Description of kV and mAs Parameter Variations in Conventional Radiography with Computed Radiography (CR) Modality on the Same Object Producing Readable Images
Keywords:
kV variation, mAs, radiographic image quality, computed radiographyAbstract
Background: Radiographic examinations play a crucial role in the diagnostic process and in evaluating medical conditions. The quality of radiographic images is strongly influenced by exposure parameters, particularly tube voltage (kV) and tube current–exposure time (mAs). Appropriate adjustment of these parameters is essential to produce diagnostic images with adequate clarity while minimizing radiation exposure. Objective: This study aimed to analyze the variations of kV and mAs parameters in conventional radiography using the Computed Radiography (CR) modality on thorax imaging with the same object thickness in order to obtain diagnostically readable images. Method: This study employed a quantitative experimental design conducted at the Radiology Department of RSUD Dr. H. Ishak Umarella from June to August 2025. The independent variables were variations in tube voltage (60, 65, and 70 kV) and mAs (0.025, 0.028, and 0.032), while the dependent variable was radiographic image quality. Image quality was evaluated based on four parameters: density, contrast, sharpness, and detail. The resulting radiographic images were assessed by three radiographer observers using a standardized evaluation checklist. Results: The results indicated that variations in kV and mAs significantly affected the quality of thorax radiographic images. The optimal image quality was obtained at 70 kV combined with mAs values of 0.025–0.032, producing images that were considered sufficiently clear in terms of density, contrast, sharpness, and detail. Conclusion: Variations in kV and mAs parameters influence thorax radiographic image quality. The use of higher kV combined with relatively lower mAs values is recommended to produce diagnostically readable images while maintaining a lower radiation dose.
References
Andrejewski J, De Marco F, Willer K, Noichl W, Gustschin A, Koehler T, et al. Whole-body X-ray dark-field radiography of a human cadaver. Eur Radiol Exp. 2021;5(1):1–9. https://doi.org/10.1186/s41747-020-00201-1.
Angaroni. Computational methods in systems biology. In: Bortolussi IL, editor. Lecture Notes in Computer Science. Trieste: Springer International Publishing; 2019. https://doi.org/10.1007/978-3-030-31304-3_8.
Babikir E, Al-Mallah A, Al-Sehlawi M, Tamam N, Sulieman A. Patient radiation dose and image quality in plain radiography: an assessment of three common procedures in ten hospitals. Radiat Phys Chem. 2020; 173:108888. https://doi.org/10.1016/j.radphyschem.2020.108888.
Distyarini P. The suitability of exposure factors stated on the control table with those produced by the X-ray tube in the Radiology Installation of Dr. Soetomo Hospital Surabaya [thesis]. Surabaya: Universitas Airlangga; 2010.
Supriyadi DS, Choridah L, Masrochah S, Nawangsih, Sari AS. Optimization of geometry verification imaging in radiotherapy using computed radiography (CR). Inti Medika Pustaka; 2024.
Flannelly LT, Flannelly KJ, Jankowski KRB. Independent, dependent, and other variables in healthcare and chaplaincy research. J Health Care Chaplain. 2014;20(4):161-170. https://doi.org/10.1080/08854726.2014.959374.
Hapizah, Riana TA, Susanti E, Simarmata RH, Nurhasanah F. Conjecture in completing creative problem-solving questions as part of development. Adv Soc Sci Educ Humanit Res. 2021; 513:673-680. https://doi.org/10.2991/assehr.k.201230.180.
Jenkins D. Imaging processes. Unpublished manuscript; 2019.
Jenkins D. Exposure factor manipulation and control. In: Radiographic photography and imaging processes. Dordrecht: Springer; 2022. https://doi.org/10.1007/978-94-009-8692-3_7.
Feng Q, Zheng M, Su S, Gu Z. A new laser scanning system for computed radiography. In: Advanced biometric technologies. London: IntechOpen; 2016. https://www.intechopen.com/books/advanced-biometric-technologies/liveness-detection-in-biometrics
Rasad S. Radiologi diagnostik. Jakarta: Balai Penerbit FKUI; 2016.
Rif’ah SA. The effect of changes in kV, mAs, and object thickness on patient radiation dose and readable image quality [thesis]. Malang: Universitas Brawijaya; 2016.
Sparzinanda, et al. The effect of exposure factors on radiographic image quality. J Online Phys. 2017;3(1).
Wita A, Fransiska E. The relationship between kV and thorax object thickness. J Kesehatan. 2018:17-21.
Zelviani S. Image quality in direct digital radiography and computed radiography. J Teknosains. 2017;11(1):59-62.
Hussain S, Mubeen I, Ullah N, Shah SS, Khan BA, Zahoor M, Ullah R, Khan FA, Sultan MA. Modern diagnostic imaging technique applications and risk factors in the medical field: a review. BioMed research international. 2022;2022(1):5164970.
Pancholy SB, Payne M, Pancholy PS, Patel GA, Patel S, Shah SC, Kaul P, Pancholy SA, Patel TM. Association between distance from the radiation source and radiation exposure: A phantom‐based study. Catheterization and Cardiovascular Interventions. 2021 May 1;97(6): E810-6.
Prakash D, Kotian RP. Standard X-Ray Tubes: Basic Principles, Types of X-Ray Tubes, and Routine Quality Control. In Fundamentals of X-ray Imaging: Basic Principles, Quality Control, Clinical Applications, and Safety 2025 Oct 1 (pp. 87-184). Singapore: Springer Nature Singapore.
Campillo-Rivera GE, Torres-Cortes CO, Vazquez-Banuelos J, Garcia-Reyna MG, Marquez-Mata CA, Vasquez-Arteaga M, Vega-Carrillo HR. X-ray spectra and gamma factors from 70 to 120 kV X-ray tube voltages. Radiation Physics and Chemistry. 2021 Jul 1; 184:109437.
Haleem AA, Huyan J, Nagasawa K, Kuroda Y, Nishiki Y, Kato A, Nakai T, Araki T, Mitsushima S. Effects of operation and shutdown parameters and electrode materials on the reverse current phenomenon in alkaline water analyzers. Journal of Power Sources. 2022 Jul 1; 535:231454.
Tafti D, Maani CV. X-ray Production. InStatPearls [Internet] 2023 Jul 31. StatPearls Publishing.
Lewis S. Exposure Indicators: Black, White, or Shades of Grey. InGeneral Radiography 2020 Jul 15 (pp. 71-94). CRC Press.
Sayed M, Knapp KM, Fulford J, Heales C, Alqahtani SJ. The principles and effectiveness of X-ray scatter correction software for diagnostic X-ray imaging: A scoping review. European Journal of Radiology. 2023 Jan 1; 158:110600.
Papp J. Quality management in the imaging Sciences-E-Book: quality management in the imaging Sciences-E-Book. Elsevier Health Sciences; 2023 Sep 18.
Pauwelyn A, Carré M, Jourlin M, Ginhac D, Meriaudeau F. Image visual quality: sharpness evaluation in the logarithmic image processing framework. Big Data and Cognitive Computing. 2025 Jun 9;9(6):154.
