Weryfikacja jakości i parametrów dynamicznego obrazowania na rzecz IGRT – 4D CBCT (Cone Beam CT) i KIM (Kilovoltage Intrafraction Monitoring)

• Autorzy: Oborska-Kumaszyńska Dominika

Warianty tytułu

EN

Verification of the quality and parameters of imaging for IGRT – 4D CBCT (Cone Beam CT) and KIM (Kilovoltage Intrafraction Monitoring)

• Języki publikacji: PL

Abstrakty

PL

Weryfikacja jakości i parametrów dynamicznego obrazowania na rzecz IGRT – 4D CBCT (Cone Beam CT) i KIM (Kilovoltage Intrafraction Monitoring)4D Cone Beam CT (4DCBCT) i Kilovoltage Intrafraction Monitoring (KIM) skorelowane z cyklem oddechowym są wykorzystywane głównie w radioterapii pacjentów z nowotworami płuc (ze szczególnym uwzględnieniem stereotaktycznego napromieniania płuc). Wstępne dane obrazowe pochodzą ze skorelowanej z cyklem oddechowym akwizycji 4DCT i stanowią odniesienie do oceny ruchu zmiany nowotworowej związanego z aktywnością oddechową pacjenta. Umożliwiają lepszą ocenę ilościową GTV niż w przypadku obrazowania 3DCT i ocenę ruchu obszaru tarczowego w całym procesie radioterapii. Obrazowanie 4DCT może być wykorzystywane do oceny ruchu oddechowego, identyfikacji targetów i tworzenia planów radioterapii dla pacjentów napromieniowanych w reżimie swobodnego oddechu oraz radioterapii adaptacyjnej. Techniki radioterapeutyczne oparte o systemy zarządzania ruchem oddechowym (Respiratory Motion Management – RMM) są równie złożone, ponieważ opierają się na różnych rozwiązaniach w zakresie tego zarządzania i są determinowane dużą heterogenicznością między pacjentami w trajektoriach i fazach ruchu zmiany nowotworowej. Zgodnie z raportem TG76 AAPM można podzielić je na kategorie: (i) techniki oceny zakresu ruchu; (ii) techniki wymuszonego płytkiego oddychania; (iii) techniki bramkowania oddechowego; (iv) techniki wstrzymywania oddechu; (v) techniki synchronizacji oddechu. Proces optymalizacji i implementacji systemu RMM do użytku klinicznego powinien obejmować weryfikację: wpływu cyklu oddechowego na dokładności geometrycznej i czasowej 4DCT i 4DCBCT, także względem siebie, jakości obrazu i wpływu na ocenę położenia zmiany nowotworowej, algorytmów rekonstrukcji i sortowania danych obrazowych względem cyklu oddechowego, dawki i metody generowania projekcji.

EN

4D Cone Beam CT (4DCBCT) and Kilovoltage Intrafraction Monitoring (KIM) correlated with the respiratory cycle is mainly used for radiotherapy treatment of patients with lung cancer (particularly stereotactic lung radiotherapy treatments). Initial imaging data come from the 4DCT acquisition correlated with the respiratory cycle. 4DCT images are treated as a reference data to assess the target movement, related to the patient’s respiratory activity. 4D imaging modalities enable a better quantification of GTV than in 3DCT imaging and assessment of the target movement throughout the radiotherapy procedure. 4DCT imaging can be used to assess respiratory movement, identify targets and create radiotherapy plans for patients irradiated in the free breathing regime and adaptive radiotherapy. Radiotherapy techniques based on Respiratory Motion Management (RMM) systems are equally complex because they base on various solutions and are determined by high heterogeneity between patients in the trajectories and phases of the target respiratory movement. According to the TG76 AAPM report, they can be categorized as: (i) motion-encompassing techniques; (ii) forced shallow breathing techniques; (iii) respiratory-gated techniques; (iv) breath-hold techniques; (v) respiratory-synchronisation techniques. The process of optimizing and implementing the RRM system to clinical use should include verification of: the impact of the respiratory cycle on the geometric and time accuracy of 4DCT and 4DCBCT (also in relation to each other), image quality and impact on the assessment of tumour location, algorithms for reconstruction and binning image data in relation to the respiratory cycle, dose and projections generation methods.

Słowa kluczowe

PL

4DCT; 4D CBCT; optymalizacja obrazowania; kontrola jakości; obrazowanie 4D

EN

4DCT; 4D CBCT; optimisation; image quality

• Wydawca: Indygo Zahir Media
• Czasopismo: Inżynier i Fizyk Medyczny
• Rocznik: 2022
• Tom: Vol. 11, nr 6
• Strony: 495--501
• Opis fizyczny: Bibliogr. 30 poz.

Twórcy

autor

Oborska-Kumaszyńska Dominika

• Nuffield Healthcare Cancer Centre London, 49 Parkside, Wimbledon, United Kingdom

Bibliografia

• 1. Task Group 142 report: Quality assurance of medical accelerators.
• 2. J.A. Ng, J.T. Booth, R.T. O’Brien, E. Colvill, C.-Y. Huang, P.R. Poulsen, P.J. Keall: Quality assurance for the clinical implementation of kilovoltage intrafraction monitoring for prostate cancer VMAT, Med. Phys., 41, 2014, 111712 (9pp.).
• 3. S. Thengumpallil et al.: Difference in performance between 3D and 4D CBCT for lung imaging: a dose and image quality analysis, Journal of Applied Clinical Medical Physics, 17, 2016, 97-106.
• 4. T. Reynolds, P. Lim, P.J. Keall, R. O’Brien: Minimizing 4DCBCT imaging dose and scan time with Respiratory Motion Guided 4DCBCT: a pre-clinical investigation, Biomed. Phys. Eng. Express, 7, 2021, 025009.
• 5. L. Santanam, C. Noel, T.R. Willoughby, J. Esthappan, S. Mutic, E.E. Klein, D.A. Low, P.J. Parikh: Quality assurance for clinical implementation of an electromagnetic tracking system, Med. Phys., 36, 2009, 3477-3486.
• 6. P.J. Keall, J.A. Ng, R. O’Brien, E. Colvill, Ch.-Y. Huang, P.R. Poulsen, W. Fledelius, P. Juneja, E. Simpson, L. Bell, F. Alfieri, T. Eade, A. Kneebone, J.T. Booth: The first clinical treatment with kilovoltage intrafraction monitoring (KIM): a real-time image guidance method, Medical Physics, 42, 2015, 354.
• 7. Quality Assurance for the Clinical Implementation of Kilovoltage Intrafraction Monitoring (KIM) in the TROG 17.03 Liver Ablative Radiotherapy with KIM (LARK) Multi-Institutional Clinical Trial AAPM ePoster Library. Sengupta C. 07/12/20; 302177; BReP-SNAP-M-118 Topic: Imaging in Treatment Planning, Image Guidance, and Adaptive Radiation Therapy.
• 8. J.-J. Sonke, L. Zijp, P. Remeijer, M. van Herk: Respiratory correlated cone beam CT, Med Phys., 32(4), 2005, 1176-1186.
• 9. R.A. Sweeney, B. Seubert, S. Stark, V. Homann, G. Müller, M. Flentje, M. Guckenberger: Accuracy and inter-observer variability of 3D versus 4D cone-beam CT based image-guidance in SBRT for lung tumors, Radiat Oncol., 7, 2012, 81.
• 10. H. Iramina, M. Nakamura, T. Mizowaki: Actual delivered dose calculation on intra-irradiation cone-beam computed tomography images: a phantom study, Institute of Physics and Engineering in Medicine, Physics in Medicine & Biology, 66(1), 2021.
• 11. S. Lee, G. Yan, B. Lu, D. Kahler, J.G. Li, S.S. Sanjiv: Impact of Scan-ning Parameters and Breathing Patterns on Image Quality and Accuracy of TumorMotion Reconstruction in 4D CBCT: A Phantom Study, Journal of Applied ClinicalMedical Physics, 16, 2015, 195-212.
• 12. N. Clements, T. Kron, R. Franich, L. Dunn, P. Roxby, Y. Aarons, B. Chesson, S. Siva, D. Duplan, D. Ball: The Effect of Irregular Breathing Patterns onInternal Target Volumes in Four-Dimensional CT and Cone-Beam CT Images inthe Context of Stereotactic Lung Radiotherapy, Medical Physics, 40, 2013.
• 13. T.-Ch. Lee, S.R. Bowen, S. St. James et al.: Accuracy Comparison of 4D Computed Tomography (4DCT) and 4D Cone Beam Computed Tomography (4DCBCT) August 2017International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, 06(03), 2017, 323-335.
• 14. J. Kim et al.: Image quality of 4D in-treatment CBCT acquired during lung SBRT using FFF beam: a phantom study, Radiat Oncol, 15, 2020, 224.
• 15. L. Zijp, J.-J. Sonke, M. van Herk: Extraction of the respiratory signal from sequential thorax Cone-Beam X-ray images, Proc 14th ICCR. Seoul, 2004, 507-509.
• 16. J. Liang, D. Lack, J. Zhou, Q. Liu, I. Grills, D. Yan: Intrafraction 4D-cone beam CT acquired during volumetric arc radiotherapy delivery: kV parameter optimization and 4D motion accuracy for lung stereotactic body radiotherapy (SBRT) patients, Appl Clin Med. Phys., 20, 2019, 10-24.
• 17. S. Thengumpallil et al.: Difference in performance between 3D and 4D CBCT for lung imaging: a dose and image quality analysis, Journal of Applied Clinical Medical Physics, 17, 2016, 97-106.
• 18. Y. Yuasa, T. Shiinoki, R. Onizuka, K. Fujimoto: Estimation of effective imaging dose and excess absolute risk of secondary cancer incidence for four-dimensional cone-beam computed tomography acquisition, J Appl Clin Med Phys, 20, 2019, 57-68.
• 19. B.J. Cooper et al.: Respiratory triggered 4D cone-beam computed tomography: a novel method to reduce imaging computed tomography: a novel method to reduce imaging dose, Med. Phys., 40, 2013, 041901–041901.
• 20. B.J. Cooper et al.: Quantifying the image quality and dose reduction of respiratory triggered 4D cone-beam computed tomography with patient-measured breathing, Phys. Med. Biol., 60, 2015, 9493-9513.
• 21. M.F. Fast et al.: Actively triggered 4d cone-beam CT acquisition, Med. Phys., 40, 2013, 091909.
• 22. R.T. O’Brien et al.: The first implementation of respiratory triggered 4DCBCT on a linear accelerator, Phys. Med. Biol., 61, 2016, 3488-3499.
• 23. R.T. O’Brien et al.: Reducing 4DCBCT imaging time and dose: the first implementation of variable gantry speed 4DCBCT on a linear accelerator, Phys. Med. Biol., 62, 2017, 4300-4317.
• 24. R.A. Sweeney et al.: Accuracy and inter-observer variability of 3D versus 4D cone-beam CT based image-guidance in SBRT for lung tumors, Radiat Oncol., 7, 2012, 81.
• 25. R.T. O’Brien et al.: Optimizing 4DCBCT projection allocation to respiratory bins, Phys. Med. Biol., 59, 2014, 5631-5649.
• 26. R.T. O’Brien et al.: Respiratory Motion Guided four dimensional cone beam computed tomography: encompassing irregular breathing, Phys. Med. Biol., 59, 2014, 579-595.
• 27. R.T. O’Brien, B.J. Cooper, P.J. Keall: Optimizing 4D cone beam computed tomography acquisition by varying the gantry velocity and projection time interval, Phys. Med. Biol., 58, 2013, 1705-1723.
• 28. T. Reynolds, P. Lim, P.J. Keall, R. O’Brien: Minimizing 4DCBCT imaging dose and scan time with Respiratory Motion Guided 4DCBCT: a pre-clinical investigation, Biomed Phys Eng., 7(2), 2021.
• 29. L. Shi, S. Han, J. Zhao, Z. Kuang, W. Jing, Y. Cui, Z. Zhu: Respiratory Prediction Based on Multi-Scale Temporal Convolutional Network for Tracking Thoracic Tumor Movement, Front Oncol., https://doi.org/10.3389/fonc.2022.884523.
• 30. Task Group 76, AAPM, Report no 91, The Management of Respiratory Motion in Radiation Oncology, 2006.