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Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area

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Abstract

This study aimed to determine the optimal radiographic conditions for detecting lesions on digital chest radiographs using an indirect conversion flat-panel detector with a copper (Cu) filter. First, we calculated the effective detective quantum efficiency (DQE) by considering clinical conditions to evaluate the image quality. We then measured the segmentation accuracy using a U-net convolutional network to verify the effectiveness of the Cu filter. We obtained images of simulated lung tumors using 10-mm acrylic spheres positioned at the right lung apex and left middle lung of an adult chest phantom. The Dice coefficient was calculated as the similarity between the output and learning images to evaluate the accuracy of tumor area segmentation using U-net. Our results showed that effective DQE was higher in the following order up to the spatial frequency of 2 cycles/mm: 120 kV + no Cu, 120 kV + Cu 0.1 mm, and 120 kV + Cu 0.2 mm. The segmented region was similar to the true region for mass–area extraction in the left middle lobe. The lesion segmentation in the upper right lobe with 120 kV + no Cu and 120 kV + Cu 0.1 mm was less successful. However, adding a Cu filter yielded reproducible images with high Dice coefficients, regardless of the tumor location. We confirmed that adding a Cu filter decreases the X-ray absorption efficiency while improving the signal-to-noise ratio (SNR). Furthermore, artificial intelligence accurately segments low-contrast lesions.

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References

  1. UNSCEAR. Medical radical exposures. Sources and effects of ionizing radiation. UNSCER 2008 Report. New York: United Nations. Annex A; 2010

  2. Oda N, Kurokawa Y, Uehara S, Sasaki N, Yamagata K, Yamazaki S, et al. Evaluation of 90 kV beam with 0.15-mm Cu filter in chest radiography using CsI-Flat panel detector. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2020;76:463–73. https://doi.org/10.6009/jjrt.2020_JSRT_76.5.463.

    Article  CAS  PubMed  Google Scholar 

  3. Oda N, Tabata Y, Mizuta M, Asada Y, Nakano T, Hara T, et al. Optimal beam quality in chest radiography using CsI-flat panel detector for detection of pulmonary nodules. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2021;77:335–43. https://doi.org/10.6009/jjrt.2021_JSRT_77.4.335.

    Article  PubMed  Google Scholar 

  4. Sun Z, Lin C, Tyan Y, Ng KH. Optimization of chest radiographic imaging parameters: A comparison of image quality and entrance skin dose for digital chest radiography systems. Clin Imaging. 2012;36:279–86. https://doi.org/10.1016/j.clinimag.2011.09.006.

    Article  PubMed  Google Scholar 

  5. Compagnone G, Baleni CM, Di Nicola E, et al. Optimisation of radiological protocols for chest imaging using computed radiography and flat-panel X-ray detectors. Radiol Med. 2013;118(4):540–54.

    Article  CAS  PubMed  Google Scholar 

  6. Vyborny C, Metz C, Doi K. Large area contrast prediction in screen-film systems. Proc Soc Photo Opt Instrum Eng. 1980;233:30–6.

    Google Scholar 

  7. Kawashima H, Ichikawa K, Kunitomo H. Relationship between radiation quality and image quality in digital chest radiography: Validation study using human soft tissue-equivalent phantom. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2021;77:255–62. https://doi.org/10.6009/jjrt.2021_JSRT_77.3.255.

    Article  CAS  PubMed  Google Scholar 

  8. Hamer OW, Sirlin CB, Strotzer M, Borisch I, Zorger N, Feuerbach S, et al. Chest radiography with a flat-panel detector: Image quality with dose reduction after copper filtration. Radiology. 2005;237:691–700. https://doi.org/10.1148/radiol.2372041738.

    Article  PubMed  Google Scholar 

  9. Asada Y, Suzuki S, Kobayashi K, Kato H, Igarashi T, Tsukamoto A, et al. Investigation of patient exposure doses in diagnostic radiography in 2011 questionnaire. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2013;69:371–9. https://doi.org/10.6009/jjrt.2013_jsrt_69.4.371.

    Article  CAS  PubMed  Google Scholar 

  10. Asada Y, Suzuki S, Kobayashi K, Kato H, Igarashi T, Tsukamoto A, et al. Summary of results of the patient exposures in diagnostic radiography in 2011 questionnaire-focus on radiographic conditions-. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2012;68:1261–8. https://doi.org/10.6009/jjrt.2012_jsrt_68.9.1261.

    Article  PubMed  Google Scholar 

  11. Kishimoto K, Ariga E, Ishigaki R, Imai M, Kawamoto K, Kobayashi K, et al. Study of appropriate dosing in consideration of image quality and patient dose on the digital radiography. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2011;67:1381–97. https://doi.org/10.6009/jjrt.67.1381.

    Article  PubMed  Google Scholar 

  12. Schaefer-Prokop CM, De Boo DW, Uffmann M, Prokop M. DR and CR: Recent advances in technology. Eur J Radiol. 2009;72:194–201. https://doi.org/10.1016/j.ejrad.2009.05.055.

    Article  CAS  PubMed  Google Scholar 

  13. IEC. Medical electrical equipment-Characteristics of digital X-ray imaging devices part1: Determination of detective quantum efficiency. ed. 1.0p. 62220–1; 2003

  14. IEC. 62220–1–2 Medical electrical equipment-Characteristics of digital X-ray imaging devices Part 1–2: Determination of the detective quantum efficiency-Detectors used in mammography. 1st ed..O, 2007

  15. Samei E, Ranger NT, Dobbins JT, Ravin CE. Effective dose efficiency: An application- specific metric of quality and dose for digital radiography. Phys Med Biol. 2011;56:5099–118. https://doi.org/10.1088/0031-9155/56/16/002.

    Article  PubMed  Google Scholar 

  16. Samei E, Ranger NT, Mackenzie A, Honey ID, Dobbins JT, Ravin CE. Detector or system? extending the concept of detective quantum efficiency to characterize the performance of digital radiographic imaging systems. Radiology. 2008;249:926–37. https://doi.org/10.1148/radiol.2492071734.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bertolini M, Nitrosi A, Rivetti S, Lanconelli N, Pattacini P, Ginocchi V, et al. A comparison of digita1radiography systems in terms of effective detective quantum efficiency. Med Phys. 2012;39:2617–27. https://doi.org/10.1118/1.4704500.

    Article  PubMed  Google Scholar 

  18. Hirai.T, Korogi. Y, Arimura. H, et al. Intracranial aneurysms at MR angiography: effect of computer-aided diagnosis on radiologists’ detection performance. Radiology. 2005;237(2):605–10.

    Article  PubMed  Google Scholar 

  19. Sosna J, Morrin MM, Kruskal JB, Lavin PT, Rosen MP, Raptopoulos V. CT colonography of colorectal polyps: A metaanalysis. AJR Am J Roentgenol. 2003;181:1593–8. https://doi.org/10.2214/ajr.181.6.1811593.

    Article  PubMed  Google Scholar 

  20. Kawashita I. Development of computer-aided medical care support system based on image recognition/processing technique: from diagnostic support to medical support. Iyou Gazou Jyouhou Gakkai Zasshi. 2014;31:xviii–xxii.

    Google Scholar 

  21. Gulshan V, Peng L, Coram M, Stumpe MC, Derek W, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10. https://doi.org/10.1001/jama.2016.17216.

    Article  PubMed  Google Scholar 

  22. IAEA Safety Series No. 1154, International basic safety standards for protection of radiation sources. Vienna: International Arts and Entertainment Alliance; 1994.

    Google Scholar 

  23. Japan network for research and information on medical exposures(J-RIME); 2020 - Japan DRLs 2020-. National Diagnostic Reference Levels in Japan. http://www.radher.jp/J-RIME/report/DRL2020_Engver.pdf

  24. Hayashi H, Takegami K, Konishi Y, Fukuda I. Indirect method of measuring the scatter X-ray fraction using collimators in the diagnostic domain. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2014;70:213–22. https://doi.org/10.6009/jjrt.2014_jsrt_70.3.213.

    Article  PubMed  Google Scholar 

  25. Tucker DM, Barnes GT, Chakraborty DP. Semiempirical model for generating tungsten target x-ray spectra. Med Phys. 1991;18:211–8. https://doi.org/10.1118/1.596709.

    Article  CAS  PubMed  Google Scholar 

  26. Berger MJ, Hubbell JH, Seltzer SM, et al. XCOM: Photon cross sections database. 1998;(NBSIR 87–3597), Inst.

  27. Hiraoka T, Kato H. Physical beam quality of Titan 320 X-ray equipment, primary X-ray spectrum and air kerma. Radiol Sci. 2011;54:38–41.

    Google Scholar 

  28. Rnoneberger O, Fischer P, Brox T. U-net Convolutional networks for biomedical image segmentation Lecture Notes in Computer Sciences (including SubserLect Notes ArtifIntellLect Notes Bioinformatics). In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention–MICCAI 2015: 18th International Conference, Munich, Germany, Proceedings, Part III. Cham: Springer International Publishing; 2015. p. 234–41.

    Google Scholar 

  29. Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, et al. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46:e1–36. https://doi.org/10.1002/mp.13264.

    Article  PubMed  Google Scholar 

  30. Onodera S, Lee Y, Kawabata T. Dose reduction potential in dual-energy subtraction chest radiography based on the relationship between spatial-resolution property and segmentation accuracy of the tumor area. Acta IMEKO. 2022;11:1. https://doi.org/10.21014/acta_imeko.v11i2.1168.

    Article  Google Scholar 

  31. Kijewski MF, Judy PF. The noise power spectrum of CT images. Phys Med Biol. 1987;32:565–75. https://doi.org/10.1088/0031-9155/32/5/003.

    Article  CAS  PubMed  Google Scholar 

  32. Riederer SJ, Pelc NJ, Chesler DA. The noise power spectrum in computed X-ray tomography. Phys Med Biol. 1978;23:446–54. https://doi.org/10.1088/0031-9155/23/3/008.

    Article  CAS  PubMed  Google Scholar 

  33. Onodera S, Lee Y, Tanaka Y. Evaluation of dose reduction potential in scatter-corrected bedside chest radiography using U-net. Radiol Phys Technol. 2020;13:336–47. https://doi.org/10.1007/s12194-020-00586-z.

    Article  PubMed  Google Scholar 

  34. Vyborny PC, Bunch HC, et al. Image quality in chest radiography. J ICRU. 2003;3:13.

    Article  Google Scholar 

  35. Nasrullah N, Sang J, Alam M, Mateen M, Cai B, Hu H. Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors (Basel). 2019;19(17):3722. https://doi.org/10.3390/s19173722.

    Article  PubMed  Google Scholar 

  36. Chiu H, Peng R, Lin Y, Wang T, Yang Y, Chen Y, et al. artificial intelligence for early detection of chest nodules in X-ray images. Biomedicines. 2022;10(11):2839. https://doi.org/10.3390/biomedicines10112839.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Rajaraman S, Folio L, Dimperio J, Alderson P, Antani S. Improved semantic segmentation of tuberculosis-consistent findings in chest X-rays using augmented training of modality-specific U-Net models with weak localizations. Diagnostics (Basel). 2021;11(4):616. https://doi.org/10.3390/diagnostics11040616.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology Division of Medical Technology, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan

    Shu Onodera, Shoko Ishizawa, Tomoyoshi Kawabata & Hiroki Ishii

  2. Graduate School of Health Sciences, Niigata University, 746 Asahimachidori 2bancho Chuo-ku, Niigata City, Niigata, 951-8518, Japan

    Shu Onodera & Yohan Kondo

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  1. Shu Onodera
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Correspondence to Shu Onodera.

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Onodera, S., Kondo, Y., Ishizawa, S. et al. Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area. Radiol Phys Technol 16, 299–309 (2023). https://doi.org/10.1007/s12194-023-00719-0

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  • DOI: https://doi.org/10.1007/s12194-023-00719-0

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