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      Assessment of different quantification metrics of [ 18F]-NaF PET/CT images of patients with abdominal aortic aneurysm

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          Abstract

          Background

          We aim to assess the spill-in effect and the benefit in quantitative accuracy for [ 18F]-NaF PET/CT imaging of abdominal aortic aneurysms (AAA) using the background correction (BC) technique.

          Methods

          Seventy-two datasets of patients diagnosed with AAA were reconstructed with ordered subset expectation maximization algorithm incorporating point spread function (PSF). Spill-in effect was investigated for the entire aneurysm (AAA), and part of the aneurysm excluding the region close to the bone (AAA exc). Quantifications of PSF and PSF+BC images using different thresholds (% of max. SUV in target regions-of-interest) to derive target-to-background (TBR) values (TBR max, TBR 90, TBR 70 and TBR 50) were compared at 3 and 10 iterations.

          Results

          TBR differences were observed between AAA and AAA exc due to spill-in effect from the bone into the aneurysm. TBR max showed the highest sensitivity to the spill-in effect while TBR 50 showed the least. The spill-in effect was reduced at 10 iterations compared to 3 iterations, but at the expense of reduced contrast-to-noise ratio (CNR). TBR 50 yielded the best trade-off between increased CNR and reduced spill-in effect. PSF+BC method reduced TBR sensitivity to spill-in effect, especially at 3 iterations, compared to PSF ( P-value ≤ 0.05).

          Conclusion

          TBR 50 is robust metric for reduced spill-in and increased CNR.

          Electronic supplementary material

          The online version of this article (10.1007/s12350-020-02220-2) contains supplementary material, which is available to authorized users.

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          Most cited references41

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          AMIDE: a free software tool for multimodality medical image analysis.

          Amide's a Medical Image Data Examiner (AMIDE) has been developed as a user-friendly, open-source software tool for displaying and analyzing multimodality volumetric medical images. Central to the package's abilities to simultaneously display multiple data sets (e.g., PET, CT, MRI) and regions of interest is the on-demand data reslicing implemented within the program. Data sets can be freely shifted, rotated, viewed, and analyzed with the program automatically handling interpolation as needed from the original data. Validation has been performed by comparing the output of AMIDE with that of several existing software packages. AMIDE runs on UNIX, Macintosh OS X, and Microsoft Windows platforms, and it is freely available with source code under the terms of the GNU General Public License.
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            18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort.

            Intratumoral uptake heterogeneity in (18)F-FDG PET has been associated with patient treatment outcomes in several cancer types. Textural feature analysis is a promising method for its quantification. An open issue associated with textural features for the quantification of intratumoral heterogeneity concerns its added contribution and dependence on the metabolically active tumor volume (MATV), which has already been shown to be a significant predictive and prognostic parameter. Our objective was to address this question using a larger cohort of patients covering different cancer types.
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              Partial-volume effect in PET tumor imaging.

              PET has the invaluable advantage of being intrinsically quantitative, enabling accurate measurements of tracer concentrations in vivo. In PET tumor imaging, indices characterizing tumor uptake, such as standardized uptake values, are becoming increasingly important, especially in the context of monitoring the response to therapy. However, when tracer uptake in small tumors is measured, large biases can be introduced by the partial-volume effect (PVE). The purposes of this article are to explain what PVE is and to describe its consequences in PET tumor imaging. The parameters on which PVE depends are reviewed. Actions that can be taken to reduce the errors attributable to PVE are described. Various PVE correction schemes are presented, and their applicability to PET tumor imaging is discussed.
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                Author and article information

                Contributors
                mercyoloniyo@yahoo.com
                c.tsoumpas@leeds.ac.uk
                Journal
                J Nucl Cardiol
                J Nucl Cardiol
                Journal of Nuclear Cardiology
                Springer International Publishing (Cham )
                1071-3581
                1532-6551
                17 June 2020
                17 June 2020
                2022
                : 29
                : 1
                : 251-261
                Affiliations
                [1 ]GRID grid.9909.9, ISNI 0000 0004 1936 8403, Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, , University of Leeds, ; Leeds, LS2 9NL UK
                [2 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, British Heart Foundation Centre for Cardiovascular Science, , University of Edinburgh, ; Edinburgh, UK
                [3 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, Edinburgh Imaging Facility, Queen’s Medical Research Institute, , University of Edinburgh, ; Edinburgh, UK
                [4 ]GRID grid.5386.8, ISNI 000000041936877X, Division of Radiopharmaceutical Sciences, Department of Radiology, , Weil Cornell Medical College of Cornell University, ; New York, NY USA
                [5 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Biomedical Engineering & Imaging Institute, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [6 ]GRID grid.498414.4, ISNI 0000 0004 0548 3187, Invicro, ; London, UK
                Article
                2220
                10.1007/s12350-020-02220-2
                8873073
                32557152
                453037a2-fc21-4ef5-9720-f20451f4e73c
                © The Author(s) 2020

                Open AccessThis 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
                : 20 February 2020
                : 26 May 2020
                Categories
                Original Article
                Custom metadata
                © The Author(s) under exclusive licence to American Society of Nuclear Cardiology 2022

                Cardiovascular Medicine
                abdominal aortic aneurysm,spill-in effect,background correction,target-to-background ratio

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