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Analysis of Dose Calculation Accuracy in Cone Beam Computed Tomography with Various Amount of Scattered Photon Contamination

DOI: 10.4236/ijmpcero.2017.63022, PP. 233-251

Keywords: Cone Beam Computed Tomography, Scattered Photon, Dose Calculation, Cupping Artifact

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Abstract:

Cone-beam computed tomography (CBCT) images have inaccurate CT numbers because of scattered photons. Thus, quantitative analysis of scattered photons that affect an electron density (ED) curve and calculated doses may be effective information to achieve CBCT-based radiation treatment planning. We quantitatively evaluated the effect of scattered photons on the accuracy of dose calculations from a lung image. The Monte Carlo method was used to calculate CBCT projection data, and we made two calibration curves for conditions with or without scattered photons. Moreover, we applied cupping artifact correction and evaluated the effects on image uniformity and dose calculation accuracy. Dose deviations were compared with those of conventional CT in conventional and volumetric intensity modulated arc therapy (VMAT) planning by using γ analysis and dose volume histogram (DVH) analysis. We found that cupping artifacts contaminated the scattered photons, and the γ analysis showed that the dose distribution was most decreased for a scattered photon ratio of 40%. Cupping artifact correction significantly improved image uniformity; therefore, ED curves were near ideal, and the pass rate results were significantly higher than those associated with the scattered photon effect in 65.1% and 78.4% without correction, 99.5% and 97.7% with correction, in conventional and VMAT planning, respectively. In the DVH analysis, all organ dose indexes were reduced in the scattered photon images, but dose index error rates with cupping artifact correction were improved within approximately 10%. CBCT image quality was strongly affected by scattered photons, and the dose calculation accuracy based on the CBCT image was improved by removing cupping artifacts caused by the scattered photons.

References

[1]  Baymey, B.M., Lee, R.J., Handrahan, D., Welsh, K.T., Cook, J.T. and Sause, W.T. (2011) Image-Guided Radiotherapy (IGRT) for Prostate Cancer Comparing KV Imaging of Fiducial Markers with Cone Beam Computed Tomography (CBCT). International Journal of Radiation Oncology Biology Physics, 80, 301-305.
https://doi.org/10.1016/j.ijrobp.2010.06.007
[2]  Ding, G.X., Duggan, D.M., Coffey, C.W., Deeley, M., Hallahan, D.E., Cmelak, A. and Malcolm, A. (2004) A Study on Adaptive IMRT Treatment Planning Using KV Cone-Beam CT. Radiotherapy and Oncology, 85, 116-125.
https://doi.org/10.1016/j.radonc.2007.06.015
[3]  Schwartz, D.L. and Dong, L. (2007) Adaptive Radiation Therapy for Head and Neck Cancer-Can an Old Goal Evolve into a New Standard? Journal of Oncology, 2011, Article ID 690595.
http://dx.doi.org/10.1155/2011/690595
[4]  Richter, A., Hu, Q., Steglich, D., Baier, K., Wilbert, J., Guckenberger, M. and Flentje, M. (2008) Investigation of the Usability of Conebeam CT Data Sets for Doe Calculation. Radiation Oncology, 3, 42.
http://doi.org/10.1186/1748-717X-3-42
[5]  Hu, W., Ye, J., Wang, J., Ma, X. and Zhang, Z. (2010) Use of Kilovoltage X-Ray Volume Imaging in Patient Dose Calculation for Head-and-Neck and Partial Brain Radiation Therapy. Radiation Oncology, 5, 29.
http://doi.org/10.1186/1748-717X-5-29
[6]  Rong, Y., Smilowitz, J., Tewatia, D., Tomé, W.A. and Paliwal, B. (2010) Dose Calculation on KV Cone Beam CT Images: An Investigation of the Hu-Density Conversion Stability and Dose Accuracy Using the Site-Specific Calibration. Medical Dosimetry, 35, 195-207.
https://doi.org/10.1016/j.meddos.2009.06.001
[7]  Fotina, I., Hopfgartner, J., Stock, M., Steininger, T., Lütgendorf-Caucig, C. and Georg, D. (2012) Feasibility of CBCT-Based Dose Calculation: Comparative Analysis of HU Adjustment Techniques. Radiotherapy and Oncology, 104, 249-256.
https://doi.org/10.1016/j.radonc.2012.06.007
[8]  Onozato, Y., Kadoya, N., Fujita, Y., Arai, K., Takeda, K., Kichi, K., Umezawa, R., Matsushita, H. and Jingu, K. (2014) Evaluation of on-Board KV Cone Beam Computed Tomography-Based Dose Calculation with Deformable Image Registration Using Hounsfield Unit Modifications. International Journal of Radiation Oncology Biology Physics, 89, 416-423.
https://doi.org/10.1016/j.ijrobp.2014.02.007
[9]  Huang, P., Yu, G., Chen, J., Ma, C., Yin, Y., Liang, Y., Li, H. and Li, D. (2017) Investigation of Dosimetric Variations of Liver Radiotherapy Using Deformable Registration of Planning CT and Cone-Beam CT. Journal Applied Clinical Medical Physics, 18, 66-75.
https://doi.org/10.1002/acm2.12008
[10]  Zhu, L., Xie, Y.Q., Wang, J. and Xing, L. (2009) Scatter Correction for Cone-Beam CT in Radiation Therapy. Medical Physics, 36, 2258-2268.
https://doi.org/10.1118/1.3130047
[11]  Maltz, J.S., Gangadharan, B., Bose, S., Hristov, D.H., Faddegon, B.A., Paidi, A. and Bani-Hashemi, A.R. (2008) Algorithm for X-Ray Scatter, Beam-Hardening, and Beam Profile Correction in Diagnostic (Kilovoltage) and Treatment (Megavoltage) Cone Beam CT. IEEE Transactions on Medical Imaging, 27, 1791-1810.
https://doi.org/10.1109/TMI.2008.928922
[12]  Sun, M. and Star-Lack, J.M. (2010) Improved Scatter Correction Using Adaptive Scatter Kernel Superposition. Physics in Medicine and Biology, 21, 6695-6720.
https://doi.org/10.1088/0031-9155/55/22/007
[13]  Thing, R.S., Bernchou, U., Mainegra-Hing, E. and Brink, C. (2013) Patient-Specific Scatter Correction in Clinical Cone Beam Computed Tomography Imaging Made Possible by the Combination of Monte Carlo Simulations and a Ray Tracing Algorithm. Acta Oncologica, 52, 1477-1483.
https://doi.org/10.3109/0284186X.2013.813641
[14]  Jarry, G., Graham, S.A., Moseley, D.J., Jaffray, D.J., Siewerdsen, J.H. and Verhaegen, F. (2006) Characterization of Scattered Radiation in kV CBCT Images Using Monte Carlo Simulations. Medical Physics, 33, 4320-4329.
https://doi.org/10.1118/1.2358324
[15]  Matsumoto, M. and Nishimura, T. (1998) Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator. ACM Transactions on Modeling and Computer Simulation, 8, 3-30.
https://doi.org/10.1145/272991.272995
[16]  GAMMEX (2004) Tissue Characterization Phantom Model 467 User’s Guide. GAMMEX, Middleton, WI.
[17]  Feldkamp, L.A., Davis, L.C. and Kress, J.W. (1984) Practical Cone-Beam Algorithm. Journal of the Optical Society of America A, 1, 612-619.
https://doi.org/10.1364/JOSAA.1.000612
[18]  Siewerdsen, J.H., Moseley, D.J., Bakhtiar, B., Richard, S. and Jaffray, D.A. (2004) The Influence of Antiscatter Grids on Soft-Tissue Detectability in Cone-Beam CT with Flat-Panel Detectors. Medical Physics, 31, 3506-3520.
https://doi.org/10.1118/1.1819789

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