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Diagnostic value of model-based iterative reconstruction combined with metal artifact reduction algorithm during CT of the oral cavity

久保, 優子 東京慈恵会医科大学 DOI:info:doi/10.3174/ajnr.A6767

2021.10.22

概要

BACKGROUND AND PURPOSE: Metal artifacts reduce the quality of CT images and increase the difficulty of interpretation. This study compared the ability of model-based iterative reconstruction and hybrid iterative reconstruction to improve CT image qual- ity in patients with metallic dental artifacts when both techniques were combined with a metal artifact reduction algorithm.

MATERIALS AND METHODS: This retrospective clinical study included 40 patients (men, 31; women, 9; mean age, 62.9 6 12.3 years) with oral and oropharyngeal cancer who had metallic dental fillings or implants and underwent contrast-enhanced ultra-high-resolu- tion CT of the neck. Axial CT images were reconstructed using hybrid iterative reconstruction and model-based iterative recon- struction, and the metal artifact reduction algorithm was applied to all images. Finally, hybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithm data were obtained. In the quantitative analysis, SDs were measured in ROIs over the apex of the tongue (metal artifacts) and nuchal muscle (no metal artifacts) and were used to calculate the metal artifact indexes. In a qualitative analysis, 3 radiologists blinded to the patients’ con- ditions assessed the image-quality scores of metal artifact reduction and structural depictions.

RESULTS: Hybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithms yielded significantly different metal artifact indexes of 82.2 and 73.6, respectively (95% CI, 2.6–14.7; P < .01). The latter algorithms resulted in significant reduction in metal artifacts and significantly improved structural depictions (P < .01).

CONCLUSIONS: Model-based iterative reconstruction + metal artifact reduction algorithms significantly reduced the artifacts and improved the image quality of structural depictions on neck CT images.

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参考文献

1. Goerres GW, Hany TF, Kamel E, et al. Head and neck imaging with PET and PET/CT: artefacts from dental metallic implants. Eur J Nucl Med Mol Imaging 2002;29:367–70 CrossRef Medline

2. Barrett JF, Keat N. Artifacts in CT: recognition and avoidance. Radiographics 2004;24:1679–91 CrossRef Medline

3. Ravishankar S, Ye JC, Fessler JA. Image reconstruction: from spar- sity to data-adaptive methods and machine learning. Proc IEEE Inst Electr Electron Eng 2020;108:86–109 CrossRef Medline

4. Willemink MJ, Noel PB. The evolution of image reconstruction for CT: from filtered back projection to artificial intelligence. Eur Radiol 2019;29:2185–95 CrossRef Medline

5. Herman GT. Fundamentals of computerized tomography: image reconstruction from projections. Springer-Verlag: London, 2009 CrossRef

6. Singh S, Kalra MK, Hsieh J, et al. Abdominal CT: comparison of adaptive statistical iterative and filtered back projection recon- struction techniques. Radiology 2010;257:373–83 CrossRef Medline

7. Gervaise A, Osemont B, Lecocq S, et al. CT image quality improve- ment using adaptive iterative dose reduction with wide-volume ac- quisition on 320-detector CT. Eur Radiol 2012;22:295–301 CrossRef Medline

8. Martinsen ACT, Sæther HK, Hol PK, et al. Iterative reconstruction reduces abdominal CT dose. Eur J Radiol 2012;81:1483–87 CrossRef Medline

9. Yu Z, Thibault JB, Bouman CA, et al. Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimiza- tion. IEEE Trans Image Process 2011;20:161–75 CrossRef Medline

10. Fleischmann D, Boas FE. Computed tomograph: old ideas and new technology. Eur Radiol 2011;21:510–17 CrossRef Medline

11. Chang W, Lee JM, Lee K, et al. Assessment of a model-based, itera- tive reconstruction algorithm (MBIR) regarding image quality and dose reduction in liver computed tomography. Invest Radiol 2013;48:598–606 CrossRef Medline

12. Goodenberger MH, Wagner-Bartak NA, Gupta S, et al. Computed to- mography image quality evaluation of a new iterative reconstruction algorithm in the abdomen (adaptive statistical iterative reconstruc- tion-V): a comparison with model-based iterative reconstruction, adaptive statistical iterative reconstruction, and filtered back projec- tion reconstructions. J Comput Assist Tomogr 2018;42:184–90 CrossRef Medline

13. Noda Y, Goshima S, Koyasu H, et al. Renovascular CT: comparison between adaptive statistical iterative reconstruction and model-based iterative reconstruction. Clin Radiol 2017;72:901.e913–19 CrossRef Medline

14. Smith EA, Dillman JR, Goodsitt MM, et al. Model-based iterative reconstruction: effect on patient radiation dose and image quality in pediatric body CT. Radiology 2014;270:526–34 CrossRef Medline

15. Taguchi N, Oda S, Imuta M, et al. Model-based iterative reconstruc- tion in low-radiation-dose computed tomography colonography: preoperative assessment in patients with colorectal cancer. Acad Radiol 2018;25:415–22 CrossRef Medline

16. Yasaka K, Katsura M, Akahane M, et al. Model-based iterative reconstruction and adaptive statistical iterative reconstruction: dose-reduced CT for detecting pancreatic calcification. Acta Radiol Open 2016;5:205846011662834 CrossRef Medline

17. Yuki H, Oda S, Utsunomiya D, et al. Clinical impact of model-based type iterative reconstruction with fast reconstruction time on image quality of low-dose screening chest CT. Acta Radiol 2016;57:295–302 CrossRef Medline

18. De Crop A, Casselman J, Van Hoof T, et al. Analysis of metal arti- fact reduction tools for dental hardware in CT scans of the oral cavity: kVp, iterative reconstruction, dual-energy CT, metal arti- fact reduction software—does it make a difference? Neuroradiology 2015;57:841–49 CrossRef Medline

19. Boudabbous S, Arditi D, Paulin E, et al. Model-based iterative recon- struction (MBIR) for the reduction of metal artifacts on CT. AJR Am J Roentgenol 2015;205:380–85 CrossRef Medline

20. Kuya K, Shinohara Y, Kato A, et al. Reduction of metal artifacts due to dental hardware in computed tomography angiography: assessment of the utility of model-based iterative reconstruction. Neuroradiology 2017;59:231–35 CrossRef Medline

21. Yasaka K, Kamiya K, Irie R, et al. Metal artefact reduction for patients with metallic dental fillings in helical neck computed to- mography: comparison of adaptive iterative dose reduction 3D (AIDR 3D), forward-projected model-based iterative reconstruc- tion solution (FIRST) and AIDR 3D with single-energy metal arte- fact reduction (SEMAR). Dentomaxillofac Radiol 2016;45:20160114 CrossRef Medline

22. Li H, Noel C, Chen H, et al. Clinical evaluation of a commercial or- thopedic metal artifact reduction tool for CT simulations in radia- tion therapy. Med Phys 2012;39:7507–17 CrossRef Medline

23. Brook OR, Gourtsoyianni S, Brook A, et al. Spectral CT with metal artifacts reduction software for improvement of tumor visibility in the vicinity of gold fiducial markers. Radiology 2012;263:696–705 CrossRef Medline

24. Lell MM, Meyer E, Kuefner MA, et al. Normalized metal artifact reduction in head and neck computed tomography. Invest Radiol 2012;47:415–21 CrossRef Medline

25. Kidoh M, Nakaura T, Nakamura S, et al. Reduction of dental metal- lic artefacts in CT: value of a newly developed algorithm for metal artefact reduction (O-MAR). Clin Radiol 2014;69:e11–16 CrossRef Medline

26. Diehn FE, Michalak GJ, DeLone DR, et al. CT dental artifact: compar- ison of an iterative metal artifact reduction technique with weighted filtered back-projection. Acta Radiol Open 2017;6:205846011774327 CrossRef Medline

27. Hakim A, Slotboom J, Lieger O, et al. Clinical evaluation of the iter- ative metal artefact reduction algorithm for post-operative CT ex- amination after maxillofacial surgery. Dentomaxillofac Radiol 2017;46:20160355 CrossRef Medline

28. Weiß J, Schabel C, Bongers M, et al. Impact of iterative metal arti- fact reduction on diagnostic image quality in patients with dental hardware. Acta Radiol 2017;58:279–85 CrossRef Medline

29. Niehues SM, Vahldiek JL, Tröltzsch D, et al. Impact of single-energy metal artifact reduction on CT image quality in patients with den- tal hardware. Comput Biol Med 2018;103:161–66 CrossRef Medline

30. Lubner MG, Pickhardt PJ, Tang J, et al. Reduced image noise at low- dose multidetector CT of the abdomen with prior image con- strained compressed sensing algorithm. Radiology 2011;260:248–56 CrossRef Medline

31. Lin XZ, Miao F, Li JY, et al. High-definition CT Gemstone spec- tral imaging of the brain: initial results of selecting optimal mono- chromatic image for beam-hardening artifacts and image noise reduction. J Comput Assist Tomogr 2011;35:294–97 CrossRef

32. Wang Y, Qian B, Li B, et al. Metal artifacts reduction using mono- chromatic images from spectral CT: evaluation of pedicle screws in patients with scoliosis. Eur J Radiol 2013;82:e360–66 CrossRef Medline

33. Lydiatt WM, Patel SG, O’Sullivan B, et al. Head and neck cancers: major changes in the American Joint Committee on Cancer Eighth Edition Cancer Staging Manual. CA Cancer J Clin 2017;67:122–37 CrossRef Medline

34. Svanholm H, Starklint H, Gundersen HJ, et al. Reproducibility of histomorphologic diagnoses with special reference to the kappa statistic. APMIS 1989;97:689–98 CrossRef Medline

35. Blatt S, Ziebart T, Krüger M, et al. Diagnosing oral squamous cell carcinoma: How much imaging do we really need? A review of the current literature. J Craniomaxillofac Surg 2016;44:538–49 CrossRef Medline

36. Cha J, Kim HJ, Kim ST, et al. Dual-energy CT with virtual mono- chromatic images and metal artifact reduction software for reduc- ing metallic dental artifacts. Acta Radiol 2017;58:1312–19 CrossRef Medline

37. Laukamp KR, Zopfs D, Lennartz S, et al. Metal artifacts in patients with large dental implants and bridges: combination of metal arti- fact reduction algorithms and virtual monoenergetic images pro- vides an approach to handle even strongest artifacts. Eur Radiol 2019;29:4228–38 CrossRef Medline

38. Toso S, Laurent M, Lozeron ED, et al. Iterative algorithms for metal artifact reduction in children with orthopedic prostheses: prelimi- nary results. Pediatr Radiol 2018;48:1884–90 CrossRef Medline

39. Wellenberg RH, Boomsma MF, van Osch JA, et al. Computed to- mography imaging of a hip prosthesis using iterative model-based reconstruction and orthopaedic metal artefact reduction: a quanti- tative analysis. J Comput Assist Tomogr 2016;40:971–78 CrossRef Medline

40. Neroladaki A, Martin SP, Bagetakos I, et al. Metallic artifact reduc- tion by evaluation of the additional value of iterative reconstruc- tion algorithms in hip prosthesis computed tomography imaging. Medicine 2019;98:e14341 CrossRef Medline

41. Tsuchida Y, Takahashi H, Watanabe H, et al. Effects of number of metal restorations and mandibular position during computed to- mography imaging on accuracy of maxillofacial models. J Prosthodont Res 2019;63:239–44 CrossRef Medline

42. Motoyama S, Ito H, Sarai M, et al. Ultra-high-resolution computed tomography angiography for assessment of coronary artery steno- sis. Circ J 2018;82:1844–51 CrossRef Medline

43. Faber J, Fonseca LM. How sample size influences research out- comes. Dental Press J Orthod 2014;19:27–29 CrossRef Medline

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