Abstract
Metal implants often produce severe artifacts in the reconstructed computed tomography (CT) images, causing information and image detail loss and making the CT images diagnostically unusable. In order to eliminate the metal artifacts and enhance the diagnostic value of the reconstructed CT images, a post-processing metal artifact reduction algorithm, based on a tissue-class model segmented by thresholding and k-means clustering with spatial information, is proposed. The image inpainting technique is incorporated into the algorithm to improve the segmentation accuracy for CT images severely corrupted by metal artifacts. A study of a water phantom and of two sets of clinical CT images was performed to test the algorithm performance. The proposed method effectively eliminates typical metal artifacts, restores the average CT numbers of different tissues to the proper levels, and preserves the edge and contrast information, thus allowing the accurate reconstruction of the tissue attenuation map. The quality of the artifact-corrected CT images allows them to be subsequently used in other clinical applications, such as three-dimensional rendering, dose estimation for radiotherapy, attenuation correction for PET and SPECT, etc. The algorithm does not rely on the use of the raw sinogram and so is not limited by the proprietary format restrictions.
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References
Boas FE, Fleischmann D: Evaluation of two iterative techniques for reducing metal artifacts in computed tomography. Radiology 259(3):894–902, 2011. https://doi.org/10.1148/radiol.11101782
Mouton A, Megherbi N, Van Slambrouck K, Nuyts J, Breckon TP: An experimental survey of metal artefact reduction in computed tomography. J Xray Sci Technol 21(2):193–226, 2013. https://doi.org/10.3233/XST-130372
De Man B, Nuyts J, Dupont P, Marchal G, Suetens P: Metal streak artifacts in x-ray computed tomography: a simulation study. IEEE Trans Nucl Sci 46(3):691–696, 1999. https://doi.org/10.1109/23.775600
Karimi S, Cosman P, Wald C, Martz H: Segmentation of artifacts and anatomy in CT metal artifact reduction. Med Phys 39(10):5857–5868, 2012. https://doi.org/10.1118/1.4749931
Yu H, Zeng K, Bharkhada DK, Wang G, Madsen MT, Saba O, Policeni B, Howard MA, Smoker WRK: A segmentation-based method for metal artifact reduction. Acad Radiol 14(4):495–504, 2007. https://doi.org/10.1016/j.acra.2006.12.015
Comaniciu D, Meer P: Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619, 2002. https://doi.org/10.1109/34.1000236
Li Y, Bao X, Yin X, Chen Y, Luo L, Chen W: Metal artifact reduction in CT based on adaptive steering filter and nonlocal sinogram inpainting. In: Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics, 2010, pp. 380–383. https://doi.org/10.1109/BMEI.2010.5639535
Bal M, Spies L: Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering. Med Phys 33(8):2852–2859, 2006. https://doi.org/10.1118/1.2218062
Klotz E, Kalender WA, Sokiransky R, Felsenberg D: Algorithms for the reduction of CT artifacts caused by metallic implants. Proc SPIE 1234:642–650, 1990. https://doi.org/10.1117/12.18985
Abdoli M, Ay MR, Ahmadian A, Zaidi H: A virtual sinogram method to reduce dental metallic implant artefacts in computed tomography-based attenuation correction for PET. Nucl Med Commun 31(1):22–31, 2010. https://doi.org/10.1097/MNM.0b013e32832fa241
Meyer E, Raupach R, Lell M, Schmidt B, Kachelriess M: Normalized metal artifact reduction (NMAR) in computed tomography. Med Phys 37(10):5482–5493, 2010. https://doi.org/10.1118/1.3484090
Abdoli M, Ay MR, Ahmadian A, Dierckx RAJO, Zaidi H: Reduction of dental filling metallic artifacts in CT-based attenuation correction of PET data using weighted virtual sinograms optimized by a genetic algorithm. Med Phys 37(12):6166–6177, 2010. https://doi.org/10.1118/1.3511507
Dempster AP, Laird NM, Rubin DB: Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Methodol 39(1):1–38, 1977 http://www.jstor.org/stable/2984875
Shepp LA, Vardi Y: Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging 1(2):113–122, 1982. https://doi.org/10.1109/TMI.1982.4307558
Gordon R, Bender R, Herman GT: Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. J Theor Biol 29(3):471–481, 1970. https://doi.org/10.1016/0022-5193(70)90109-8
Nuyts J, De Man B, Dupont P, Defrise M, Suetens P, Mortelmans L: Iterative reconstruction for helical CT: a simulation study. Phys Med Biol 43(4):729–737, 1998. https://doi.org/10.1088/0031-9155/43/4/003
Elbakri IA, Fessler JA: Segmentation-free statistical image reconstruction for polyenergetic x-ray computed tomography with experimental validation. Phys Med Biol 48(15):2453–2477, 2003. https://doi.org/10.1088/0031-9155/48/15/314
Prell D, Kyriakou Y, Beister M, Kalender WA: A novel forward projection-based metal artifact reduction method for flat-detector computed tomography. Phys Med Biol 54(21):6575–6591, 2009. https://doi.org/10.1088/0031-9155/54/21/009
Lemmens C, Faul D, Nuyts J: Suppression of metal artifacts in CT using a reconstruction procedure that combines MAP and projection completion. IEEE Trans Med Imaging 28(2):250–260, 2009. https://doi.org/10.1109/TMI.2008.929103
Naranjo V, Llorens R, Alcaniz M, Verdu-Monedero R, Larrey-Ruiz J, Morales-Sanchez J: A new 3D paradigm for metal artifact reduction in dental CT. In: Proceedings of the 18th IEEE International Conference on Image Processing, 2011, pp. 461–464. https://doi.org/10.1109/ICIP.2011.6116551
Naranjo V, Llorens R, Paniagua P, Alcaniz M, Albalat S: A new approach in metal artifact reduction for CT 3D reconstruction. In: Mira J, Ferrandez JM, Alvarez Sanchez JR, Paz F, Toledo J, editors: Bioinspired Applications in Artificial and Natural Computation, Berlin: Springer-Verlag, pp. 11–19, 2009. https://doi.org/10.1007/978-3-642-02267-8_2
Serifovic-Trbalic A, Trbalic A: Image-based metal artifact reduction in CT images. In: Proceedings of the 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2016, pp. 442–446. https://doi.org/10.1109/MIPRO.2016.7522184
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 38(2):450–461, 2017. https://doi.org/10.1148/rg.2018170102
Gjesteby L, De Man B, Jin Y, Paganetti H, Verburg J, Giantsoudi D, Wang G: Metal artifact reduction in CT: where are we after four decades? IEEE Access 4:5826–5849, 2016. https://doi.org/10.1109/ACCESS.2016.2608621
Giantsoudi D, De Man B, Verburg J, Trofimov A, Jin Y, Wang G, Gjesteby L, Paganetti H: Metal artifacts in computed tomography for radiation therapy planning: dosimetric effects and impact of metal artifact reduction. Phys Med Biol 62(8):R49–R80, 2017. https://doi.org/10.1088/1361-6560/aa5293
Wu J, Shih CT, Chang SJ, Huang TC, Sun JY, Wu TH: Metal artifact reduction algorithm based on model images and spatial information. Nucl Inst Methods Phys Res A 652(1):602–605, 2011. https://doi.org/10.1016/j.nima.2011.01.041
Hegazy MAA, Cho MH, Lee SY: A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation. Biomed Eng Online 15(119), 2016. https://doi.org/10.1186/s12938-016-0240-8
Nam H, Baek J: A metal artifact reduction algorithm in CT using multiple prior images by recursive active contour segmentation. PLoS One 12(6):e0179022, 2017. https://doi.org/10.1371/journal.pone.0179022
Duda RO, Hart PE: Pattern classification and scene analysis, 1st ed. New York: John Wiley & Sons, 1973
Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ: Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15, 2006. https://doi.org/10.1016/j.compmedimag.2005.10.001
Bertalmio M, Sapiro G, Caselles V, Ballester C: Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, 2000, pp. 417–424. https://doi.org/10.1145/344779.344972
Lyra M, Ploussi A: Filtering in SPECT image reconstruction. Int J Biomed Imaging 2011:693795, 2011. https://doi.org/10.1155/2011/693795
Acknowledgements
The authors thank Dr. Cheng-Ting Shih for his contribution during the initial phase of this study. This work was financially supported by the Ministry of Science and Technology of Taiwan (MOST 104-2314-B-010-066-MY3.)
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Luzhbin, D., Wu, J. Model Image-Based Metal Artifact Reduction for Computed Tomography. J Digit Imaging 33, 71–82 (2020). https://doi.org/10.1007/s10278-019-00210-6
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DOI: https://doi.org/10.1007/s10278-019-00210-6