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Distortion verification of helical computed tomography for image-guided radiotherapy

Published online by Cambridge University Press:  25 September 2023

Takayuki Harada*
Affiliation:
Department of Radiology, National Hospital Organization Kanazawa Medical Center, 1-1 Shimoishibiki, Kanazawa, Ishikawa 920-8650, Japan Division of Health Science, Graduate School, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa 920-0942, Japan
Akihiro Takemura
Affiliation:
Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa 920-0942, Japan
*
Corresponding author: Takayuki Harada; Email: harada.takayuki.mf@mail.hosp.go.jp

Abstract

Introduction:

In image-guided radiotherapy (IGRT), the imaging conditions of computed tomography (CT) may impact the positioning uncertainty, but verification methods are currently unavailable. This study aimed to propose a validation method for the imaging conditions of helical CT for IGRT. Predicting the impact of image distortion on image guidance may reduce uncertainty in radiotherapy planning.

Methods:

Image guidance was performed on the reference images of four Duracon balls by changing the imaging conditions and the positions on the CT images by helical scanning. The predictors of image guidance error and those of the contour mismatch between the reference and cone-beam CT (CBCT) images were analysed.

Results:

The image guidance error exceeded 1 mm when the contour centre of the ball was shifted by more than 1 mm. The mismatch between the contours of the reference and CBCT images occurred with the imaging conditions wherein the first slice of the ball was distorted.

Conclusions:

Mismatch can be predicted by the coefficient of variation of the radii in the first and centre slices of the ball. Moreover, the image guidance error can be predicted by the contour centre shift of the ball.

Type
Original Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

Arimura, H. Basics of deformable image registration in radiation therapy. Igaku Butsuri 2019; 39 (1): 26. doi: 10.11323/jjmp.39.1_2 Google ScholarPubMed
Japan Society of Medical Physics. Japanese Society of Radiological Technology, Japanese Society for Radiation Oncology. Guidelines for Clinical Implementation of Image-Guided Radiation Therapy. Tokyo: Japan Society of Medical Physics, 2010.Google Scholar
Katsuhiro, I, Yoshihisa, M. Hyojyun X Sen CT Gazo Keisoku. Tokyo: Ohmsha, 2009.Google Scholar
Wang, G, Vannier, MW. The effect of pitch in multislice spiral/helical CT. Med Phys 1999; 26 (12): 26482653. doi: 10.1118/1.598804 CrossRefGoogle ScholarPubMed
Magara, T, Kikumura, R. Evaluation of therapeutic carbon-beam attenuation in inhomogeneous layered phantoms: comparison with the present method using a water phantom. Jpn J Med Phys 2006; 26 (4): 177. doi: 10.11323/jjmp2000.26.4_173 Google ScholarPubMed
Lu, B, Lu, H, Palta, J. A comprehensive study on decreasing the kilovoltage cone-beam CT dose by reducing the projection number. J Appl Clin Med Phys 2010; 11 (3): 3274. doi: 10.1120/jacmp.v11i3.3274 CrossRefGoogle Scholar
Roche, A, Malandain, G, Pennec, X, Ayache, N. The correlation ratio as a new similarity measure for multimodal image registration. In: Wells, WM, Colchester, A, Delp, S (eds). Medical Image Computing and Computer-Assisted Intervention—MICCAI’98. Lecture Notes in Computer Science, Volume 1496. Berlin; Heidelberg: Springer, 1998: 11151124. doi: 10.1007/BFb0056301 CrossRefGoogle Scholar
Hristov, DH, Fallone, BG. A grey-level image alignment algorithm for registration of portal images and digitally reconstructed radiographs. Med Phys 1996; 23 (1): 7584. doi: 10.1118/1.597743 CrossRefGoogle ScholarPubMed
Bissonnette, JP, Balter, PA, Dong, L et al. Quality assurance for image-guided radiation therapy utilizing CT-based technologies: a report of the AAPM TG-179. Med Phys 2012; 39 (4): 19461963. doi: 10.1118/1.3690466 CrossRefGoogle ScholarPubMed
Silver, MD, Taguchi, K, Hein, IA, Chiang, B, Kazama, M, Mori, I. Windmill artifact in multislice helical CT. Proc SPIE Image Process 2003; 5032: 19181927. doi: 10.1117/12.483585 Google Scholar
Kanamori, I. Saishin: X-sen CT No Jissen. Tokyo: Iryoukagakusha, 2006: 63.Google Scholar
Meyer, J, Wilbert, J, Baier, K et al. Positioning accuracy of cone-beam computed tomography in combination with a HexaPOD robot treatment table. Int J Radiat Oncol Biol Phys 2007; 67 (4): 12201228. doi: 10.1016/j.ijrobp.2006.11.010 CrossRefGoogle ScholarPubMed
Sato, K, Kanai, T, Lee, SH et al. Development of a quantitative analysis method for assessing patient body surface deformation using an optical surface tracking system. Radiol Phys Technol 2022; 15: 367378. doi: 10.1007/s12194-022-00676-0 CrossRefGoogle ScholarPubMed