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Lan W, Sari H, Rominger A, Fougère CL, Schmidt FP. Optimization and impact of sensitivity mode on abbreviated scan protocols with population-based input function for parametric imaging of [ 18F]-FDG for a long axial FOV PET scanner. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06745-3. [PMID: 38763962 DOI: 10.1007/s00259-024-06745-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 04/28/2024] [Indexed: 05/21/2024]
Abstract
BACKGROUND The long axial field of view, combined with the high sensitivity of the Biograph Vision Quadra PET/CT scanner enables the precise deviation of an image derived input function (IDIF) required for parametric imaging. Traditionally, this requires an hour-long dynamic PET scan for [18F]-FDG, which can be significantly reduced by using a population-based input function (PBIF). In this study, we expand these examinations and include the scanner's ultra-high sensitivity (UHS) mode in comparison to the high sensitivity (HS) mode and evaluate the potential for further shortening of the scan time. METHODS Patlak Ki and DV estimates were determined by the indirect and direct Patlak methods using dynamic [18F]-FDG data of 6 oncological patients with 26 lesions (0-65 min p.i.). Both sensitivity modes for different number/duration of PET data frames were compared, together with the potential of using abbreviated scan durations of 20, 15 and 10 min by using a PBIF. The differences in parametric images and tumour-to-background ratio (TBR) due to the shorter scans using the PBIF method and between the sensitivity modes were assessed. RESULTS A difference of 3.4 ± 7.0% (Ki) and 1.2 ± 2.6% (DV) was found between both sensitivity modes using indirect Patlak and the full IDIF (0-65 min). For the abbreviated protocols and indirect Patlak, the UHS mode resulted in a lower bias and higher precision, e.g., 45-65 min p.i. 3.8 ± 4.4% (UHS) and 6.4 ± 8.9% (HS), allowing shorter scan protocols, e.g. 50-65 min p.i. 4.4 ± 11.2% (UHS) instead of 7.3 ± 20.0% (HS). The variation of Ki and DV estimates for both Patlak methods was comparable, e.g., UHS mode 3.8 ± 4.4% and 2.7 ± 3.4% (Ki) and 14.4 ± 2.7% and 18.1 ± 7.5% (DV) for indirect and direct Patlak, respectively. Only a minor impact of the number of Patlak frames was observed for both sensitivity modes and Patlak methods. The TBR obtained with direct Patlak and PBIF was not affected by the sensitivity mode, was higher than that derived from the SUV image (6.2 ± 3.1) and degraded from 20.2 ± 12.0 (20 min) to 10.6 ± 5.4 (15 min). Ki and DV estimate images showed good agreement (UHS mode, RC: 6.9 ± 2.3% (Ki), 0.1 ± 3.1% (DV), peak signal-to-noise ratio (PSNR): 64.5 ± 3.3 dB (Ki), 61.2 ± 10.6 dB (DV)) even for abbreviated scan protocols of 50-65 min p.i. CONCLUSIONS Both sensitivity modes provide comparable results for the full 65 min dynamic scans and abbreviated scans using the direct Patlak reconstruction method, with good Ki and DV estimates for 15 min short scans. For the indirect Patlak approach the UHS mode improved the Ki estimates for the abbreviated scans.
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Affiliation(s)
- W Lan
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany
| | - H Sari
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - A Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - C la Fougère
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, Tuebingen, Germany
| | - F P Schmidt
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany.
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tuebingen, Tuebingen, Germany.
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Sun P, Thomas MA, Luo D, Pan T. New full-counts phase-matched data-driven gated (DDG) PET/CT. Med Phys 2024. [PMID: 38648671 DOI: 10.1002/mp.17097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/06/2024] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Data-driven gated (DDG) PET has gained clinical acceptance and has been shown to match or outperform external-device gated (EDG) PET. However, in most clinical applications, DDG PET is matched with helical CT acquired in free breathing (FB) at a random respiratory phase, leaving registration, and optimal attenuation correction (AC) to chance. Furthermore, DDG PET requires additional scan time to reduce image noise as it only preserves 35%-50% of the PET data at or near the end-expiratory phase of the breathing cycle. PURPOSE A new full-counts, phase-matched (FCPM) DDG PET/CT was developed based on a low-dose cine CT to improve registration between DDG PET and DDG CT, to reduce image noise, and to avoid increasing acquisition times in DDG PET. METHODS A new DDG CT was developed for three respiratory phases of CT images from a low dose cine CT acquisition of 1.35 mSv for a coverage of about 15.4 cm: end-inspiration (EI), average (AVG), and end-expiration (EE) to match with the three corresponding phases of DDG PET data: -10% to 15%; 15% to 30%, and 80% to 90%; and 30% to 80%, respectively. The EI and EE phases of DDG CT were selected based on the physiological changes in lung density and body outlines reflected in the dynamic cine CT images. The AVG phase was derived from averaging of all phases of the cine CT images. The cine CT was acquired over the lower lungs and/or upper abdomen for correction of misregistration between PET and FB CT as well as DDG PET and FB CT. The three phases of DDG CT were used for AC of the corresponding phases of PET. After phase-matched AC of each PET dataset, the EI and AVG PET data were registered to the EE PET data with deformable image registration. The final result was FCPM DDG PET/CT which accounts for all PET data registered at the EE phase. We applied this approach to 14 18F-FDG lung cancer patient studies acquired at 2 min/bed position on the GE Discovery MI (25-cm axial FOV) and evaluated its efficacy in improved quantification and noise reduction. RESULTS Relative to static PET/CT, the SUVmax increases for the EI, AVG, EE, and FCPM DDG PET/CT were 1.67 ± 0.40, 1.50 ± 0.28, 1.64 ± 0.36, and 1.49 ± 0.28, respectively. There were 10.8% and 9.1% average decreases in SUVmax from EI and EE to FCPM DDG PET/CT, respectively. EI, AVG, and EE DDG PET/CT all maintained increased image noise relative to static PET/CT. However, the noise levels of FCPM and static PET were statistically equivalent, suggesting the inclusion of all counts was able to decrease the image noise relative to EI and EE DDG PET/CT. CONCLUSIONS A new FCPM DDG PET/CT has been developed to account for 100% of collected PET data in DDG PET applications. Image noise in FCPM is comparable to static PET, while small decreases in SUVmax were also observed in FCPM when compared to either EI or EE DDG PET/CT.
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Affiliation(s)
- Peng Sun
- Department of Imaging Physics, UT MD Anderson Cancer Center, Houston, Texas, USA
| | - M Allan Thomas
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Dershan Luo
- Department of Radiation Physics, UT MD Anderson Cancer Center, Houston, Texas, USA
| | - Tinsu Pan
- Department of Imaging Physics, UT MD Anderson Cancer Center, Houston, Texas, USA
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Artesani A, Bruno A, Gelardi F, Chiti A. Empowering PET: harnessing deep learning for improved clinical insight. Eur Radiol Exp 2024; 8:17. [PMID: 38321340 PMCID: PMC10847083 DOI: 10.1186/s41747-023-00413-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/20/2023] [Indexed: 02/08/2024] Open
Abstract
This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the "black-box" problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.Relevance statementAI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.Key points• Applying AI has the potential to enhance the entire PET imaging pipeline.• AI may support several clinical tasks in both PET diagnosis and prognosis.• Interpreting the relationships between imaging and multiomics data will heavily rely on AI.
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Affiliation(s)
- Alessia Artesani
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele, 20090, Italy
| | - Alessandro Bruno
- Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", IULM Libera Università Di Lingue E Comunicazione, Via P. Filargo 38, Milan, 20143, Italy
| | - Fabrizia Gelardi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele, 20090, Italy.
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.
| | - Arturo Chiti
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milan, 20132, Italy
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Nii T, Hosokawa S, Kotani T, Domoto H, Nakamura Y, Tanada Y, Kondo R, Takahashi Y. Evaluation of Data-Driven Respiration Gating in Continuous Bed Motion in Lung Lesions. J Nucl Med Technol 2023; 51:32-37. [PMID: 36750380 DOI: 10.2967/jnmt.122.264909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/04/2023] [Accepted: 01/04/2023] [Indexed: 02/09/2023] Open
Abstract
Respiration gating is used in PET to prevent image quality degradation due to respiratory effects. In this study, we evaluated a type of data-driven respiration gating for continuous bed motion, OncoFreeze AI, which was implemented to improve image quality and the accuracy of semiquantitative uptake values affected by respiratory motion. Methods: 18F-FDG PET/CT was performed on 32 patients with lung lesions. Two types of respiration-gated images (OncoFreeze AI with data-driven respiration gating, device-based amplitude-based OncoFreeze with elastic motion compensation) and ungated images (static) were reconstructed. For each image, we calculated SUV and metabolic tumor volume (MTV). The improvement rate (IR) from respiration gating and the contrast-to-noise ratio (CNR), which indicates the improvement in image noise, were also calculated for these indices. IR was also calculated for the upper and lower lobes of the lung. As OncoFreeze AI assumes the presence of respiratory motion, we examined quantitative accuracy in regions where respiratory motion was not present using a 68Ge cylinder phantom with known quantitative accuracy. Results: OncoFreeze and OncoFreeze AI showed similar values, with a significant increase in SUV and decrease in MTV compared with static reconstruction. OncoFreeze and OncoFreeze AI also showed similar values for IR and CNR. OncoFreeze AI increased SUVmax by an average of 18% and decreased MTV by an average of 25% compared with static reconstruction. From the IR results, both OncoFreeze and OncoFreeze AI showed a greater IR from static reconstruction in the lower lobe than in the upper lobe. OncoFreeze and OncoFreeze AI increased CNR by 17.9% and 18.0%, respectively, compared with static reconstruction. The quantitative accuracy of the 68Ge phantom, assuming a region of no respiratory motion, was almost equal for the static reconstruction and OncoFreeze AI. Conclusion: OncoFreeze AI improved the influence of respiratory motion in the assessment of lung lesion uptake to a level comparable to that of the previously launched OncoFreeze. OncoFreeze AI provides more accurate imaging with significantly larger SUVs and smaller MTVs than static reconstruction.
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Affiliation(s)
- Takeshi Nii
- Division of Radiological Technology, Department of Medical Technology, University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan;
| | - Shota Hosokawa
- Department of Radiation Science, Graduate School of Health Sciences, Hirosaki University, Hirosaki, Japan
| | - Tomoya Kotani
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hiroshi Domoto
- Division of Radiological Technology, Department of Medical Technology, University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yasunori Nakamura
- Division of Radiological Technology, Department of Medical Technology, University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan.,Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan; and
| | - Yasutomo Tanada
- Division of Radiological Technology, Department of Medical Technology, University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan.,Department of Quantum Medical Technology, Graduate School of Medical Sciences, Kanazawa University, Ishikawa, Japan
| | - Ryotaro Kondo
- Division of Radiological Technology, Department of Medical Technology, University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yasuyuki Takahashi
- Department of Radiation Science, Graduate School of Health Sciences, Hirosaki University, Hirosaki, Japan
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5
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Faist D, Jreige M, Oreiller V, Nicod Lalonde M, Schaefer N, Depeursinge A, Prior JO. Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [ 18F]-FDG PET/CT. Eur J Hybrid Imaging 2022; 6:33. [PMID: 36309636 PMCID: PMC9617997 DOI: 10.1186/s41824-022-00153-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/14/2022] [Indexed: 11/13/2022] Open
Abstract
Background Quality and reproducibility of radiomics studies are essential requirements for the standardisation of radiomics models. As recent data-driven respiratory gating (DDG) [18F]-FDG has shown superior diagnostic performance in lung cancer, we evaluated the impact of DDG on the reproducibility of radiomics features derived from [18F]-FDG PET/CT in comparison to free-breathing flow (FB) imaging.
Methods Twenty four lung nodules from 20 patients were delineated. Radiomics features were derived on FB flow PET/CT and on the corresponding DDG reconstruction using the QuantImage v2 platform. Lin’s concordance factor (Cb) and the mean difference percentage (DIFF%) were calculated for each radiomics feature using the delineated nodules which were also classified by anatomical localisation and volume. Non-reproducible radiomics features were defined as having a bias correction factor Cb < 0.8 and/or a mean difference percentage DIFF% > 10.
Results In total 141 features were computed on each concordance analysis, 10 of which were non-reproducible on all pulmonary lesions. Those were first-order features from Laplacian of Gaussian (LoG)-filtered images (sigma = 1 mm): Energy, Kurtosis, Minimum, Range, Root Mean Squared, Skewness and Variance; Texture features from Gray Level Cooccurence Matrix (GLCM): Cluster Prominence and Difference Variance; First-order Standardised Uptake Value (SUV) feature: Kurtosis. Pulmonary lesions located in the superior lobes had only stable radiomics features, the ones from the lower parts had 25 non-reproducible radiomics features. Pulmonary lesions with a greater size (defined as long axis length > median) showed a higher reproducibility (9 non-reproducible features) than smaller ones (20 non-reproducible features).
Conclusion Calculated on all pulmonary lesions, 131 out of 141 radiomics features can be used interchangeably between DDG and FB PET/CT acquisitions. Radiomics features derived from pulmonary lesions located inferior to the superior lobes are subject to greater variability as well as pulmonary lesions of smaller size. Supplementary Information The online version contains supplementary material available at 10.1186/s41824-022-00153-2.
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Affiliation(s)
- Daphné Faist
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland
| | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland
| | - Valentin Oreiller
- Institute of Information Systems, University of Applied Sciences Western Switzerland, (HES-SO), Rue du Technopôle 3, CH-3960, Sierre, Switzerland
| | - Marie Nicod Lalonde
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Rue du Bugnon 21, CH-1011, Lausanne, Switzerland
| | - Niklaus Schaefer
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Rue du Bugnon 21, CH-1011, Lausanne, Switzerland
| | - Adrien Depeursinge
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland.,Institute of Information Systems, University of Applied Sciences Western Switzerland, (HES-SO), Rue du Technopôle 3, CH-3960, Sierre, Switzerland
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland. .,Faculty of Biology and Medicine, University of Lausanne, Rue du Bugnon 21, CH-1011, Lausanne, Switzerland.
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Noto B, Roll W, Zinken L, Rischen R, Kerschke L, Evers G, Heindel W, Schäfers M, Büther F. Respiratory motion correction in F-18-FDG PET/CT impacts lymph node assessment in lung cancer patients. EJNMMI Res 2022; 12:61. [PMID: 36107357 PMCID: PMC9478021 DOI: 10.1186/s13550-022-00926-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/19/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUNDS Elastic motion correction in PET has been shown to increase image quality and quantitative measurements of PET datasets affected by respiratory motion. However, little is known on the impact of respiratory motion correction on clinical image evaluation in oncologic PET. This study evaluated the impact of motion correction on expert readers' lymph node assessment of lung cancer patients. METHODS Forty-three patients undergoing F-18-FDG PET/CT for the staging of suspected lung cancer were included. Three different PET reconstructions were investigated: non-motion-corrected ("static"), belt gating-based motion-corrected ("BG-MC") and data-driven gating-based motion-corrected ("DDG-MC"). Assessment was conducted independently by two nuclear medicine specialists blinded to the reconstruction method on a six-point scale [Formula: see text] ranging from "certainly negative" (1) to "certainly positive" (6). Differences in [Formula: see text] between reconstruction methods, accounting for variation caused by readers, were assessed by nonparametric regression analysis of longitudinal data. From [Formula: see text], a dichotomous score for N1, N2, and N3 ("negative," "positive") and a subjective certainty score were derived. SUV and metabolic tumor volumes (MTV) were compared between reconstruction methods. RESULTS BG-MC resulted in higher scores for N1 compared to static (p = 0.001), whereas DDG-MC resulted in higher scores for N2 compared to static (p = 0.016). Motion correction resulted in the migration of N1 from tumor free to metastatic on the dichotomized score, consensually for both readers, in 3/43 cases and in 2 cases for N2. SUV was significantly higher for motion-corrected PET, while MTV was significantly lower (all p < 0.003). No significant differences in the certainty scores were noted. CONCLUSIONS PET motion correction resulted in significantly higher lymph node assessment scores of expert readers. Significant effects on quantitative PET parameters were seen; however, subjective reader certainty was not improved.
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Affiliation(s)
- Benjamin Noto
- grid.16149.3b0000 0004 0551 4246Department of Nuclear Medicine, University Hospital Münster, Münster, Germany ,grid.16149.3b0000 0004 0551 4246Clinical for Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany
| | - Wolfgang Roll
- grid.16149.3b0000 0004 0551 4246Department of Nuclear Medicine, University Hospital Münster, Münster, Germany
| | - Laura Zinken
- grid.16149.3b0000 0004 0551 4246Department of Nuclear Medicine, University Hospital Münster, Münster, Germany
| | - Robert Rischen
- grid.16149.3b0000 0004 0551 4246Clinical for Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany
| | - Laura Kerschke
- grid.5949.10000 0001 2172 9288Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Georg Evers
- grid.16149.3b0000 0004 0551 4246Department of Medicine A, Hematology, Oncology and Pulmonary Medicine, University Hospital Münster, Münster, Germany
| | - Walter Heindel
- grid.16149.3b0000 0004 0551 4246Clinical for Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany ,West German Cancer Centre (WTZ), Münster, Germany
| | - Michael Schäfers
- grid.16149.3b0000 0004 0551 4246Department of Nuclear Medicine, University Hospital Münster, Münster, Germany ,grid.5949.10000 0001 2172 9288European Institute for Molecular Imaging, University of Münster, Münster, Germany ,West German Cancer Centre (WTZ), Münster, Germany
| | - Florian Büther
- grid.16149.3b0000 0004 0551 4246Department of Nuclear Medicine, University Hospital Münster, Münster, Germany ,grid.5949.10000 0001 2172 9288European Institute for Molecular Imaging, University of Münster, Münster, Germany
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