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Measurement-anchored Monte Carlo prediction of diagnostic x-ray air kerma using HVL-matched spectra and per-electron normalization.

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Τίτλος: Measurement-anchored Monte Carlo prediction of diagnostic x-ray air kerma using HVL-matched spectra and per-electron normalization.
Συγγραφείς: Obara Y; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata-shi, Niigata, Japan., Sekimoto M; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata-shi, Niigata, Japan., Kato T; Department of Radiology, Toyohashi Municipal Hospital, Toyohashi, Aichi, Japan.
Πηγή: Medical physics [Med Phys] 2026 Oct; Vol. 53 (10), pp. e70679.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE
Imprint Name(s): Publication: 2017- : Hoboken, NJ : John Wiley and Sons, Inc.
Original Publication: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics.
Ιατρικοί όροι (MeSH): Monte Carlo Method* , Air* , Electrons*, X-Rays
Περίληψη: Background: Quantitative Monte Carlo (MC) prediction of diagnostic x-ray air kerma is limited by mismatch between theoretical spectra and measured beam quality and by the lack of a reproducible normalization from source-photon tallies to mAs-based exposures.
Purpose: To test whether HVL-matched spectra combined with measurement-anchored per-electron normalization can predict free-in-air and transmitted air kerma under selected radiographic exposure settings and to evaluate the framework in PHITS and EGS5.
Methods: Birch-Marshall spectra at 50, 80, and 120 kV were adjusted by tuning the Al-equivalent filtration parameter so that the theoretical HVL agreed with the measured HVL. The matched spectra were used as common input for PHITS and EGS5. Free-in-air air kerma was measured for 102 exposure conditions, and transmitted air kerma was measured after 2.0-cm PMMA or 0.5-cm Al for seven conditions at each tube voltage. From measured air kerma and MC air-kerma tallies expressed per source photon, we defined the normalization constant INLINEMATH , the per-electron correction factor INLINEMATH , and the tube-voltage-averaged coefficient INLINEMATH . Predictions based on INLINEMATH were compared with measurements using percentage error. Performance on the full free-in-air dataset was interpreted as agreement within the calibration dataset, whereas held-out-condition performance was assessed by leave-one-out cross-validation (LOOCV) on seven selected radiographic exposure settings at each tube voltage. Combined standard uncertainty was also summarized.
Results: After HVL matching, the free-in-air MC air-kerma tallies expressed per source photon differed between PHITS and EGS5, a finding interpreted primarily in relation to differences in source definition and source normalization. After measurement-based normalization with INLINEMATH , predicted air kerma was generally consistent with measurements under both free-in-air and transmitted conditions. The largest mean percentage error was 7.8%, slightly exceeding the predefined ± 7.5% operational benchmark. The representative combined standard uncertainty was 5.6%; when the nominal tube-voltage setting was used directly as a simulation input, inclusion of the tube-voltage accuracy term increased it to 7.3%.
Conclusions: Within the tested radiographic system and slab-transmission geometries, HVL-matched spectra combined with measurement-anchored per-electron normalization enabled prediction of air kerma under actual exposure conditions. Despite differences in the MC air-kerma tallies expressed per source photon, the final predicted values were generally consistent with measurements in both codes under the tested conditions.
(© 2026 American Association of Physicists in Medicine.)
References: Kawrakow I, The EGSnrc Code System: Monte Carlo simulation of electron and photon transport. NRCC Report PIRS 701; 2000–2003.
Boone JM, McNitt Gray MF, Hernandez AM. Monte Carlo basics for radiation dose assessment in diagnostic radiology. J Am Coll Radiol. 2017;14(6):793‐794.
Lee H. Monte Carlo methods for medical imaging research. Biomed Eng Lett. 2024;14:1195‐1205. doi:10.1007/s13534‐024‐00378‐x.
Andreo P. Monte Carlo simulations in radiotherapy dosimetry. Radiat Oncol. 2018;13(1):121. doi:10.1186/s13014‐018‐1065‐3.
Muraro S, Battistoni G, Kraan AC. Challenges in Monte Carlo Simulations as Clinical and Research Tool in Particle Therapy: a Review. Front Phys. 2020;8:567800. doi:10.3389/fphy.2020.567800.
Sato T, Niita K, Matsuda N, et al. Overview of the PHITS code and its application to medical physics. Prog Nucl Sci Technol. 2014;4:879‐882.
Sato T, Iwamoto Y, Hashimoto S, et al. Recent improvements of the Particle and Heavy Ion Transport code System (PHITS) version 3.33. J Nucl Sci Technol. 2024;61:127‐135.
Iwamoto Y, Hashimoto S, Sato T, et al. Benchmark study of particle and heavy ion transport code system using shielding integral benchmark archive and database for accelerator shielding experiments. J Nucl Sci Technol. 2022;59:665‐675.
Hirayama H, Namito Y, Bielajew AF, Wilderman SJ, Nelson WR, The EGS5 Code System. SLAC Report SLAC‐R‐730; KEK Report 2005–8.
Agostinelli S, Allison J, Amako KA, et al. Geant4—A simulation toolkit. Nucl Instrum Methods A. 2003;506:250‐303.
Allison J, Amako K, Apostolakis J, et al. Recent developments in Geant4. Nucl Instrum Methods A. 2016;835:186‐225.
Allison J, Amako K, Apostolakis JEA, et al. Geant4 Developments and Applications. IEEE Trans Nucl Sci. 2006;53(1):270‐278. doi:10.1109/TNS.2006.869826.
Boone JM, Seibert JA. An accurate method for computer generating tungsten anode X ray spectra from 30 to 140 kV (TASMIP). Med Phys. 1997;24(11):1661‐1670. doi:10.1118/1.597953.
Poludniowski G, Landry G, DeBlois F, Evans PM, Verhaegen F. SpekCalc: a program to calculate photon spectra from tungsten anode X ray tubes. Phys Med Biol. 2009;54(19):N433‐N438. doi:10.1088/0031‐9155/54/19/N01.
Bujila R, Omar A, Poludniowski G. A validation of SpekPy: a software toolkit for modelling x‐ray tube spectra. Phys Med. 2020;75:44‐54. doi:10.1016/j.ejmp.2020.04.026.
Poludniowski G, Omar A, Bujila R, Andreo P. Technical Note: spekPy v2.0—a software toolkit for modeling X ray tube spectra. Med Phys. 2021;48(7):3630‐3637. doi:10.1002/mp.14945.
Punnoose J, Xu J, Sisniega A, Zbijewski W, Siewerdsen JH. Technical Note: spektr 3.0—A computational tool for X ray spectrum modeling and analysis. Med Phys. 2016;43(8):4711. doi:10.1118/1.4955438.
IPEM Report 78. Catalogue of Diagnostic X ray Spectra and Other Data. Institute of Physics and Engineering in Medicine. 1997.
Huy BN, Dung PV. Simulation and comparison of X ray spectra in diagnostic radiography by IPEM 78, SpekPy, Xpecgen, SPEKTR 3.0, and EGSnrc Monte Carlo. Ann Clin Case Rep. 2023;8:2495.
Chen SC, Jong WL, Harun AZ. Evaluation of X ray beam quality based on measurements and estimations using SpekCalc and IPEM78 models. Malays J Med Sci. 2012;19(3):22‐28.
Sekimoto M, Katoh Y. Derivation of total filtration thickness for diagnostic x‐ray source assembly. Phys Med Biol. 2016;61(16):6011‐6024. doi:10.1088/0031‐9155/61/16/6011.
Birch R, Marshall M. Computation of bremsstrahlung X ray spectra and comparison with spectra measured with a Ge(Li) detector. Phys Med Biol. 1979;24:505‐517. doi:10.1088/0031‐9155/24/3/002.
IEC 61267. 2005. Medical diagnostic X ray equipment—Radiation conditions for use in the determination of characteristics. International Electrotechnical Commission; 2005.
IEC 60601‐2‐54. 2022. Medical electrical equipment—Part 2–54: Particular requirements for the basic safety and essential performance of X ray equipment for radiography and radioscopy. International Electrotechnical Commission; 2022.
IEC 61674. 2012. Medical electrical equipment—Dosimeters with ionization chambers and/or semiconductor detectors as used in X ray diagnostic imaging. International Electrotechnical Commission; 2012.
IAEA TRS 457. Dosimetry in Diagnostic Radiology: An International Code of Practice. International Atomic Energy Agency. 2007.
JCGM 100. 2008. Evaluation of measurement data—Guide to the expression of uncertainty in measurement. Joint Committee for Guides in Metrology; 2008.
Contributed Indexing: Keywords: Monte Carlo simulation; diagnostic radiology; dosimetry
Entry Date(s): Date Created: 20260918 Date Completed: 20260918 Latest Revision: 20260918
Update Code: 20260919
DOI: 10.1002/mp.70679
PMID: 42758581
Βάση Δεδομένων: MEDLINE
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  Data: Measurement-anchored Monte Carlo prediction of diagnostic x-ray air kerma using HVL-matched spectra and per-electron normalization.
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  Data: <searchLink fieldCode="AU" term="%22Obara+Y%22">Obara Y</searchLink>; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata-shi, Niigata, Japan.<br /><searchLink fieldCode="AU" term="%22Sekimoto+M%22">Sekimoto M</searchLink>; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata-shi, Niigata, Japan.<br /><searchLink fieldCode="AU" term="%22Kato+T%22">Kato T</searchLink>; Department of Radiology, Toyohashi Municipal Hospital, Toyohashi, Aichi, Japan.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22John+Wiley+and+Sons%2C+Inc%22">John Wiley and Sons, Inc </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>0425746 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>2473-4209 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200942405%22">00942405 </searchLink><i>NLM ISO Abbreviation: </i>Med Phys <i>Subsets: </i>MEDLINE
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  Data: <i>Publication</i>: 2017- : Hoboken, NJ : John Wiley and Sons, Inc.<br /><i>Original Publication</i>: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics.
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  Data: <searchLink fieldCode="MM" term="%22Monte+Carlo+Method%22">Monte Carlo Method*</searchLink> <br /><searchLink fieldCode="MM" term="%22Air%22">Air*</searchLink> <br /><searchLink fieldCode="MM" term="%22Electrons%22">Electrons*</searchLink><br /><searchLink fieldCode="MH" term="%22X-Rays%22">X-Rays</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Quantitative Monte Carlo (MC) prediction of diagnostic x-ray air kerma is limited by mismatch between theoretical spectra and measured beam quality and by the lack of a reproducible normalization from source-photon tallies to mAs-based exposures.<br />Purpose: To test whether HVL-matched spectra combined with measurement-anchored per-electron normalization can predict free-in-air and transmitted air kerma under selected radiographic exposure settings and to evaluate the framework in PHITS and EGS5.<br />Methods: Birch-Marshall spectra at 50, 80, and 120 kV were adjusted by tuning the Al-equivalent filtration parameter so that the theoretical HVL agreed with the measured HVL. The matched spectra were used as common input for PHITS and EGS5. Free-in-air air kerma was measured for 102 exposure conditions, and transmitted air kerma was measured after 2.0-cm PMMA or 0.5-cm Al for seven conditions at each tube voltage. From measured air kerma and MC air-kerma tallies expressed per source photon, we defined the normalization constant INLINEMATH , the per-electron correction factor INLINEMATH , and the tube-voltage-averaged coefficient INLINEMATH . Predictions based on INLINEMATH were compared with measurements using percentage error. Performance on the full free-in-air dataset was interpreted as agreement within the calibration dataset, whereas held-out-condition performance was assessed by leave-one-out cross-validation (LOOCV) on seven selected radiographic exposure settings at each tube voltage. Combined standard uncertainty was also summarized.<br />Results: After HVL matching, the free-in-air MC air-kerma tallies expressed per source photon differed between PHITS and EGS5, a finding interpreted primarily in relation to differences in source definition and source normalization. After measurement-based normalization with INLINEMATH , predicted air kerma was generally consistent with measurements under both free-in-air and transmitted conditions. The largest mean percentage error was 7.8%, slightly exceeding the predefined ± 7.5% operational benchmark. The representative combined standard uncertainty was 5.6%; when the nominal tube-voltage setting was used directly as a simulation input, inclusion of the tube-voltage accuracy term increased it to 7.3%.<br />Conclusions: Within the tested radiographic system and slab-transmission geometries, HVL-matched spectra combined with measurement-anchored per-electron normalization enabled prediction of air kerma under actual exposure conditions. Despite differences in the MC air-kerma tallies expressed per source photon, the final predicted values were generally consistent with measurements in both codes under the tested conditions.<br /> (© 2026 American Association of Physicists in Medicine.)
– Name: Ref
  Label: References
  Group: RefInfo
  Data: Kawrakow I, The EGSnrc Code System: Monte Carlo simulation of electron and photon transport. NRCC Report PIRS 701; 2000–2003.<br />Boone JM, McNitt Gray MF, Hernandez AM. Monte Carlo basics for radiation dose assessment in diagnostic radiology. J Am Coll Radiol. 2017;14(6):793‐794.<br />Lee H. Monte Carlo methods for medical imaging research. Biomed Eng Lett. 2024;14:1195‐1205. doi:10.1007/s13534‐024‐00378‐x.<br />Andreo P. Monte Carlo simulations in radiotherapy dosimetry. Radiat Oncol. 2018;13(1):121. doi:10.1186/s13014‐018‐1065‐3.<br />Muraro S, Battistoni G, Kraan AC. Challenges in Monte Carlo Simulations as Clinical and Research Tool in Particle Therapy: a Review. Front Phys. 2020;8:567800. doi:10.3389/fphy.2020.567800.<br />Sato T, Niita K, Matsuda N, et al. Overview of the PHITS code and its application to medical physics. Prog Nucl Sci Technol. 2014;4:879‐882.<br />Sato T, Iwamoto Y, Hashimoto S, et al. Recent improvements of the Particle and Heavy Ion Transport code System (PHITS) version 3.33. J Nucl Sci Technol. 2024;61:127‐135.<br />Iwamoto Y, Hashimoto S, Sato T, et al. Benchmark study of particle and heavy ion transport code system using shielding integral benchmark archive and database for accelerator shielding experiments. J Nucl Sci Technol. 2022;59:665‐675.<br />Hirayama H, Namito Y, Bielajew AF, Wilderman SJ, Nelson WR, The EGS5 Code System. SLAC Report SLAC‐R‐730; KEK Report 2005–8.<br />Agostinelli S, Allison J, Amako KA, et al. Geant4—A simulation toolkit. Nucl Instrum Methods A. 2003;506:250‐303.<br />Allison J, Amako K, Apostolakis J, et al. Recent developments in Geant4. Nucl Instrum Methods A. 2016;835:186‐225.<br />Allison J, Amako K, Apostolakis JEA, et al. Geant4 Developments and Applications. IEEE Trans Nucl Sci. 2006;53(1):270‐278. doi:10.1109/TNS.2006.869826.<br />Boone JM, Seibert JA. An accurate method for computer generating tungsten anode X ray spectra from 30 to 140 kV (TASMIP). Med Phys. 1997;24(11):1661‐1670. doi:10.1118/1.597953.<br />Poludniowski G, Landry G, DeBlois F, Evans PM, Verhaegen F. SpekCalc: a program to calculate photon spectra from tungsten anode X ray tubes. Phys Med Biol. 2009;54(19):N433‐N438. doi:10.1088/0031‐9155/54/19/N01.<br />Bujila R, Omar A, Poludniowski G. A validation of SpekPy: a software toolkit for modelling x‐ray tube spectra. Phys Med. 2020;75:44‐54. doi:10.1016/j.ejmp.2020.04.026.<br />Poludniowski G, Omar A, Bujila R, Andreo P. Technical Note: spekPy v2.0—a software toolkit for modeling X ray tube spectra. Med Phys. 2021;48(7):3630‐3637. doi:10.1002/mp.14945.<br />Punnoose J, Xu J, Sisniega A, Zbijewski W, Siewerdsen JH. Technical Note: spektr 3.0—A computational tool for X ray spectrum modeling and analysis. Med Phys. 2016;43(8):4711. doi:10.1118/1.4955438.<br />IPEM Report 78. Catalogue of Diagnostic X ray Spectra and Other Data. Institute of Physics and Engineering in Medicine. 1997.<br />Huy BN, Dung PV. Simulation and comparison of X ray spectra in diagnostic radiography by IPEM 78, SpekPy, Xpecgen, SPEKTR 3.0, and EGSnrc Monte Carlo. Ann Clin Case Rep. 2023;8:2495.<br />Chen SC, Jong WL, Harun AZ. Evaluation of X ray beam quality based on measurements and estimations using SpekCalc and IPEM78 models. Malays J Med Sci. 2012;19(3):22‐28.<br />Sekimoto M, Katoh Y. Derivation of total filtration thickness for diagnostic x‐ray source assembly. Phys Med Biol. 2016;61(16):6011‐6024. doi:10.1088/0031‐9155/61/16/6011.<br />Birch R, Marshall M. Computation of bremsstrahlung X ray spectra and comparison with spectra measured with a Ge(Li) detector. Phys Med Biol. 1979;24:505‐517. doi:10.1088/0031‐9155/24/3/002.<br />IEC 61267. 2005. Medical diagnostic X ray equipment—Radiation conditions for use in the determination of characteristics. International Electrotechnical Commission; 2005.<br />IEC 60601‐2‐54. 2022. Medical electrical equipment—Part 2–54: Particular requirements for the basic safety and essential performance of X ray equipment for radiography and radioscopy. International Electrotechnical Commission; 2022.<br />IEC 61674. 2012. Medical electrical equipment—Dosimeters with ionization chambers and/or semiconductor detectors as used in X ray diagnostic imaging. International Electrotechnical Commission; 2012.<br />IAEA TRS 457. Dosimetry in Diagnostic Radiology: An International Code of Practice. International Atomic Energy Agency. 2007.<br />JCGM 100. 2008. Evaluation of measurement data—Guide to the expression of uncertainty in measurement. Joint Committee for Guides in Metrology; 2008.
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  Data: <i>Keywords: </i>Monte Carlo simulation; diagnostic radiology; dosimetry
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