Optimization of Tube Voltage in Digital Chest Radiography: Integrating Exposure Index and Image Noise Using an Anthropomorphic Phantom
Downloads
Tube voltage (kVp) influences detector exposure and objective image-quality characteristics in digital radiography; however, the integration of standardized exposure indicators with region-specific image noise and signal-to-noise ratio (SNR) across anatomically distinct thoracic regions remains limited. This study evaluated the effects of tube-voltage variation on Exposure Index (EI), Deviation Index (DI), image noise, and SNR in digital chest radiography. An anthropomorphic PBU-50 thorax phantom was imaged using a Samsung XGEO GF50 digital radiography system at eleven tube-voltage settings from 50 to 100 kVp in 5-kVp increments while maintaining a constant 8 mAs. Three independent acquisitions were performed at each setting, yielding 33 radiographic images. EI and DI were extracted from DICOM metadata, while mean pixel value, image noise, and SNR were evaluated in five anatomical regions of interest: clavicle, scapula, costae, sternum, and lung field. Descriptive statistics, linear and quadratic regression, and Pearson and Spearman correlation analyses were performed. EI increased from 73.95 ± 0.28 at 50 kVp to 1980.91 ± 16.78 at 100 kVp, while DI increased from −4.01 ± 0.02 to +10.28 ± 0.04. Image noise generally decreased with increasing kVp, with reductions from 50 to 100 kVp ranging from 6.9% in the scapula to 58.8% in the sternum. SNR exhibited region-dependent nonlinear responses, and quadratic models better characterized several kVp-dependent relationships. At 60 kVp, EI was 216.02 ± 1.68, and DI was +0.65 ± 0.03, representing the only investigated condition within the predefined −1 ≤ DI ≤ +1 interval while providing higher SNR than at 50 kVp across all ROIs. These findings demonstrate an anatomy-dependent trade-off between detector exposure and objective image quality. The 60-kVp condition should therefore be considered a candidate detector-exposure condition for further optimization using patient-dose measurements and diagnostic image-quality assessment
[1] H. R. Mendes, J. C. Silva, M. Marcondes, and A. Tomal, “Optimization of image quality and dose in adult and pediatric chest radiography via Monte Carlo simulation and experimental methods,” Radiation Physics and Chemistry, vol. 201, p. 110396, Nov. 2022, doi: 10.1016/J.RADPHYSCHEM.2022.110396.
[2] P. H. Huynh and T. D. Truong, “Dynamic Fine-Tuning Strategy of Deep Learning Models for Lung Disease Classification on Chest X-ray Images,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 8, no. 3, pp. 1047–1059, Jul. 2026, doi: 10.35882/JEEEMI.V8I3.1352.
[3] L. Song et al., “Artificial intelligence for chest X-ray image enhancement,” Radiat. Med. Prot., vol. 6, no. 1, pp. 61–68, Feb. 2025, doi: 10.1016/J.RADMP.2024.12.003.
[4] Z. Mousavi, N. Shahini, S. Sheykhivand, S. Mojtahedi, and A. Arshadi, “COVID-19 detection using chest X-ray images based on a developed deep neural network,” SLAS Technol., vol. 27, no. 1, pp. 63–75, Feb. 2022, doi: 10.1016/J.SLAST.2021.10.011.
[5] E. Saez de Gordoa, A. Portella, J. M. Escudero-Fernández, and J. Andreu Soriano, “Usefulness of chest X-rays for detecting COVID 19 pneumonia during the SARS-CoV-2 pandemic,” Radiología (English Edition), vol. 64, no. 4, pp. 310–316, Jul. 2022, doi: 10.1016/J.RXENG.2021.11.003.
[6] F. Homayounieh et al., “Variations in CT Utilization, Protocols, and Radiation Doses in COVID-19 Pneumonia: Results from 28 Countries in the IAEA Study,” Radiology, vol. 298, no. 3, pp. E141–E151, Mar. 2021, doi: 10.1148/RADIOL.2020203453.
[7] M. C. Devery, L. Sweetman, and D. Cody, “Optimising beam quality for chest X-ray imaging: Insights from over two decades of peer reviewed publication,” Physica Medica, vol. 148, p. 105907, Aug. 2026, doi: 10.1016/J.EJMP.2026.105907.
[8] E.E. Akşit, A. Yalçın, and T. Olğar, “Effect of varying X-ray tube voltage and additional filtration on image quality and patient dose in digital radiography system,” Applied Radiation and Isotopes, vol. 199, p. 110893, Sep. 2023, doi: 10.1016/J.APRADISO.2023.110893.
[9] N. Tanaka and Y. Yoon, “Exposure index and radiographic noise of an indirect flat-panel detector system for various X-ray beam qualities,” Physical and Engineering Sciences in Medicine 2025 48:2, vol. 48, no. 2, pp. 857–865, May 2025, doi: 10.1007/S13246-025-01553-X.
[10] S. Johansen et al., “Knowledge and use of Exposure Index (EI) in radiography in Norway and Singapore,” Radiography, vol. 32, no. 4, p. 103392, May 2026, doi: 10.1016/J.RADI.2026.103392.
[11] S. Lewis, T. Pieterse, and H. Lawrence, “Evaluating the use of exposure indicators in digital x-ray imaging system: Gauteng South Africa,” Radiography, vol. 25, no. 3, pp. e58–e62, Aug. 2019, doi: 10.1016/J.RADI.2019.01.003.
[12] T. Takaki, T. Fujibuchi, S. Murakami, T. Aoki, and M. Ohki, “The clinical significance of modifying X-ray tube current-time product based on prior image deviation index for digital radiography,” Physica Medica, vol. 63, pp. 35–40, Jul. 2019, doi: 10.1016/J.EJMP.2019.05.011.
[13] I. A. Tsalafoutas, S. AlKhazzam, and M. H. Kharita, “Exposure index in digital radiography and its dependence on acquisition parameters, anatomy, and manufacturer,” J. Appl. Clin. Med. Phys., vol. 27, no. 1, p. e70331, Jan. 2026, doi: 10.1002/ACM2.70331.
[14] M. Funahashi, K. Kashiyama, T. Nakamura, and J. Shiraishi, “Utilization of upper and lower limits of exposure index in clinical digital radiography,” Radiol. Phys. Technol., vol. 15, no. 4, pp. 349–357, Dec. 2022, doi: 10.1007/S12194-022-00674-2.
[15] T. Takaki, S. Murakami, R. Watanabe, T. Aoki, and T. Fujibuchi, “Calculating the target exposure index using a deep convolutional neural network and a rule base,” Physica Medica, vol. 71, pp. 108–114, Mar. 2020, doi: 10.1016/j.ejmp.2020.02.012.
[16] P. Soulis, P. Papavasileiou, A. Bakas, E. Lavdas, N. Stogiannos, and P. Galanis, “‘Exposing’ the exposure index: Navigating between image quality and radiation dose,” J. Med. Imaging Radiat. Sci., vol. 55, no. 4, p. 101766, Dec. 2024, doi: 10.1016/J.JMIR.2024.101766.
[17] H. Park, Y. Yoon, E. Kim, H. Jeong, and J. Kim, “Monitoring clinical exposure index and deviation index for dose optimization based on national diagnostic reference level: Focusing on general radiography of extremities,” J. Xray Sci. Technol., vol. 30, no. 3, pp. 419–432, 2022, doi: 10.3233/XST-211084.
[18] Y. Yoon et al., “Proper Management of the Clinical Exposure Index Based on Body Thickness Using Dose Optimization Tools in Digital Chest Radiography: A Phantom Study,” Int. J. Environ. Res. Public Health, vol. 18, no. 10, p. 5203, May 2021, doi: 10.3390/IJERPH18105203.
[19] M. E. Lochschmidt et al., “Exposure control at the first dark-field chest radiography demonstrator system,” Physica Medica, vol. 135, p. 105013, Jul. 2025, doi: 10.1016/J.EJMP.2025.105013.
[20] A. P. Sauter et al., “Correlation of image quality parameters with tube voltage in X-ray dark-field chest radiography: a phantom study,” Scientific Reports 2021 11:1, vol. 11, no. 1, p. p.14130, Jul. 2021, doi: 10.1038/s41598-021-93716-5.
[21] S. Asriningrum, K. Ansory, and P. T. Hasan, “Faktor Eksposi terhadap Kualitas Citra Radiografi dan Dosis Pasien Menggunakan Parameter Penilaian Signal to Noise Ratio (SNR) pada Pemeriksaan Thorax Posteroanterior dengan Menggunakan Pesawat Computed Radiografi,” Jurnal Imejing Diagnostik (JImeD), vol. 7, no. 1, pp. 15–18, Feb. 2021, doi: 10.31983/JIMED.V7I1.6650.
[22] J. K. Dave et al., “Current state of practice regarding digital radiography exposure indicators and deviation indices: Report of AAPM Imaging Physics Committee Task Group 232,” Med. Phys., vol. 45, no. 11, pp. e1146–e1160, Nov. 2018, doi: 10.1002/MP.13212.
[23] D. C. Montgomery and G. C. Runger, Applied Statistics and Probability for Engineers, 7th ed. Wiley Global Education, 2018. [Online]. Available: https://books.google.co.id/books?id=Y9VSDwAAQBAJ
[24] S. Ye, J. Y. Lim, and W. Huang, “Statistical considerations for repeatability and reproducibility of quantitative imaging biomarkers,” BJR Open, vol. 4, no. 1, p. 20210083, Aug. 2022, doi: 10.1259/BJRO.20210083.
[25] D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis, 6th ed. in Wiley Series in Probability and Statistics. Hoboken, NJ, USA: John Wiley & Sons, 2021. [Online]. Available: https://books.google.co.id/books?id=N8EZEAAAQBAJ
[26] P. Schober, C. Boer, and L. A. Schwarte, “Correlation Coefficients: Appropriate Use and Interpretation,” Anesth. Analg., vol. 126, no. 5, pp. 1763–1768, May 2018, doi: 10.1213/ANE.0000000000002864.
[27] W. Elshami, M. M. Abuzaid, M. M. Gibril, A. Sulieman, and D. A. Bradley, “Impact of high kilo-voltage peak technique on radiation dose for neonates undergoing chest radiography: Experimental study,” Radiation Physics and Chemistry, vol. 199, p. 110327, Oct. 2022, doi: 10.1016/J.RADPHYSCHEM.2022.110327.
[28] M. D. Cohen, M. L. Cooper, K. Piersall, and B. K. Apgar, “Quality assurance: using the exposure index and the deviation index to monitor radiation exposure for portable chest radiographs in neonates,” Pediatr. Radiol., vol. 41, no. 5, pp. 592–601, Dec. 2011, doi: 10.1007/S00247-010-1951-9.
[29] N. Tanaka, Y. Yoon, and T. Ideguchi, “Relationship between exposure index and radiographic noise under varying exposure conditions for chest and abdominal radiography,” Radiation Physics and Chemistry, vol. 243, p. 113676, Jun. 2026, doi: 10.1016/J.RADPHYSCHEM.2026.113676.
[30] A. Creeden and M. Curtis, “Optimising default radiographic exposure factors using Deviation Index,” Radiography, vol. 26, no. 4, pp. 308–313, Nov. 2020, doi: 10.1016/J.RADI.2020.02.009.
[31] D. Lučić, L. Jurša, E. Alukić, and N. Mekiš, “Comparative analysis of lateral cervical radiographic imaging quality with and without anti-scatter grid application,” Applied Radiation and Isotopes, vol. 230, p. 112459, Apr. 2026, doi: 10.1016/J.APRADISO.2026.112459.
[32] M. Sayed, K. M. Knapp, J. Fulford, C. Heales, and S. J. Alqahtani, “The principles and effectiveness of X-ray scatter correction software for diagnostic X-ray imaging: A scoping review,” Eur. J. Radiol., vol. 158, p. 110600, Jan. 2023, doi: 10.1016/j.ejrad.2022.110600.
[33] N. Chukorn, M. Inkhoon, K. Sanklaa, S. Suwan-o-pas, P. Nguanbunmak, and P. Maksorn, “Dose optimization for digital chest radiography in phantom,” J. Med. Imaging Radiat. Sci., vol. 53, no. 4, p. S59, Dec. 2022, doi: 10.1016/J.JMIR.2022.10.203.
[34] M. Frank et al., “Dark-field chest X-ray imaging for the assessment of COVID-19-pneumonia,” Communications Medicine, vol. 2, no. 1, p. 147, Nov. 2022, doi: 10.1038/s43856-022-00215-3.
[35] R. Higashide and K. Kanamori, “Fundamental study of a novel image quality index for optimising image quality and dose in digital radiography systems,” Radiography, vol. 32, no. 2, p. 103303, Feb. 2026, doi: 10.1016/J.RADI.2025.103303.
Copyright (c) 2026 Jihan Rahima, Anisa Nur Istiqomah, Muhamad Fa’ik (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
