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      Validation of image-derived input function using a long axial field of view PET/CT scanner for two different tracers

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          Abstract

          Background

          Accurate image-derived input function (IDIF) from highly sensitive large axial field of view (LAFOV) PET/CT scanners could avoid the need of invasive blood sampling for kinetic modelling. The aim is to validate the use of IDIF for two kinds of tracers, 3 different IDIF locations and 9 different reconstruction settings.

          Methods

          Eight [ 18F]FDG and 10 [ 18F]DPA-714 scans were acquired respectively during 70 and 60 min on the Vision Quadra PET/CT system. PET images were reconstructed using various reconstruction settings. IDIFs were taken from ascending aorta (AA), descending aorta (DA), and left ventricular cavity (LV). The calibration factor (CF) extracted from the comparison between the IDIFs and the manual blood samples as reference was used for IDIFs accuracy and precision assessment. To illustrate the effect of various calibrated-IDIFs on Patlak linearization for [ 18F]FDG and Logan linearization for [ 18F]DPA-714, the same target time-activity curves were applied for each calibrated-IDIF.

          Results

          For [ 18F]FDG, the accuracy and precision of the IDIFs were high (mean CF ≥ 0.82, SD ≤ 0.06). Compared to the striatum influx ( K i ) extracted using calibrated AA IDIF with the updated European Association of Nuclear Medicine Research Ltd. standard reconstruction (EARL2), K i mean differences were < 2% using the other calibrated IDIFs. For [ 18F]DPA714, high accuracy of the IDIFs was observed (mean CF ≥ 0.86) except using absolute scatter correction, DA and LV (respectively mean CF = 0.68, 0.47 and 0.44). However, the precision of the AA IDIFs was low (SD ≥ 0.10). Compared to the distribution volume ( V T ) in a frontal region obtained using calibrated continuous arterial sampler input function as reference, V T mean differences were small using calibrated AA IDIFs (for example V T mean difference = -5.3% using EARL2), but higher using calibrated DA and LV IDIFs (respectively + 12.5% and + 19.1%).

          Conclusions

          For [ 18F]FDG, IDIF do not need calibration against manual blood samples. For [ 18F]DPA-714, AA IDIF can replace continuous arterial sampling for simplified kinetic quantification but only with calibration against arterial blood samples. The accuracy and precision of IDIF from LAFOV PET/CT system depend on tracer, reconstruction settings and IDIF VOI locations, warranting careful optimization.

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          Most cited references35

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          Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations.

          The method of graphical analysis for the evaluation of sequential data (e.g., tissue and blood concentrations over time) in which the test substance is irreversibly trapped in the system has been expanded. A simpler derivation of the original analysis is presented. General equations are derived that can be used to analyze tissue uptake data when the blood-plasma concentration of the test substance cannot be easily measured. In addition, general equations are derived for situations when trapping of the test substance is incomplete and for a combination of these two conditions. These derivations are independent of the actual configuration of the compartmental system being analyzed and show what information can be obtained for the period when the reversible compartments are in effective steady state with the blood. This approach is also shown to result in equations with at least one less nonlinear term than those derived from direct compartmental analysis. Specific applications of these equations are illustrated for a compartmental system with one reversible region (with or without reversible binding) and one irreversible region.
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            Graphical analysis of PET data applied to reversible and irreversible tracers.

            Jean Logan (2000)
            The differential equations of compartmental analysis form the basis of the models describing the uptake of tracers used in imaging studies. Graphical analyses convert the model equations into linear plots, the slopes of which represent measures of tracer binding. The graphical methods are not dependent upon a particular model structure but the slopes can be related to combinations of the model parameters if a model structure is assumed. The input required is uptake data from a region of interest vs time and an input function that can either be plasma measurements or uptake data from a suitable reference region. Graphical methods can be applied to both reversible and irreversibly binding tracers. They provide considerable ease of computation compared to the optimization of individual model parameters in the solution of the differential equations generally used to describe the binding of tracers. Conditions under which the graphical techniques are applicable and some problems encountered in separating tracer delivery and binding are considered. Also the effect of noise can introduce a bias in the distribution volume which is the slope of the graphical analysis of reversible tracers. Smoothing techniques may minimize this problem and retain the model independence. In any case graphical techniques can provide insight into the binding kinetics of tracers in a visual way.
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              Anatomy of SUV. Standardized uptake value.

              Standardized uptake value (SUV) for [F-18]fluorodeoxyglucose (FDG) studies that is commonly used to differentiate malignant from benign tumors and to assess the efficacy of therapy is reviewed as a simplified calculation of the more general modeling approach. Based on such a basis, the merits and limitations of the SUV approach is examined with reference to literature reports on tumor uptake of FDG. Results indicate the complexity and large variation of glucose uptake mechanism in tumors. Consistently performed procedures and more basic studies are needed to improve the utility of FDG SUV.
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                Author and article information

                Contributors
                x.palard@rennes.unicancer.fr
                Journal
                EJNMMI Phys
                EJNMMI Phys
                EJNMMI Physics
                Springer International Publishing (Cham )
                2197-7364
                13 March 2024
                13 March 2024
                December 2024
                : 11
                : 25
                Affiliations
                [1 ]GRID grid.410368.8, ISNI 0000 0001 2191 9284, Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, ; Rennes, France
                [2 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, ; Amsterdam, The Netherlands
                [3 ]Amsterdam Neuroscience, Brain Imaging, ( https://ror.org/01x2d9f70) Amsterdam, The Netherlands
                [4 ]University Medical Center Utrecht, ( https://ror.org/0575yy874) Utrecht, The Netherlands
                [5 ]Cancer Center Amsterdam, Imaging and Biomarkers, ( https://ror.org/0286p1c86) Amsterdam, The Netherlands
                [6 ]Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, ( https://ror.org/02jx3x895) London, UK
                Article
                628
                10.1186/s40658-024-00628-0
                10933214
                38472680
                11d34f4e-b741-46c4-b9fc-0b55b183953b
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 December 2023
                : 27 February 2024
                Categories
                Original Research
                Custom metadata
                © Springer Nature Switzerland AG 2024

                [18f]dpa-714,[18f]fdg,lafov pet/ct,idif
                [18f]dpa-714, [18f]fdg, lafov pet/ct, idif

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