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      Data-driven gating (DDG)-based motion match for improved CTAC registration

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          Abstract

          Background

          Respiratory motion artefacts are a pitfall in thoracic PET/CT imaging. A source of these motion artefacts within PET images is the CT used for attenuation correction of the images. The arbitrary respiratory phase in which the helical CT ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox{CT}_{\text{helical}}$$\end{document}

          ) is acquired often causes misregistration between PET and CT images, leading to inaccurate attenuation correction of the PET image. As a result, errors in tumour delineation or lesion uptake values can occur. To minimise the effect of motion in PET/CT imaging, a data-driven gating (DDG)-based motion match (MM) algorithm has been developed that estimates the phase of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox{CT}_{\text{helical}}$$\end{document}
          , and subsequently warps this CT to a given phase of the respiratory cycle, allowing it to be phase-matched to the PET. A set of data was used which had four-dimensional CT (4DCT) acquired alongside PET/CT. The 4DCT allowed ground truth CT phases to be generated and compared to the algorithm-generated motion match CT (MMCT). Measurements of liver and lesion margin positions were taken across CT images to determine any differences and establish how well the algorithm performed concerning warping the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox{CT}_{\text{helical}}$$\end{document}
          to a given phase (end-of-expiration, EE).

          Results

          Whilst there was a minor significance in the liver measurement between the 4DCT and MMCT ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 0.045$$\end{document}

          ), no significant differences were found between the 4DCT or MMCT for lesion measurements ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 1.0$$\end{document}
          ). In all instances, the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox{CT}_{\text{helical}}$$\end{document}
          was found to be significantly different from the 4DCT ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.001$$\end{document}
          ). Consequently, the 4DCT and MMCT can be considered equivalent with respect to warped CT generation, showing the DDG-based MM algorithm to be successful.

          Conclusion

          The MM algorithm successfully enables the phase-matching of a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox{CT}_{\text{helical}}$$\end{document}

          to the EE of a ground truth 4DCT. This would reduce the motion artefacts caused by PET/CT registration without requiring additional patient dose (required for a 4DCT).

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          Most cited references26

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          • Article: not found

          Phantom and Clinical Evaluation of the Bayesian Penalized Likelihood Reconstruction Algorithm Q.Clear on an LYSO PET/CT System.

          Q.Clear, a Bayesian penalized-likelihood reconstruction algorithm for PET, was recently introduced by GE Healthcare on their PET scanners to improve clinical image quality and quantification. In this work, we determined the optimum penalization factor (beta) for clinical use of Q.Clear and compared Q.Clear with standard PET reconstructions.
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            Attenuation correction for a combined 3D PET/CT scanner.

            In this work we demonstrate the proof of principle of CT-based attenuation correction of 3D positron emission tomography (PET) data by using scans of bone and soft tissue equivalent phantoms and scans of humans. This method of attenuation correction is intended for use in a single scanner that combines volume-imaging (3D) PET with x-ray computed tomography (CT) for the purpose of providing accurately registered anatomical localization of structures seen in the PET image. The goal of this work is to determine if we can perform attenuation correction of the PET emission data using accurately aligned CT attenuation information. We discuss possible methods of calculating the PET attenuation map at 511 keV based on CT transmission information acquired from 40 keV through 140 keV. Data were acquired on separate CT and PET scanners and were aligned using standard image registration procedures. Results are presented on three of the attenuation calculation methods: segmentation, scaling, and our proposed hybrid segmentation/scaling method. The results are compared with those using the standard 3D PET attenuation correction method as a gold standard. We demonstrate the efficacy of our proposed hybrid method for converting the CT attenuation map from an effective CT photon energy of 70 keV to the PET photon energy of 511 keV. We conclude that using CT information is a feasible way to obtain attenuation correction factors for 3D PET.
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              Respiration-induced movement of the upper abdominal organs: a pitfall for the three-dimensional conformal radiation treatment of pancreatic cancer.

              Respiration-induced movement of the upper abdominal organs (pancreas, liver and kidneys) was assessed in 12 subjects using dynamic magnetic resonance imaging. The movement of each organ in the cranio-caudal, the lateral and the anterior-posterior direction was deduced from the movement of the center of gravity on two-dimensional images. This center of gravity was computed from the volume delineated on sequential 8-mm slices of both sagittal and coronal dynamic series. The largest movements were noticed in the cranio-caudal direction for pancreas and liver (23.7+/-15.9 mm and 24.4+/-16.4 mm). The kidneys showed smaller movements in the cranio-caudal direction (left kidney 16.9+/-6.7 mm and right kidney 16.1+/-7.9 mm). The movements of the different organs in the anterior-posterior and lateral directions were less pronounced. It is of the greatest importance to be aware of these movements in the planning of a conformal radiation treatment for pancreatic cancer.
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                Author and article information

                Contributors
                daniel.mcgowan@oncology.ox.ac.uk
                Journal
                EJNMMI Phys
                EJNMMI Phys
                EJNMMI Physics
                Springer International Publishing (Cham )
                2197-7364
                1 May 2024
                1 May 2024
                December 2024
                : 11
                : 42
                Affiliations
                [1 ]Department of Oncology, University of Oxford, ( https://ror.org/052gg0110) Oxford, UK
                [2 ]GRID grid.418143.b, ISNI 0000 0001 0943 0267, GE HealthCare, ; Waukesha, Wisconsin USA
                [3 ]GE HealthCare, Haifa, Israel
                [4 ]Department of Medical Physics and Clinical Engineering, Oxford University Hospitals Foundation Trust, ( https://ror.org/052gg0110) Oxford, UK
                Author information
                http://orcid.org/0000-0002-6880-5687
                Article
                644
                10.1186/s40658-024-00644-0
                11554991
                38691232
                3f1e1535-e3fe-4902-9fdb-8edac8b0e007
                © 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
                : 9 January 2024
                : 24 April 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000289, Cancer Research UK;
                Award ID: C34326/A28684
                Award ID: C42780/A27066
                Award ID: C5255/A25069
                Award ID: C5255/A16466
                Award Recipient :
                Categories
                Original Research
                Custom metadata
                © Springer Nature Switzerland AG 2024

                respiratory motion,data-driven gating,4dct
                respiratory motion, data-driven gating, 4dct

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