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      Technical note: how to determine the FDG activity for tumour PET imaging that satisfies European guidelines

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          Abstract

          Background

          For tumour imaging with PET, the literature proposes to administer a patient-specific FDG activity that depends quadratically on a patient’s body weight. However, a practical approach on how to implement such a protocol in clinical practice is currently lacking. We aimed to provide a practical method to determine a FDG activity formula for whole-body PET examinations that satisfies both the EANM guidelines and this quadratic relation.

          Results

          We have developed a methodology that results in a formula describing the patient-specific FDG activity to administer. A PET study using the NEMA NU-2001 image quality phantom forms the basis of our method. This phantom needs to be filled with 2.0 and 20.0 kBq FDG/mL in the background and spheres, respectively. After a PET acquisition of 10 min, a reconstruction has to be performed that results in sphere recovery coefficients (RCs) that are within the specifications as defined by the EANM Research Ltd (EARL). By performing reconstructions based on shorter scan durations, the minimal scan time per bed position ( T min) needs to be extracted using an image coefficient of variation (COV) of 15 %. At T min, the RCs should be within EARL specifications as well. Finally, the FDG activity (in MBq) to administer can be described by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ A=c \cdot {w}^2\cdot \frac{T_{\min }}{t} $$\end{document} with c a constant that is typically 0.0533 (MBq/kg 2), w the patient’s body weight (in kg), and t the scan time per bed position that is chosen in a clinical setting (in seconds). We successfully demonstrated this methodology using a state-of-the-art PET/CT scanner.

          Conclusions

          We provide a practical method that results in a formula describing the FDG activity to administer to individual patients for whole-body PET examinations, taking into account both the EANM guidelines and a quadratic relation between FDG activity and patient’s body weight. This formula is generally applicable to any PET system, using a specified image reconstruction and scan time per bed position.

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          Most cited references11

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          FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0

          The aim of this guideline is to provide a minimum standard for the acquisition and interpretation of PET and PET/CT scans with [18F]-fluorodeoxyglucose (FDG). This guideline will therefore address general information about [18F]-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) and is provided to help the physician and physicist to assist to carrying out, interpret, and document quantitative FDG PET/CT examinations, but will concentrate on the optimisation of diagnostic quality and quantitative information.
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            Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: a simulation study.

            Semiquantitative standard uptake values (SUVs) are used for tumor diagnosis and response monitoring. However, the accuracy of the SUV and the accuracy of relative change during treatment are not well documented. Therefore, an experimental and simulation study was performed to determine the effects of noise, image resolution, and region-of-interest (ROI) definition on the accuracy of SUVs. Experiments and simulations are based on thorax phantoms with tumors of 10-, 15-, 20-, and 30-mm diameter and background ratios (TBRs) of 2, 4, and 8. For the simulation study, sinograms were generated by forward projection of the phantoms. For each phantom, 50 sinograms were generated at 3 noise levels. All sinograms were reconstructed using ordered-subset expectation maximization (OSEM) with 2 iterations and 16 subsets, with or without a 6-mm gaussian filter. For each tumor, the maximum pixel value and the average of a 50%, a 70%, and an adaptive isocontour threshold ROI were derived as well as with an ROI of 15 x 15 mm. The accuracy of SUVs was assessed using the average of 50 ROI values. Treatment response was simulated by varying the tumor size or the TBR. For all situations, a strong correlation was found between maximum and isocontour-based ROI values resulting in similar dependencies on image resolution and noise of all studied SUV measures. A strong variation with tumor size of > or =50% was found for all SUV values. For nonsmoothed data with high noise levels this variation was primarily due to noise, whereas for smoothed data with low noise levels partial-volume effects were most important. In general, SUVs showed under- and overestimations of > or =50% and depended on all parameters studied. However, SUV ratios, used for response monitoring, were only slightly dependent of ROI definition but were still affected by noise and resolution. The poor accuracy of the SUV under various conditions may hamper its use for diagnosis, especially in multicenter trials. SUV ratios used to measure response to treatment, however, are less dependent on noise, image resolution, and ROI definition. Therefore, the SUV might be more suitable for response-monitoring purposes.
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              Noise considerations for PET quantification using maximum and peak standardized uptake value.

              In tumor response monitoring studies with (18)F-FDG PET, maximum standardized uptake value (SUV(max)) is commonly applied as a quantitative metric. Although it has several advantages due to its simplicity of determination, concerns about the influence of image noise on single-pixel SUV(max) persist. In this study, we measured aspects of bias and reproducibility associated with SUV(max) and the closely related peak SUV (SUV(peak)) using real patient data to provide a realistic noise context. List-mode 3-dimensional PET data were acquired for 15 min over a single bed position in twenty (18)F-FDG oncology patients. For each patient, data were sorted so as to form 2 sets of images: respiration-gated images such that each image had statistical quality comparable to a 3 min/bed position scan, and 5 statistically independent (ungated) images of different durations (1, 2, 3, 4, and 5 min). Tumor SUV(max) and SUV(peak) (12-mm-diameter spheric region of interest positioned so as to maximize the enclosed average) were analyzed in terms of reproducibility and bias. The component of reproducibility due to statistical noise (independent of physiologic and other variables) was measured using paired SUVs from 2 comparable respiration-gated images. Bias was measured as a function of scan duration. Replicate tumor SUV measurements had a within-patient SD of 5.6% ± 0.9% for SUV(max) and 2.5% ± 0.4% for SUV(peak). SUV(max) had average positive biases of 30%, 18%, 12%, 4%, and 5% for the 1-, 2-, 3-, 4-, and 5-min images, respectively. SUV(peak) was also biased but to a lesser extent: 11%, 8%, 5%, 1%, and 4% for the 1-, 2-, 3-, 4-, and 5-min images, respectively. The advantages of SUV(max) are best exploited when PET images have a high statistical quality. For images with noise properties typically associated with clinical whole-body studies, SUV(peak) provides a slightly more robust alternative for assessing the most metabolically active region of tumor.
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                Author and article information

                Contributors
                +31 384245238 , d.koopman@isala.nl
                j.a.c.van.osch@isala.nl
                p.l.jager@isala.nl
                c.j.a.tenbergen@student.utwente.nl
                s.knollema@isala.nl
                c.h.slump@utwente.nl
                j.a.van.dalen@isala.nl
                Journal
                EJNMMI Phys
                EJNMMI Phys
                EJNMMI Physics
                Springer International Publishing (Cham )
                2197-7364
                29 September 2016
                29 September 2016
                December 2016
                : 3
                : 22
                Affiliations
                [1 ]Department of Nuclear Medicine, Isala, Zwolle, The Netherlands
                [2 ]MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
                [3 ]Department of Medical Physics, Isala, Zwolle, The Netherlands
                Author information
                http://orcid.org/0000-0002-8976-5434
                Article
                158
                10.1186/s40658-016-0158-z
                5040656
                27682837
                525e0ba8-0107-449c-9e89-7403387d02d9
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 26 May 2016
                : 17 September 2016
                Categories
                Original Research
                Custom metadata
                © The Author(s) 2016

                fdg-pet,scan time protocol,tumour imaging,eanm guidelines

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