptake normalization strategies on quantitative image analysis.

Authors:  Georg Kuhnert; Ronald Boellaard; Sergej Sterzer; Deniz Kahraman; Matthias Scheffler; Jürgen Wolf; Markus Dietlein; Alexander Drzezga; Carsten Kobe
Journal:  Eur J Nucl Med Mol Imaging       Date:  2015-08-18       Impact factor: 9.236

4.  Application of integrated positron emission tomography/magnetic resonance imaging in evaluating the prognostic factors of head and neck squamous cell carcinoma with positron emission tomography, diffusion-weighted imaging, dynamic contrast enhancement and combined model.

Authors:  Haodan Dang; Yu Chen; Zhuhua Zhang; Xiaohua Shi; Xingming Chen; Xiaoli Zhu; Bo Hou; Haiqun Xing; Huadan Xue; Zhengyu Jin
Journal:  Dentomaxillofac Radiol       Date:  2020-04-03       Impact factor: 2.419

5.  Impact of Time-of-Flight and Point-Spread-Function for Respiratory Artifact Reduction in PET/CT Imaging: Focus on Standardized Uptake Value.

Authors:  Roya Sharifpour; Pardis Ghafarian; Mehrdad Bakhshayesh-Karam; Hamidreza Jamaati; Mohammad Reza Ay
Journal:  Tanaffos       Date:  2017

6.  Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks.

Authors:  Karim Armanious; Tobias Hepp; Thomas Küstner; Helmut Dittmann; Konstantin Nikolaou; Christian La Fougère; Bin Yang; Sergios Gatidis
Journal:  EJNMMI Res       Date:  2020-05-24       Impact factor: 3.138

7.  Impact of PET reconstruction protocols on quantification of lesions that fulfil the PERCIST lesion inclusion criteria.

Authors:  Joke Devriese; Laurence Beels; Alex Maes; Christophe Van de Wiele; Hans Pottel
Journal:  EJNMMI Phys       Date:  2018-12-07

8.  Differences among [18F]FDG PET-derived parameters in lung cancer produced by three software packages.

Authors:  Agnieszka Bos-Liedke; Paulina Cegla; Krzysztof Matuszewski; Ewelina Konstanty; Adam Piotrowski; Magdalena Gross; Julian Malicki; Maciej Kozak
Journal:  Sci Rep       Date:  2021-07-06       Impact factor: 4.379

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

Authors:  Elske Quak; Pierre-Yves Le Roux; Michael S Hofman; Philippe Robin; David Bourhis; Jason Callahan; David Binns; Cédric Desmonts; Pierre-Yves Salaun; Rodney J Hicks; Nicolas Aide
Journal:  Eur J Nucl Med Mol Imaging       Date:  2015-07-30       Impact factor: 9.236

10.  Timing of fluorodeoxyglucose positron emission tomography maximum standardized uptake value for diagnosis of local recurrence of non-small cell lung cancer after stereotactic body radiation therapy.

Authors:  Daren Tan; Suki Gill; Nelson Loh
Journal:  Cancer Med       Date:  2020-08-26       Impact factor: 4.452

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