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The Crucial Role of the Establishment of Computed Tomography Density Conversion Tables for Treating Brain or Head/Neck Tumors

  • Yang, Shu-Chin (Department of Radiation Oncology, Chi Mei Medical Center) ;
  • Lo, Su-Hua (Chiu Ho Medical System Co., LTD) ;
  • Shie, Li-Tsuen (Department of Radiation Oncology, Chi Mei Medical Center) ;
  • Lee, Sung-Wei (Department of Radiation Oncology, Chi Mei Medical Center) ;
  • Ho, Sheng-Yow (Department of Radiation Oncology, Chi Mei Medical Center)
  • Received : 2021.05.17
  • Accepted : 2021.08.06
  • Published : 2021.09.30

Abstract

Purpose: The relationship between computed tomography (CT) number and electron density (ED) has been investigated in previous studies. However, the role of these measures for guiding cancer treatment remains unclear. Methods: The CT number was plotted against ED for different imaging protocols. The CT number was imported into ED tables for the Pinnacle treatment planning system (TPS) and was used to determine the effect on dose calculations. Conversion tables for radiation dose calculations were generated and subsequently monitored using a dosimeter to determine the effect of different CT scanning protocols and treatment sites. These tables were used to retrospectively recalculate the radiation therapy plans for 41 patients after an incorrect scanning protocol was inadvertently used. The gamma index was further used to assess the dose distribution, percentage dose difference (DD), and distance-to-agreement (DTA). Results: For densities <1.1 g/cm3, the standard deviation of the CT number was ±0.6% and the greatest variation was noted for brain protocol conditions. For densities >1.1 g/cm3, the standard deviation of the CT number was ±21.2% and the greatest variation occurred for the tube voltage and head and neck (H&N) protocol conditions. These findings suggest that the factors most affecting the CT number are the tube voltage and treatment site (brain and H&N). Gamma index analyses for the 41 retrospective clinical cases, as well as brain metastases and H&N tumors, showed gamma passing rates >90% and <90% for the passing criterion of 2%/2 and 1%/1 mm, respectively. Conclusions: The CT protocol should be carefully decided for TPS. The correct protocol should be used for the corresponding TPS based on the treatment site because this especially affects the dose distribution for brain metastases and H&N tumor recognition. Such steps could help reduce systematic errors.

Keywords

Acknowledgement

The authors are greatly thankful to Mrs. Lo of Chiu Ho Medical System Co., LTD, Taiwan, for the knowledge on Pinnacle TPS. This study was supported by the Chi Mei Medical Center, Liouying, from January 2020 (grants CLFHR10914).

References

  1. Das IJ, Cheng CW, Cao M, Johnstone PA. Computed tomography imaging parameters for inhomogeneity correction in radiation treatment planning. J Med Phys. 2016;41:3-11. https://doi.org/10.4103/0971-6203.177277
  2. Geise RA, McCullough EC. The use of CT scanners in megavoltage photon-beam therapy planning. Radiology. 1977;124:133-141. https://doi.org/10.1148/124.1.133
  3. Goodsitt MM, Christodoulou EG, Larson SC. Accuracies of the synthesized monochromatic CT numbers and effective atomic numbers obtained with a rapid kVp switching dual energy CT scanner. Med Phys. 2011;38:2222-2232. https://doi.org/10.1118/1.3567509
  4. Lv P, Liu J, Zhang R, Jia Y, Gao J. Combined use of automatic tube voltage selection and current modulation with iterative reconstruction for CT evaluation of small hypervascular hepatocellular carcinomas: effect on lesion conspicuity and image quality. Korean J Radiol. 2015;16:531-540. https://doi.org/10.3348/kjr.2015.16.3.531
  5. Guan H, Yin FF, Kim JH. Accuracy of inhomogeneity correction in photon radiotherapy from CT scans with different settings. Phys Med Biol. 2002;47:N223-N231. https://doi.org/10.1088/0031-9155/47/17/402
  6. Davis AT, Palmer AL, Nisbet A. Can CT scan protocols used for radiotherapy treatment planning be adjusted to optimize image quality and patient dose? A systematic review. Br J Radiol. 2017;90:20160406. https://doi.org/10.1259/bjr.20160406
  7. Cropp RJ, Seslija P, Tso D, Thakur Y. Scanner and kVp dependence of measured CT numbers in the ACR CT phantom. J Appl Clin Med Phys. 2013;14:4417.
  8. Skrzynski W, Zielinska-Dabrowska S, Wachowicz M, Slusarczyk-Kacprzyk W, Kukolowicz PF, Bulski W. Computed tomography as a source of electron density information for radiation treatment planning. Strahlenther Onkol. 2010;186:327-333. https://doi.org/10.1007/s00066-010-2086-5
  9. du Plessis FC, Willemse CA, Lotter MG, Goedhals L. Comparison of the Batho, ETAR and Monte Carlo dose calculation methods in CT based patient models. Med Phys. 2001;28:582-589. https://doi.org/10.1118/1.1357223
  10. Cozzi L, Fogliata A, Buffa F, Bieri S. Dosimetric impact of computed tomography calibration on a commercial treatment planning system for external radiation therapy. Radiother Oncol. 1998;48:335-338. https://doi.org/10.1016/S0167-8140(98)00072-3
  11. Zurl B, Tiefling R, Winkler P, Kindl P, Kapp KS. Hounsfield units variations: impact on CT-density based conversion tables and their effects on dose distribution. Strahlenther Onkol. 2014;190:88-93. https://doi.org/10.1007/s00066-013-0464-5
  12. Gorlitz E. Dosimetrische und verfahrenstechnische Untersuchungen zur Qualitatssicherung eines Bestrahlungsplanungsprogramms [Thesis]. Hambur: Universitat Hamburg; 2006.
  13. Low DA, Harms WB, Mutic S, Purdy JA. A technique for the quantitative evaluation of dose distributions. Med Phys. 1998;25:656-661. https://doi.org/10.1118/1.598248
  14. Cheng A, Harms WB Sr, Gerber RL, Wong JW, Purdy JA. Systematic verification of a three-dimensional electron beam dose calculation algorithm. Med Phys. 1996;23:685-693. https://doi.org/10.1118/1.597714
  15. Shiu AS, Tung S, Hogstrom KR, Wong JW, Gerber RL, Harms WB, et al. Verification data for electron beam dose algorithms. Med Phys. 1992;19:623-636. https://doi.org/10.1118/1.596808
  16. William BH, Daniel AL, James AP. A quantitative software analysis tool for verifying 3-d dose calculation programs. Int J Radiat Oncol Biol Phys. 1994;30(Suppl 1):187. https://doi.org/10.1016/0360-3016(94)90685-8
  17. Ezzell GA, Galvin JM, Low D, Palta JR, Rosen I, Sharpe MB, et al. Guidance document on delivery, treatment planning, and clinical implementation of IMRT: report of the IMRT Subcommittee of the AAPM Radiation Therapy Committee. Med Phys. 2003;30:2089-2115. https://doi.org/10.1118/1.1591194
  18. Fraass B, Doppke K, Hunt M, Kutcher G, Starkschall G, Stern R, et al. American Association of Physicists in Medicine Radiation Therapy Committee Task Group 53: quality assurance for clinical radiotherapy treatment planning. Med Phys. 1998;25:1773-1829. https://doi.org/10.1118/1.598373
  19. Thomas SJ. Relative electron density calibration of CT scanners for radiotherapy treatment planning. Br J Radiol. 1999;72:781-786. https://doi.org/10.1259/bjr.72.860.10624344
  20. Rhee DJ, Kim S, Jeong DH, Moon YM, Kim JK. Effects of the difference in tube voltage of the CT scanner on dose calculation. J Korean Phys Soc. 2015;67:123-128. https://doi.org/10.3938/jkps.67.123
  21. Van Dyk J, Barnett RB, Cygler JE, Shragge PC. Commissioning and quality assurance of treatment planning computers. Int J Radiat Oncol Biol Phys. 1993;26:261-273. https://doi.org/10.1016/0360-3016(93)90206-b
  22. Gershkevitsh E, Schmidt R, Velez G, Miller D, Korf E, Yip F, et al. Dosimetric verification of radiotherapy treatment planning systems: results of IAEA pilot study. Radiother Oncol. 2008;89:338-346. https://doi.org/10.1016/j.radonc.2008.07.007
  23. Kilby W, Sage J, Rabett V. Tolerance levels for quality assurance of electron density values generated from CT in radiotherapy treatment planning. Phys Med Biol. 2002;47:1485-1492. https://doi.org/10.1088/0031-9155/47/9/304
  24. Venselaar J, Welleweerd H, Mijnheer B. Tolerances for the accuracy of photon beam dose calculations of treatment planning systems. Radiother Oncol. 2001;60:191-201. https://doi.org/10.1016/S0167-8140(01)00377-2
  25. Yang SC, Chang YL, Chang LY, Ho SY. The effectiveness of skill learning assessment using direct observation of procedural skills (DOPS) in the training of post-graduate training year and internship students of radiological technologist. C J Radiologic Tech. 2018;42:89-96.