Comparison of Image Quality According To Application of CT Algorithms for Acquisition of Clinical Information: A Phantom Study

Document Type : Original Paper


1 Seoul National University Bundang Hospital, Department of Radiology, Department of Healthcare, Graduate School ,Eulji University, Republic of Korea

2 Department of Radiological Science, Seongnam-si, Gyeonggi-do, Republic of Korea


Introduction: While various algorithms are applied in acquiring diagnostic information during computed tomography, such algorithms may affect image quality. The present study aimed to investigate the changes in image quality according to the application of the metal reduction algorithm and monoenergetic image in standard imaging.
Material and Methods: Spectral computed tomography was used to acquire images with the application of standard, metal artifact reduction, monoenergetic, and monoenergetic+metal artifact reduction under the same conditions according to without or with of metal in ACR phantom. ImageJ program was used to measure the HU, noise, and SNR of polyethylene, bone, and acrylic located inside the ACR phantom using the same-sized ROIs.
Results: HU measurement results showed changes in all materials, except acrylic with metal artifacts in the images. Moreover, the results showed a decrease in HU in images with the application of monoenergetic. Noise measurement results also showed changes in all materials, except acrylic with metal artifacts in the images. Moreover, the results showed a decrease in noise in images with the application of monoenergetic. For SNR measured relative to standard images, the results showed degradation of image quality due to a decrease of 36.5–77.7% in SNR and an increase in error value in all materials except acrylic. Whereas, acrylic showed an increase of 3.2–4.1% and a decrease in error values, resulting in improved image quality.
Conclusion: Therefore, it is believed that the accuracy of reading could be increased by considering the changes in image quality and characteristics when applying algorithms for acquiring clinical information from CT.


Main Subjects

  1. Lell MM, Kachelrieß M. Recent and upcoming technological developments in computed tomography: high speed, low dose, deep learning, multienergy. Investigative radiology. 2020 Jan 1;55(1):8-19.
  2. Nielsen JS, Van Leemput K, Edmund JM. MR‐based CT metal artifact reduction for head‐and‐neck photon, electron, and proton radiotherapy. Medical physics. 2019 Oct;46(10):4314-23.
  3. Hong J, Choi J, Kim K, Ahn J. Evaluation of Radiation Treatment Planning By Computed Tomography Metal Artifact Reduction Algorithm. Iranian Journal of Medical Physics. 2021 Nov 1;18(6).
  4. Zhang Y, Yan H, Jia X, Yang J, Jiang SB, Mou X. A hybrid metal artifact reduction algorithm for x‐ray CT. Medical physics. 2013 Apr;40(4):041910.
  5. Healthcare P. Metal artifact reduction for orthopedic implants (O-MAR). White Paper, Philips CT Clinical Science, Andover, Massachusetts. 2012.
  6. Albrecht MH, Vogl TJ, Martin SS, Nance JW, Duguay TM, Wichmann JL, et al. Review of clinical applications for virtual monoenergetic dual-energy CT. Radiology. 2019 Nov;293(2):260-71.
  7. Pessis E, Sverzut JM, Campagna R, Guerini H, Feydy A, Drape JL. Reduction of metal artifact with dual-energy CT: virtual monospectral imaging with fast kilovoltage switching and metal artifact reduction software. InSeminars in musculoskeletal radiology. 2015 ; 19(05): 446-55.
  8. Kwon H, Kim KS, Chun YM, Wu HG, Carlson JN, Park JM, et al. Evaluation of a commercial orthopaedic metal artefact reduction tool in radiation therapy of patients with head and neck cancer. The British journal of radiology. 2015 Aug;88(1052):20140536.
  9. Kwan AC, Pourmorteza A, Stutman D, Bluemke DA, Lima JA. Next-generation hardware advances in CT: cardiac applications. Radiology. 2021 Jan;298(1):3-17.
  10. Batawil N, Sabiq S. Hounsfield unit for the diagnosis of bone mineral density disease: a proof of concept study. Radiography. 2016 May 1;22(2):e93-8.
  11. Gjesteby L, Yang Q, Xi Y, Zhou Y, Zhang J, Wang G. Deep learning methods to guide CT image reconstruction and reduce metal artifacts. Physics of medical imaging. 2017 Mar 10132:752-8
  12. Ye N, Jian F, Xue J, Wang S, Liao L, Huang W, et al. Accuracy of in-vitro tooth volumetric measurements from cone-beam computed tomography. American journal of orthodontics and dentofacial orthopedics. 2012 Dec 1;142(6):879-87.
  13. Verdun FR, Racine D, Ott JG, Tapiovaara MJ, Toroi P, Bochud FO, et al. Image quality in CT: From physical measurements to model observers. Physica Medica. 2015 Dec 1;31(8):823-43.
  14. Diwakar M, Kumar M. A review on CT image noise and its denoising. Biomedical Signal Processing and Control. 2018 Apr 1;42:73-88.
  15. Diwakar M, Kumar M. A review on CT image noise and its denoising. Biomedical Signal Processing and Control. 2018 Apr 1;42:73-88.
  16. Cheraya G, Sharma S, Chhabra A. Dual energy CT in musculoskeletal applications beyond crystal imaging: bone marrow maps and metal artifact reduction. Skeletal radiology. 2022 Aug;51(8):1521-34.
  17. Katsura M, Sato J, Akahane M, Kunimatsu A, Abe O. Current and novel techniques for metal artifact reduction at CT: practical guide for radiologists. Radiographics. 2018 Mar;38(2):450-61.
  18. Al-Baldawi Y, Hokamp NG, Haneder S, Steinhauser S, Püsken M, Persigehl T, et al. Virtual mono-energetic images and iterative image reconstruction: abdominal vessel imaging in the era of spectral detector CT. Clinical Radiology. 2020 Aug 1;75(8):641-e9.
  19. Gao Z, Meng D, Lu H, Yao B, Huang N, Ye Z. Utility of dual‐energy spectral CT and low‐iodine contrast medium in DIEP angiography. International Journal of Clinical Practice. 2016 Sep;70(9B):B64-71.
  20. Wichmann JL, Nöske EM, Kraft J, Burck I, Wagenblast J, Eckardt A, et al. Virtual monoenergetic dual-energy computed tomography: optimization of kiloelectron volt settings in head and neck cancer. Investigative radiology. 2014 Nov 1;49(11):735-41.
  21. Albrecht MH, Scholtz JE, Hüsers K, Beeres M, Bucher AM, Kaup M, et al. Advanced image-based virtual monoenergetic dual-energy CT angiography of the abdomen: optimization of kiloelectron volt settings to improve image contrast. European radiology. 2016 Jun;26:1863-70.
  22. Neuhaus V, Hokamp NG, Abdullayev N, Rau R, Mpotsaris A, Maintz D, et al. Metal artifact reduction by dual-layer computed tomography using virtual monoenergetic images. European journal of radiology. 2017 Aug 1;93:143-8.






Volume 20, Issue 3
May and June 2023
Pages 153-158
  • Receive Date: 14 April 2022
  • Revise Date: 08 July 2022
  • Accept Date: 17 July 2022
  • First Publish Date: 17 July 2022