Evaluation of deep learning-based scatter correction on a long-axial field-of-view PET scanner

Alberts I, Sari H, Mingels C, Afshar-Oromieh A, Pyka T, Shi K, Rominger A. Long-axial field-of-view PET/CT: perspectives and review of a revolutionary development in nuclear medicine based on clinical experience in over 7000 patients. Cancer imaging : the official publication of the International Cancer Imaging Society. 2023;23:28. https://doi.org/10.1186/s40644-023-00540-3.

Slart R, Tsoumpas C, Glaudemans A, Noordzij W, Willemsen A, Borra R, Dierckx R, Lammertsma A. Long axial field of view PET scanners: a road map to implementation and new possibilities. European Journal of Nuclear Medicine and Molecular Imaging. 2021;48. https://doi.org/10.1007/s00259-021-05461-6

Surti S, Pantel AR, Karp JS. Total body pet: Why, how, what for? IEEE Transactions on Radiation and Plasma Medical Sciences. 2020;4(3):283–92. https://doi.org/10.1109/TRPMS.2020.2985403.

Article  PubMed  PubMed Central  Google Scholar 

Alberts I, Hünermund JN, Prenosil G, Mingels C, Bohn K, Viscione M, Sari H, Vollnberg B, Shi K, Afshar-Oromieh A, Rominger A. Clinical performance of long axial field of view pet/ct: a head-to-head intra-individual comparison of the biograph vision quadra with the biograph vision pet/ct. European Journal of Nuclear Medicine and Molecular Imaging. 2021;48:1–10. https://doi.org/10.1007/s00259-021-05282-7.

Article  CAS  Google Scholar 

Prenosil G, Sari H, Fuerstner M, Afshar-Oromieh A, Shi K, Rominger A, Hentschel M. Performance characteristics of the biograph vision quadra pet/ct system with long axial field of view using the nema nu 2–2018 standard. Journal of Nuclear Medicine. 2021;63:121–261972. https://doi.org/10.2967/jnumed.121.261972.

Article  CAS  Google Scholar 

Mingels C, Weidner S, Sari H, Buesser D, Zeimpekis K, Shi K, Alberts I, Rominger A. Impact of the new ultra-high sensitivity mode in a long axial field-of-view PET/CT. Annals of nuclear medicine. 2023;37. https://doi.org/10.1007/s12149-023-01827-y

Ollinger J. Model-based scatter correction for fully 3D PET. Phys Med Biol. 1996;41:153–76.

Article  CAS  PubMed  Google Scholar 

Watson CC, Newport D, Casey ME. A Single Scatter Simulation Technique for Scatter Correction in 3D PET; 1996. pp. 255–268. Springer, Dordrecht

Watson CC. New, faster, image-based scatter correction for 3D PET. IEEE Trans Nucl Sci. 2000;47(4):1587–94. https://doi.org/10.1109/23.873020.

Article  Google Scholar 

Barret O, Carpenter T, Clark J, Ansorge R, Fryer T. Monte Carlo simulation and scatter correction of the GE advance PET scanner with SimSET and Geant4. Physics in medicine and biology. 2005;50:4823–40. https://doi.org/10.1088/0031-9155/50/20/006.

Article  CAS  PubMed  Google Scholar 

Holdsworth CH, Levin C, Janecek M, Dahlbom M, Hoffman EJ. Performance analysis of an improved 3-d pet monte carlo simulation and scatter correction. Nuclear Science, IEEE Transactions on. 2002;49:83–9. https://doi.org/10.1109/TNS.2002.998686.

Article  Google Scholar 

Levin C, Dahlbom M, Hoffman EJ. A Monte Carlo correction for the effect of compton scattering in 3-D PET brain imaging. Nuclear Science, IEEE Transactions on. 1995;42:1181–5. https://doi.org/10.1109/23.467880.

Article  Google Scholar 

Tsoumpas C, Aguiar P, Ros D, Dikaios N, Thielemans K. Scatter Simulation Including Double Scatter. 2005. vol. 3, pp. 1615–1619. IEEE. https://doi.org/10.1109/NSSMIC.2005.1596628

Watson CC, Hu J, Zhou C. Double scatter simulation for more accurate image reconstruction in positron emission tomography. IEEE Transactions on Radiation and Plasma Medical Sciences. 2020;4(5):570–84. https://doi.org/10.1109/TRPMS.2020.2990335.

Article  Google Scholar 

Adam L-E, Karp JS, Brix G. Investigation of scattered radiation in 3D whole-body positron emission tomography using Monte Carlo simulations. Physics in Medicine & Biology. 1999;44:2879–95.

Article  CAS  Google Scholar 

Zhang X, Zhou J, Cherry S, Badawi R, Qi J. Quantitative image reconstruction for total-body PET imaging using the 2-meter long EXPLORER scanner. Physics in Medicine and Biology. 2017;62:2465–85. https://doi.org/10.1088/1361-6560/aa5e46.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bal H, Panin VY, Schaefferkoetter J, Cabello J, Conti M. Fully 3D scatter estimation in axially long FOV PETCT scanners: Residual estimation approach. In: 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).2021. pp. 1–4. https://doi.org/10.1109/NSS/MIC44867.2021.9875665

Teuho J, Johansson J, Linden J, Saunavaara V, Teräs M. Comparison of single-scatter simulation and Monte Carlo single-scatter simulation on Philips Ingenuity TF PET/MR. In: 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2014. pp. 1–3. https://doi.org/10.1109/NSSMIC.2014.7430940

Bayerlein R, Spencer B, Leung E, Omidvari N, Abdelhafez Y, Wang Q, Nardo L, Cherry S, Badawi R. Development of a monte carlo-based scatter correction method for total-body PET using the uEXPLORER PET/CT scanner. Physics in medicine and biology. 2024;69. https://doi.org/10.1088/1361-6560/ad2230

Jahangir R, Kamali-Asl A, Arabi H, Zaidi H. Strategies for deep learning-based attenuation and scatter correction of brain 18F-FDG PET images in the image domain. Med Phys. 2024;51(2):870–80. https://doi.org/10.1002/mp.16914.

Article  CAS  PubMed  Google Scholar 

Guo R, Xue S, Hu J, Sari H, Mingels C, Zeimpekis K, Prenosil G, Wang Y, Zhang Y, Viscione M, Sznitman R, Rominger A, Li B, Shi K. Using domain knowledge for robust and generalizable deep learning-based CT-free pet attenuation and scatter correction. Nature Communications. 2022;13:5882. https://doi.org/10.1038/s41467-022-33562-9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Laurent B, Bousse A, Merlin T, Nekolla S, Visvikis D. PET scatter estimation using deep learning U-Net architecture. Physics in Medicine & Biology. 2023;68(6). https://doi.org/10.1088/1361-6560/ac9a97.

Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging. 1982;1(2):113–22.

Article  CAS  PubMed  Google Scholar 

Hudson HM, Larkin RS. Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging. 1994;13(4):601–9.

Article  CAS  PubMed  Google Scholar 

Knoll GF. Radiation Detection and Measurement. John Wiley & Sons. 2010

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. 2015. arXiv:1505.04597

Chollet F, et al. Keras. Accessed: 2022-06-06. 2015. https://keras.io

Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C. Corrado Gs, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Kaiser L, Kudlur M, Levenberg J, Zheng X. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems; 2015.

Kingma D, Ba J. Adam: A method for stochastic optimization. International Conference on Learning Representations. 2014.

Segars WP, Sturgeon G, Mendonca S, Grimes J, Tsui BMW. 4d XCAT phantom for multimodality imaging research: 4D XCAT phantom for multimodality imaging research. Med Phys. 2010;37(9):4902–15. https://doi.org/10.1118/1.3480985.

Ferrero A, Poon JK, Chaudhari AJ, MacDonald LR, Badawi RD. Effect of object size on scatter fraction estimation methods for PET–A computer simulation study. IEEE Trans Nucl Sci. 2011;58(1):82–6. https://doi.org/10.1109/TNS.2010.2080685.

Article  Google Scholar 

Le Maitre A, Segars WP, Marache S, Reilhac A, Hatt M, Tomei S, Lartizien C, Visvikis D. Incorporating patient-specific variability in the simulation of realistic whole-body \(^}\) distributions for oncology applications. Proc IEEE. 2009;97(12):2026–38. https://doi.org/10.1109/JPROC.2009.2027925.

Article  Google Scholar 

Peña-Acosta MM, Gallardo S, Lorduy-Alós M, Verdú G. Application of nema protocols to verify gate models based on the digital biograph vision and the biograph vision quadra scanners. Z Med Phys. 2024. https://doi.org/10.1016/j.zemedi.2024.01.005.

Article  PubMed  Google Scholar 

Salvadori J, Merlet A, Presles B, Cabello J, Su K-H, Cochet A, Etxebeste A, Vrigneaud J-M, Sarrut D. Pet digitization chain for monte carlo simulation in gate. Physics in Medicine & Biology. 2024;69(16). https://doi.org/10.1088/1361-6560/ad638c.

Jan S, Santin G, Strul D, Staelens S, Assié K, Autret D, Avner S, Barbier R, Bardiès M, Bloomfield PM, Brasse D, Breton V, Bruyndonckx P, Buvat I, Chatziioannou AF, Choi Y, Chung YH, Comtat C, Donnarieix D, Ferrer L, Glick SJ, Groiselle CJ, Guez D, Honore P-F, Kerhoas-Cavata S, Kirov AS, Kohli V, Koole M, Krieguer M, Laan DJ, Lamare F, Largeron G, Lartizien C, Lazaro D, Maas MC, Maigne L, Mayet F, Melot F, Merheb C, Pennacchio E, Perez J, Pietrzyk U, Rannou FR, Rey M, Schaart DR, Schmidtlein CR, Simon L, Song TY, Vieira J-M, Visvikis D, Walle RV, Wieërs E, Morel C. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol. 2004;49(19):4543–61. https://doi.org/10.1088/0031-9155/49/19/007.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ghabrial A, Franklin D, Zaidi H. A monte carlo simulation study of scatter fraction and the impact of patient BMI on scatter in long axial field-of-view PET scanners. Zeitschrift für Medizinische Physik. 2021;31. https://doi.org/10.1016/j.zemedi.2021.01.006

Hatt M, Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28(6):881–93. https://doi.org/10.1109/TMI.2008.2012036.

Article  PubMed  PubMed Central  Google Scholar 

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