THE COMPARISON OF 2D DOSE PATIENT-SPECIFIC QUALITY ASSURANCE BETWEEN MONTE CARLO-CONVOLUTION AND MODIFIED CLARKSON INTEGRATION ALGORITHM

Akbar Azzi; Supriyanto Ardjo Pawiro; Terry Mart

doi:10.21009/SPEKTRA.082.01

Akbar Azzi Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Supriyanto Ardjo Pawiro Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Terry Mart Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia

DOI: https://doi.org/10.21009/SPEKTRA.082.01

Keywords: patient-specific quality assurance, log files, 2D dose calculation algorithm

Abstract

A sophisticated machine of radiotherapy treatment process follows the complexity of the quality assurance (QA) measurement. Non-measurement QA becomes one of the solutions to reduce the medical physicists’ workload. However, this method has not been clinically established. This study compared two non-measurement methods of patient-specific quality assurance (PSQA) to find the feasible algorithm for the adaptive radiotherapy process. Monte Carlo-based (MC) PSQA used a phase space file of the medical linear accelerator (Linac) to obtain the photon energy fluence and forward projected to the isoplane. In contrast, Modified Clarkson Integration-based (MCI) used a non-uniform fluence map in the isoplane. For the modulated intensity, we used a pair of the dynamic log files of the multileaf-collimator (MLC) and then employed them in the algorithms. The dose distributions of MC and MCI methods were compared to the treatment planning system (TPS) using gamma index analysis. We found that the gamma pass rates (GPR) for MC-TPS and MCI-TPS were 99.54% and 99.57%, respectively. Further, the dose distribution in the off-axis region for the MCI method showed lesser accuracy due to the higher secondary dose contribution. The linac log file information can be used and calculated into a 2D dose distribution using both MC and MCI methods, providing high-accuracy results.

References

A. Yazdanian, “Oncology Information System: A Qualitative Study to Identify Cancer Patient Care Workflows,” Journal Of Medicine and Life, vol. 13, no. 4, pp. 469-474, 2020, doi: 10.25122/jml-2019-0169.

S. Kirrmann et al., “Visualization of data in radiotherapy using web services for optimization of workflow,” Radiation Oncology, vol. 10, no. 1, pp. 1-12, 2015, doi: 10.1186/s13014-014-0322-3.

A. Chendi et al., “EPID-based 3D dosimetry for pre-treatment FFF VMAT stereotactic body radiotherapy plan verification using dosimetry CheckTM,” Physica Medica, vol. 81, pp. 227-236, 2021, doi: 10.1016/j.ejmp.2020.12.014.

P. D. H. Wall et al., “Prospective Clinical Validation of Virtual Patient-Specific Quality Assurance of Volumetric Modulated Arc Therapy Radiation Therapy Plans,” International Journal of Radiation Oncology, Biology, Physics, vol. 113, no. 5, pp. 1091-1102, 2022, doi: 10.1016/j.ijrobp.2022.04.040.

J. W. Lee et al., “Inverse verification of the dose distribution for intensity modulated radiation therapy patient-specific quality assurance using dynamic MLC log files,” Journal Korean Physics Social, vol. 55, no. 4, pp. 1649-1656, 2009, doi: 10.3938/jkps.55.1649.

C. Han et al., “Cross verification of independent dose recalculation, log files based, and phantom measurement-based pretreatment quality assurance for volumetric modulated arc therapy,” Journal Applied Clinical Medical Physics, vol. 21, no. 11, pp. 98-104, 2020, doi: 10.1002/acm2.13036.

Y. Katsuta et al., “Patient-Specific Quality Assurance Using Monte Carlo Dose Calculation and Elekta Log Files for Prostate Volumetric-Modulated Arc Therapy,” Technology in Cancer Research & Treatment, vol. 16, no. 6, pp. 1220-1225, 2017, doi: 10.1177/1533034617745250.

W. Lu et al., “Adaptive fractionation therapy: I. Basic concept and strategy,” Physics in Medicine & Biology, vol. 53, no. 19, pp. 5495-5511, 2008, doi: 10.1088/0031-9155/53/19/015.

L. Belshaw et al., “Adaptive radiotherapy for head and neck cancer reduces the requirement for rescans during treatment due to spinal cord dose,” Radiation Oncology, vol. 14, pp. 1-7, 2019, doi: 10.1186/s13014-019-1400-3.

W. Z. Chen et al., “Impact of dose calculation algorithm on radiation therapy,” World Journal of Radiology, vol. 6, no. 11, p. 874, 2014, doi: 10.4329/wjr.v6.i11.874.

S. Mubarok, W. E. Wahyu and P. A. Supriyanto, “2D dose reconstruction of IMRT patient-specific QA based on log file,” Radiation Physics and Chemistry, vol. 166, p. 108473, 2020, doi: 10.1016/j.radphyschem.2019.108473.

M. R. McEwen, I. Kawrakow and C. K. Ross, “The effective point of measurement of ionization chambers and the build-up anomaly in MV x-ray beams,” Medical Physics, vol. 35, no. 3, pp. 950-958, 2008, doi: 10.1118/1.2839329.

E. Tonkopi et al., “Influence of ion chamber response on in-air profile measurements in megavoltage photon beams,” Medical Physics, vol. 32, no. 9, pp. 2918-2927, 2005, doi: 10.1118/1.2011090.

S. A. Pawiro, A. Azzi and D. S. Soejoko, “A Monte Carlo Study of Photon Beam Characteristics on Various Linear Accelerator Filters,” Journal of Biomedical Physics & Engineering, vol. 10, no. 5, p. 613, 2020.

A. Azzi, S. A. Pawiro and T. Mart, “3D dose reconstruction of 6 MV medical linear accelerator based on modified ray tracing algorithm: A preliminary result,” AIP Conference Proceedings, vol. 2346, no. 1, pp. 1-8, 2021, doi: 10.1063/5.0047744.

M. Pourkaveh et al., “Optimization of Clarkson’s method for calculating absorbed dose under compensator filters used in intensity-modulated radiation therapy,” Journal of Biomedical Physics & Engineering, vol. 10, no. 5, pp. 575-582, 2020, doi: 10.31661/jbpe.v0i0.858.

C. Böhm and M. Hofer, “Basic Applications of Multileaf Collimators,” AAPM Report Task Group, 2001.

W. Yao and J. B. Farr, “Determining the optimal dosimetric leaf gap setting for rounded leaf-end multileaf collimator systems by simple test fields,” Journal of Applied Clinical Medical Physics, vol. 16, no. 4, pp. 65-77, 2015, doi: 10.1120/jacmp.v16i4.5321.

M. S. Huq et al., “A dosimetric comparison of various multileaf collimators,” Physics in Medicine & Biology, vol. 47, no. 12, 2002, doi: 10.1088/0031-9155/47/12/401.

D. A. Low et al., “A technique for the quantitative evaluation of dose distributions,” Medical Physics, vol. 25, no. 5, pp. 656-661, 1998.

P. Tsiamas et al., “Beam quality and dose perturbation of 6MV flattening-filter-free linac,” Physica Medica, vol. 30, no. 1, pp. 47-56, 2014, doi: 10.1016/j.ejmp.2013.02.004.

P. R. M. Storchi, L. J. Van Battum and E. Woudstra, “Calculation of a pencil beam kernel from measured photon beam data,” Physics in Medicine Biology, vol. 44, no. 12, pp. 2917-2928, 1999, doi: 10.1088/0031-9155/44/12/305.

T. C. Zhu et al., “Report of AAPM Task Group 219 on independent calculation‐based dose/MU verification for IMRT,” Medical Physics, vol. 48, no. 10, pp. e808-e829, 2021, doi: 10.1002/mp.15069.

H. A. Alkhatib et al., “Output calculation of electron therapy at extended SSD using an improved LBR method,” Medical Physics, vol. 42, no. 2, pp. 735-740, 2015, doi: 10.1118/1.4905375.