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Synthetic CT in assessment of anatomical and dosimetric variations in radiotherapy - procedure validation

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Introduction: One of many procedures to control the quality of radiotherapy is daily imaging of the patient's anatomy. The CBCT (Cone Beam Computed Tomography) plays an important role in patient positioning, and dose delivery monitoring. Nowadays, CBCT is a baseline for the calculation of fraction and total dose. Thus, it provides the potential for more comprehensive monitoring of the delivered dose and adaptive radiotherapy. However, due to the poor quality and the presence of numerous artifacts, the replacement of the CBCT image with the corrected one is desired for dose calculation. The aim of the study was to validate a method for generating a synthetic CT image based on deformable image registration. Material and methods: A Head & Torso Freepoint phantom, model 002H9K (Computerized Imaging Reference Systems, Norfolk, USA) with inserts was imaged with CT (Computed Tomography). Then, contouring and treatment plan were created in Eclipse (Varian Medical Systems, Palo Alto, CA, USA) treatment planning system. The phantom was scanned again with the CBCT. The planning CT was registered and deformed to the CBCT, resulting in a synthetic CT in Velocity software (Varian Medical Systems, Palo Alto, CA, USA). The dose distribution was recalculated based on the created CT image. Results: Differences in structure volumes and dose statistics calculated both on CT and synthetic CT were evaluated. Discrepancies between the original and delivered plan from 0.0 to 2.5% were obtained. Dose comparison was performed on the DVH (Dose-Volume Histogram) for all delineated inserts. Conclusions: Our findings suggest the potential utility of deformable registration and synthetic CT for providing dose reconstruction. This study reports on the limitation of the procedure related to the limited length of the CBCT volume and deformable fusion inaccuracies.
Rocznik
Strony
185--192
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
  • Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
autor
  • Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
  • Medical Physics Department, Institute of Physics, University of Silesia, Chorzów, Poland
  • Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
  • Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
  • Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
Bibliografia
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  • 3. Goff PH, Harrison LB, Furhang E et al. 2D kV orthogonal imaging with fiducial markers is more precise for daily image guided alignments than soft-tissue cone beam computed tomography for prostate radiation therapy. Adv Radiat Oncol 2017;2(3):420-428
  • 4. Goyal S, Kataria T, Image Guidance in Radiation Therapy: Techniques and Applications. Radiol Res Prac 2014;Article ID 705604, https://doi.org/10.1155/2014/705604
  • 5. Li G, Yang TJ, Furtado H et al. Clinical Assessment of 2D/3D Registration Accuracy in 4 Major Anatomic Sites Using On-Board 2D Kilovoltage Images for 6D Patient Setup. Technol Cancer Res Treat 2015;14(3):305-324
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  • 7. Lindfors N, Lund H, Johansson H et al. Influence of patient position and other inherent factors on image quality in two different cone beam computed tomography (CBCT) devices. Eur J Radiol Open 2017:132-137
  • 8. Keall PJ, Hsu A, Xing L. Image-Guided Adaptive Radiotherapy, Leibel and Phillips Textbook of Radiation Oncology, wyd. Third Edition 2010.
  • 9. Li X. A., Adaptive Radiation Therapy in: Hendee W., Imaging in medical diagnosis and therapy, CRC Press Taylor and Francis Group 2011.
  • 10. Sonke JJ, Aznar M, Rasch C. Adaptive Radiotherapy for Anatomical Changes. Semin Radiat Oncol 2019;29(3):245-257
  • 11. Feldkamp L, Davis L, Kress J. Practical cone-beam algorithm. J Opt Soc Am A 1984;1(6):612-619
  • 12. Srinivasan K, Mohammadi M, Shepherd J. Applications of linac-mounted kilovoltage Cone-beam Computed Tomography on modern radiation therapy: A Review. Pol J Radiol 2014:79:181-93
  • 13. Mao W, Liu C, Gardner SJ et al. Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT. Technol Cancer Res Treat 2019:18: 1533033818823054
  • 14. Stock M, Pasler M, Birkfellner W et al. Image quality and stability of image-guided radiotherapy (IGRT) devices: A comparative study. Radiother Oncol 2009;93(1)
  • 15. Moteabbed M, Sharp G, Wang Y et al. Validation of a deformable image registration technique for cone beam CT-based dose verification. Med Phys 2015;42(1):195-205
  • 16. Zoellner C, Rit S, Kurz C et al. Decomposing a prior-CT-based cone-beam CT projection correction algorithm into scatter and beam hardening components. Phys Imag Radiat Oncol 2017;3:49-52
  • 17. Schulze R, Heil U, Gross D et al. Artefacts in CBCT: a review. Dentomaxillofac Radiol 2011;40(5):265-273
  • 18. Kalender WA, Kyriakou Y. Flat-detector Computed Tomography (FD-CT). Eur Radiol 2007;17(11):2767-2779
  • 19. Marchant T, Joshi K, Moore C. Accuracy of radiotherapy dose calculations based on cone-beam CT: comparison of deformable registration and image correction based methods. Phys Med Biol 2018;63(6)
  • 20. Yuan Z, Rong Y, Benedict SH et al. “Dose of the day” based on cone beam computed tomography and deformable image registration for lung cancer radiotherapy. J Appl Clin Med Phys 2020;21(1):88-94
  • 21. Veiga C, McClelland J, Moinuddin S et al. Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CTto-CBCT deformable registration for “dose of the day” calculations. Med Phys 2014;41(3)
  • 22. Oh S, Kim S. Deformable image registration in radiation therapy. Radiat Oncol J 2017;35(2):101-111
  • 23. Rigaud B, Simon A, Castelli J et al. Deformable image registration for radiation therapy: principle, methods, applications and evaluation. Acta Oncol 2019;58(9):1225-1237
  • 24. Weistrand O, Svensson S. The ANACONDA algorithm for deformable image registration in radiotherapy. Med Phys 2015;42(1):40-53
  • 25. Brock KK, Mutic S, McNutt TR et al. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132. Med Phys 2017;44(7)
  • 26. Brock K. K., Hawkins M. A., Eccles C. L. et al., Improving image-guided target localization through deformable registration. Acta Oncol 2008;47(7):1279-1285
  • 27. Thirion J. Image matching as a diffusion process: an analogy with Maxwell's demons. Medical Image Analysis 1998;2(3):243-260
  • 28. Wang H, Dong L, O'Daniel J et al. Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy. Phys Med Biol;50(12):2887-2905
  • 29. Lawson JD, Schreibmann E, Jani AB et al. Quantitative evaluation of a cone-beam computed tomography–planning computed tomography deformable image registration method for adaptive radiation therapy. Journal of Applied Clinical Medical Physics 2007;8(4):96-113
  • 30. Velocity 4.0 Instructions for use. Varian Medical Systems 2018
  • 31. Niu T, Sun M, Star-Lack J et al. Shading correction for on‐board cone‐beam CT in radiation therapy using planning MDCT images. Med Phys 2010;37(10):5395-5406
  • 32. Kurz C, Dedes G, Resch A et al. Comparing cone-beam CT intensity correction methods for dose recalculation in adaptive intensitymodulated photon and proton therapy for head and neck cancer. Acta Oncol 2015;54(9):1651-1657
  • 33. Laundry G, Dedes G, Zoellner C et al. Phantom Based Evaluation of CT to CBCT Image Registration for Proton Therapy Dose Recalculation. Phys Med Biol 2014;60(2):595-613
  • 34. Kurz C, Kamp F, Park Y-K et al. Investigating deformable image registration and scatter correction for CBCT‐based dose calculation in adaptive IMPT. Med Phys 2016;43(10):5635-5646
  • 35. Thing RS, Bernchou U, Mainegra-Hing E et al. Hounsfield unit recovery in clinical cone beam CT images of the thorax acquired for image guided radiation therapy. Phys Med Biol 2016;61(15):5781-5802
  • 36. Thummerer A, Zaffino P, Meijers A et al. Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy. Phys Med Biol 2020;65(9)
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-477ef098-3958-4e31-a236-7117b5bce3ad
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