Document Type: Original Research


1 PhD, Department of Physics, Ranchi University, Ranchi- 834008, Jharkhand, India

2 DipRP, Research Scholars, University Department of Physics, Ranchi University, Ranchi- 834008, Jharkhand State, India

3 PhD, Department of Radiotherapy, AIIMS, Bhopal- 462020, Madhya Pradesh, India


Background: Nowadays, advanced radiotherapy equipment includes algorithms to calculate dose. The verification of the calculated doses is important to achieve accurate results. Mostly homogeneous dosimetric phantoms are available commercially which do not mimic the actual patient anatomy; therefore, an indigenous heterogeneous pelvic phantom mimicking actual human pelvic region has been used to verify the doses calculated by different algorithms.
Objective: This study aims to compare the planed dose using different algorithms with measured dose using an indigenous heterogeneous pelvic phantom.
Material and Methods: In this experimental study, various three dimensional conformal radiotherapy (3D-CRT) plans were made using different doses calculated by algorithms. The plans were delivered by medical linear accelerator and doses were measured by ion chamber placed in the indigenous pelvic phantom. Planned and measured doses were compared with together and analyzed.
Results: The relative electron densities of different parts in the pelvic phantom were found to be in good agreement with that of actual pelvic parts, including bladder, rectum, fats and bones. The highest percentage deviations between planned and measured dose were calculated in the single field for Superposition algorithm (3.09%) and single field with 45˚wedge for Superposition (3.04%). The least percentage deviation was calculated in the opposite field for Convolution which was - 0.08%. The results were within the range of ±5% as recommended by International Commission on Radiation Units and Measurement.
Conclusion: The cost-effective indigenous heterogeneous pelvic phantom has the density pattern similar to the actual pelvic region; thus, it can be used for routine patient-specific quality assurance.


  1. Brahme A. Dosimetric precision requirements in radiation therapy. Acta Radiol Oncol. 1984;23:379-91. doi: 10.3109/02841868409136037. PubMed PMID: 6095609.
  2. Mijnheer BJ, Battermann JJ, Wambersie A. What degree of accuracy is required and can be achieved in photon and neutron therapy? Radiother Oncol. 1987;8:237-52. doi: 10.1016/s0167-8140(87)80247-5. PubMed PMID: 3107087.
  3. Nette P, Svensson H. Radiation dosimetry in health care: expanding the reach of global networks. IAEA BULLETIN. 1994;36:33-36.
  4. Agence internationale de l’énergie atomique. Radiation oncology physics: A handbook for teachers and students. Vienna: International Atomic Energy Agency; 2005.
  5. Van Dyk J, Barnett RB, Cygler JE, Shragge PC. Commissioning and quality assurance of treatment planning computers. Int J Radiat Oncol Biol Phys. 1993;26:261-73. doi: 10.1016/0360-3016(93)90206-b. PubMed PMID: 8491684.
  6. Fraass BA. Quality assurance for 3-D treatment planning. Teletherapy: Present and Future. 1996:253-318.
  7. 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;25:1773-829. doi: 10.1118/1.598373. PubMed PMID: 9800687.
  8. Brahme A. Design principles and clinical possibilities with a new generation of radiation therapy equipment. A review. Acta Oncol. 1987;26:403-12. doi: 10.3109/02841868709113708. PubMed PMID: 3328620.
  9. Seco J, Evans PM. Assessing the effect of electron density in photon dose calculations. Med Phys. 2006;33:540-52. doi: 10.1118/1.2161407. PubMed PMID: 16532961.
  10. Animesh. Advantages of multiple algorithm support in treatment planning system for external beam dose calculations. J Cancer Res Ther. 2005;1:12-20. PubMed PMID: 17998620.
  11. Mackie TR, Scrimger JW, Battista JJ. A convolution method of calculating dose for 15-MV x rays. Med Phys. 1985;12:188-96. doi: 10.1118/1.595774. PubMed PMID: 4000075.
  12. Boyer AL, Zhu YP, Wang L, Francois P. Fast Fourier transform convolution calculations of x-ray isodose distributions in homogeneous media. Med Phys. 1989;16:248-53. doi: 10.1118/1.596375. PubMed PMID: 2497314.
  13. Miften MM, Beavis AW, Marks LB. Influence of dose calculation model on treatment plan evaluation in conformal radiotherapy: a three-case study. Med Dosim. 2002;27:51-7. doi: 10.1016/s0958-3947(02)00088-2. PubMed PMID: 12019966.
  14. Muralidhar KR, Murthy NP, Raju AK, Sresty N. Comparative study of convolution, superposition, and fast superposition algorithms in conventional radiotherapy, three-dimensional conformal radiotherapy, and intensity modulated radiotherapy techniques for various sites, done on CMS XIO planning system. J Med Phys. 2009;34:12-22. doi: 10.4103/0971-6203.48716. PubMed PMID: 20126561; PubMed Central PMCID: PMCPMC2804143.
  15. International Atomic Energy Agency. 398. Absorbed dose determination in external beam radiotherapy: An International Code of Practice for Dosimetry based on standards of absorbed dose to water. Vienna: International Atomic Energy Agency. 2000. p. 1011-4289.
  16. Gurjar OP, Mishra SP, Bhandari V, Pathak P, Patel P, Shrivastav G. Radiation dose verification using real tissue phantom in modern radiotherapy techniques. J Med Phys. 2014;39:44-9. doi: 10.4103/0971-6203.125504. PubMed PMID: 24600172; PubMed Central PMCID: PMCPMC3931228.
  17. Shrotriya D, Yadav RS, Srivastava RN. Design and Development of an Indigenous in-house Tissue-Equivalent Female Pelvic Phantom for Radiological Dosimetric Applications. Iranian Journal of Medical Physics. 2018;15:200-5.
  18. International Commission on Radiation Units and Measurements. Dose specification for reporting external beam therapy with photons and electrons. ICRU Report 29. Baltimore: Bethesda; 1978.
  19. Akpochafor MO, Madu CB, Habeebu MY, Omojola AD, Adeneye SO, Aweda MA. Development of pelvis phantom for verification of treatment planning system using convolution, fast superposition, and superposition algorithms. Journal of Clinical Sciences. 2017;14:74. doi: 10.4103/jcls.jcls_78_16.