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Introduction: Metal Artifact Reduction (MAR) processing has been clinically applied to computed tomography (CT) images using various methods. Iterative MAR (iMAR) is an algorithm for reducing metal artifacts from implants and is tailored to the type, shape, and imaging site of a metal object. Various implants have been targeted using iMAR; however, there are some implants and metals that do not have a dedicated iMAR. The potential of iMAR for managing such artifacts has not yet been explored. Utilizing iMAR in unavoidable extracorporeal metal artifact cases could improve diagnosis. We aimed to assess whether the iMAR reduces extracorporeal metal artifacts and enhances image quality. Material and methods: CT was performed on a whole-body phantom with electrocardiogram (ECG) electrodes attached. Images were obtained without the iMAR and with eight different iMAR processings. The CT value profiles were perpendicular to the direction of artifact generation, and the maximum adjacent CT value difference was extracted from each CT value profile as the largest variation. The cumulative probabilities for the largest variations were obtained, and the location and scale parameters were calculated from the cumulative probability plots. Kruskal–Wallis tests and multiple comparisons were performed on nine different images. Results: Regarding the 100 cumulative probability plots of the largest variations obtained from each CT value profile, the coefficients of determination (R2) for all cumulative probability plots were as high as > 0.84, indicating that the features of the extracorporeal metal artifact generated from the ECG electrodes evaluated in this study asymptotically approached a Gumbel distribution. The location parameters showed no significant differences among the nine processed images (p > 0.11), whereas the scale parameters showed significant differences for neuro coil, shoulder implant, extremity implant, and thoracic coil iMAR-processed images compared with controls (p < 0.05). Conclusion: iMAR may improve diagnosis by reducing extracorporeal metal artifacts and enhancing image quality.
Słowa kluczowe
Rocznik
Tom
Strony
189--196
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
autor
- Graduate School of Health Science, Suzuka University of Medical Science, Suzuka-city, Mie, Japan
- Department of Radiological Technology, Faculty of Health Science, Suzuka University of Medical Science, Suzuka-city, Mie, Japan
autor
- Graduate School of Health Science, Suzuka University of Medical Science, Suzuka-city, Mie, Japan
- Department of Radiological Technology, Faculty of Health Science, Suzuka University of Medical Science, Suzuka-city, Mie, Japan
Bibliografia
- 1. Ichikawa K. CT super basic. Tokyo, Japan: Ohmsha; 2015.
- 2. 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;38(2):450-461. https://doi.org/10.1148/rg.2018170102
- 3. Greffier J, Larbi A, Frandon J, Daviau PA, Beregi JP, Pereira F. Influence of iterative reconstruction and dose levels on metallic artifact reduction: a phantom study within four CT systems. Diagn Interv Imaging. 2019;100(5):269-277. https://doi.org/10.1016/j.diii.2018.12.007
- 4. Huang JY, Kerns JR, Nute JL, et al. An evaluation of three commercially available metal artifact reduction methods for CT imaging. Phys Med Biol. 2015;60(3):1047-1067. https://doi.org/10.1088/0031-9155/60/3/1047
- 5. Selles M, van Osch JAC, Maas M, Boomsma MF, Wellenberg RHH. Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques. Eur J Radiol. 2024;170:111276. https://doi.org/10.1016/j.ejrad.2023.111276
- 6. Hauser TK, Oergel A, Hurth H, Ernemann U, Seeger A. Artifact reduction in the diagnosis of vasospasm in computed tomographic perfusion: potential of iterative metal artifact reduction. J Comput Assist Tomogr. 2019;43(4):553-558. https://doi.org/10.1097/rct.0000000000000879
- 7. Aissa J, Boos J, Sawicki LM, et al. Iterative metal artifact reduction (MAR) in postsurgical chest CT: comparison of three iMAR-algorithms. Br J Radiol. 2017;90(1079):20160778. https://doi.org/10.1259/bjr.20160778
- 8. Pagniez J, Legrand L, Khung S, et al. Metal artifact reduction on chest computed tomography examinations: comparison of the iterative metallic artifact reduction algorithm and the monoenergetic approach. J Comput Assist Tomogr. 2017;41(3):446-454. https://doi.org/10.1097/rct.0000000000000544
- 9. Boomsma MF, Warringa N, Edens MA, et al. Quantitative analysis of orthopedic metal artifact reduction in 64-slice computed tomography scans in large head metal-on-metal total hip replacement, a phantom study. Springerplus. 2016;5:405. https://doi.org/10.1186/s40064-016-2006-y
- 10. Pan YN, Chen G, Li AJ, et al. Reduction of metallic artifacts of the post-treatment intracranial aneurysms: effects of single Energy metal artifact reduction algorithm. Clin Neuroradiol. 2019;29(2):277-284. https://doi.org/10.1007/s00062-017-0644-2
- 11. Aissa J, Thomas C, Sawicki LM, et al. Iterative metal artifact reduction in CT: can dedicated algorithms improve image quality after spinal instrumentation? Clin Radiol. 2017;72(5):428.e7-428.e12. https://doi.org/10.1016/j.crad.2016.12.006
- 12. Neroladaki A, Martin SP, Bagetakos I, et al. Metallic artifact reduction by evaluation of the additional value of iterative reconstruction algorithms in hip prosthesis computed tomography imaging. Medicine (Baltimore). 2019;98(6):e14341. https://doi.org/10.1097/md.0000000000014341
- 13. Hoyoshi K, Satou T, Okada A. [Effect of hybrid iterative reconstruction on CT image quality using metal artifact reduction]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2018;74(8):797-804. Japanese. https://doi.org/10.6009/jjrt.2018_jsrt_74.8.797
- 14. Takayanagi T, Arai T, Amanuma M, et al. [Pacemaker-induced metallic artifacts in coronary computed tomography angiography: clinical feasibility of single energy metal artifact reduction technique]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2017;73(6):460-466. Japanese. https://doi.org/10.6009/jjrt.2017_jsrt_73.6.460
- 15. Nagayama Y, Tanoue S, Oda S, et al. Metal artifact reduction in head CT performed for patients with deep brain stimulation devices: effectiveness of a single-energy metal artifact reduction algorithm. AJNR Am J Neuroradiol. 2020;41(2):231-237. https://doi.org/10.3174/ajnr.a6375
- 16. Tsuboi K, Fukunaga M, Yamamoto H. [The effect of metal artifact reduction at different calibrated and display field of views in computed tomography]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2016;72(12):1237-1244. Japanese. https://doi.org/10.6009/jjrt.2016_jsrt_72.12.1237
- 17. Takada K, Ichikawa K, Banno S, Otobe K. [Suggestion of the relative artifact index for noise-independent evaluation of the streak artifact]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2018;74(4):315-325. Japanese. https://doi.org/10.6009/jjrt.2018_jsrt_74.4.315
- 18. Imai K, Ikeda M, Wada S, et al. Analysis of streak artifacts on CT images using statistics of extremes. Br J Radiol. 2007;80(959):911-918. https://doi.org/10.1259/bjr/93741044
- 19. Nakamura S, Kawata H, Kuroki H, Mizoguchi A. [Effect of reconstruction technique for metal artifact reduction in computed tomography by changing display field of view]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2015;71(11):1096-1102. Japanese. https://doi.org/10.6009/jjrt.2015_jsrt_71.11.1096
- 20. Kitaguchi S, Imai K, Ueda S, et al. [Quantitative evaluation of metal artifacts on CT images on the basis of statistics of extremes]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2016;72(5):402-409. Japanese. https://doi.org/10.6009/jjrt.2016_jsrt_72.5.402
- 21. Nakane J, Kobayashi Y, Shiozawa T. [Isotropic evaluation of streak artifact using extreme value statistical analysis]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2015;71(12):1165-1173. Japanese. https://doi.org/10.6009/jjrt.2015_jsrt_71.12.1165
- 22. Nomura Y, Watanabe H, Manila NG, Asai S, Kurabayashi T. Evaluation of streak metal artifacts in cone beam computed tomography by using the Gumbel distribution: a phantom study. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;131(4):494-502. https://doi.org/10.1016/j.oooo.2020.08.031
- 23. Imai K, Ikeda M, Enchi Y, Niimi T. Quantitative assessment of image noise and streak artifact on CT image: comparison of z-axis automatic tube current modulation technique with fixed tube current technique. Comput Med Imaging Graph. 2009;33(5):353-358. https://doi.org/10.1016/j.compmedimag.2009.02.003
- 24. Imai K, Ikeda M, Enchi Y, Niimi T. Statistical characteristics of streak artifacts on CT images: relationship between streak artifacts and mA s values. Med Phys. 2009;36(2):492-499. https://doi.org/10.1118/1.3056554
- 25. Ishikawa T, Suzuki S, Harashima S, Fukui R, Kaiume M, Katada Y. Metal artifacts reduction in computed tomography: A Phantom study to compare the effectiveness of metal artifact reduction algorithm, model-based iterative reconstruction, and virtual monochromatic imaging. Medicine (Baltimore). 2020 11;99(50):e23692. https://doi.org/10.1097/md.0000000000023692
- 26. Wayer DR, Kim NY, Otto BJ, Grayev AM, Kuner AD. Unintended consequences: review of new artifacts introduced by iterative reconstruction CT metal artifact reduction in spine imaging. AJNR Am J Neuroradiol. 2019;40(11):1973-1975. https://doi.org/10.3174/ajnr.a6238
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f39cee02-40d5-4a4e-a675-c633baed916a
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