Imaging Science in Dentistry

  • 제50권2호
  • /
  • Pages.161-168
  • /
  • 2020
  • /
  • 2233-7822(pISSN)
  • /
  • 2233-7830(eISSN)
대한영상치의학회 (Korean Academy of Oral and Maxillofacial Radiology)
권호 표지
DOI QR코드

DOI QR Code

How image-processing parameters can influence the assessment of dental materials using micro-CT

  • Torres, Fernanda Ferrari Esteves (Department of Restorative Dentistry, Sao Paulo State University (UNESP), School of Dentistry) ;
  • Jacobs, Reinhilde (OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven and Oral and Maxillofacial Surgery, University Hospitals Leuven) ;
  • EzEldeen, Mostafa (OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven and Oral and Maxillofacial Surgery, University Hospitals Leuven) ;
  • de Faria-Vasconcelos, Karla (OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven and Oral and Maxillofacial Surgery, University Hospitals Leuven) ;
  • Guerreiro-Tanomaru, Juliane Maria (Department of Restorative Dentistry, Sao Paulo State University (UNESP), School of Dentistry) ;
  • dos Santos, Bernardo Camargo (Department of Nuclear Energy, Federal University of Rio de Janeiro (UFRJ)) ;
  • Tanomaru-Filho, Mario (Department of Restorative Dentistry, Sao Paulo State University (UNESP), School of Dentistry)
  • 투고 : 2019.11.19
  • 심사 : 2020.02.20
  • 발행 : 2020.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Purpose: The aim of this study was to evaluate the influence of voxel size and different post-processing algorithms on the analysis of dental materials using micro-computed tomography (micro-CT). Materials and Methods: Root-end cavities were prepared in extracted maxillary premolars, filled with mineral trioxide aggregate (MTA), Biodentine, and Intermediate Restorative Material (IRM), and scanned using micro-CT. The volume and porosity of materials were evaluated and compared using voxel sizes of 5, 10, and 20 ㎛, as well as different software tools(post-processing algorithms). The CTAn or MeVisLab/Materialise 3-matic software package was used to perform volume and morphological analyses, and the CTAn or MeVisLab/Amira software was used to evaluate porosity. Data were analyzed using 1-way ANOVA and the Tukey test(P<0.05). Results: Using MeVisLab/Materialise 3-matic, a consistent tendency was observed for volume to increase at larger voxel sizes. CTAn showed higher volumes for MTA and IRM at 20 ㎛. Using CTAn, porosity values decreased as voxel size increased, with statistically significant differences for all materials. MeVisLab/Amira showed a difference for MTA and IRM at 5 ㎛, and for Biodentine at 20 ㎛. Significant differences in volume and porosity were observed in all software packages for Biodentine across all voxel sizes. Conclusion: Some differences in volume and porosity were found according to voxel size, image-processing software, and the radiopacity of the material. Consistent protocols are needed for research evaluating dental materials.

키워드

참고문헌

  1. Mao T, Neelakantan P. Three-dimensional imaging modalities in endodontics. Imaging Sci Dent 2014; 44: 177-83. https://doi.org/10.5624/isd.2014.44.3.177
  2. Celikten B, Jacobs R, de Faria Vasconcelos K, Huang Y, Shaheen E, Nicolielo LF, et al. Comparative evaluation of cone beam CT and micro-CT on blooming artifacts in human teeth filled with bioceramic sealers. Clin Oral Investig 2019; 23: 3267-73. https://doi.org/10.1007/s00784-018-2748-8
  3. Acar B, Kamburoglu K, Tatar I, Arikan V, Celik HH, Yuksel S, et al. Comparison of micro-computerized tomography and conebeam computerized tomography in the detection of accessory canals in primary molars. Imaging Sci Dent 2015; 45: 205-11. https://doi.org/10.5624/isd.2015.45.4.205
  4. Tanomaru-Filho M, Torres FF, Chavez-Andrade GM, de Almeida M, Navarro LG, Steier L, et al. Physicochemical properties and volumetric change of silicone/bioactive glass and calcium silicate-based endodontic sealers. J Endod 2017; 43: 2097-101. https://doi.org/10.1016/j.joen.2017.07.005
  5. Marciano MA, Camilleri J, Costa RM, Matsumoto MA, Guimaraes BM, Duarte MA. Zinc oxide inhibits dental discoloration caused by white mineral trioxide aggregate angelus. J Endod 2017; 43: 1001-7. https://doi.org/10.1016/j.joen.2017.01.029
  6. Basturk FB, Nekoofar MH, Gunday M, Dummer PM. Effect of various mixing and placement techniques on the flexural strength and porosity of mineral trioxide aggregate. J Endod 2014; 40: 441-5. https://doi.org/10.1016/j.joen.2013.08.010
  7. Viapiana R, Moinzadeh AT, Camilleri L, Wesselink PR, Tanomaru Filho M, Camilleri J. Porosity and sealing ability of root fillings with gutta-percha and BioRoot RCS or AH Plus sealers. Evaluation by three ex vivo methods. Int Endod J 2016; 49: 774-82. https://doi.org/10.1111/iej.12513
  8. Kang SW, Lee WJ, Choi SC, Lee SS, Heo MS, Huh KH, et al. Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation. Imaging Sci Dent 2015; 45: 7-13. https://doi.org/10.5624/isd.2015.45.1.7
  9. Rovaris K, Queiroz PM, Vasconcelos KF, Corpas LD, Silveira BM, Freitas DQ. Segmentation methods for micro CT images: a comparative study using human bone samples. Braz Dent J 2018; 29: 150-3. https://doi.org/10.1590/0103-6440201801385
  10. Barrett WA, Mortensen EN. Interactive live-wire boundary extraction. Med Image Anal 1997; 1: 331-41. https://doi.org/10.1016/S1361-8415(97)85005-0
  11. Galibourg A, Dumoncel J, Telmon N, Calvet A, Michetti J, Maret D. Assessment of automatic segmentation of teeth using a watershed-based method. Dentomaxillofac Radiol 2018; 47: 20170220. https://doi.org/10.1259/dmfr.20170220
  12. de Oliveira MV, Santos AC, Paulo G, Campos PS, Santos J. Application of a newly developed software program for image quality assessment in cone-beam computed tomography. Imaging Sci Dent 2017; 47: 75-86. https://doi.org/10.5624/isd.2017.47.2.75
  13. Bouxsein ML, Boyd SK, Christiansen BA, Guldberg RE, Jepsen KJ, Muller R. Guidelines for assessment of bone microstructure in rodents using micro-computed tomography. J Bone Miner Res 2010; 25: 1468-86. https://doi.org/10.1002/jbmr.141
  14. Michetti J, Georgelin-Gurgel M, Mallet JP, Diemer F, Boulanouar K. Influence of CBCT parameters on the output of an automatic edge-detection-based endodontic segmentation. Dentomaxillofac Radiol 2015; 44: 20140413. https://doi.org/10.1259/dmfr.20140413
  15. Huybrechts B, Bud M, Bergmans L, Lambrechts P, Jacobs R. Void detection in root fillings using intraoral analogue, intraoral digital and cone beam CT images. Int Endod J 2009; 42: 675-85. https://doi.org/10.1111/j.1365-2591.2009.01566.x
  16. Cooper D, Turinsky A, Sensen C, Hallgrimsson B. Effect of voxel size on 3D micro-CT analysis of cortical bone porosity. Calcif Tissue Int 2007; 80: 211-9. https://doi.org/10.1007/s00223-005-0274-6
  17. Kline TL, Knudsen BE, Anderson JL, Vercnocke AJ, Jorgensen SM, Ritman EL. Anatomy of hepatic arteriolo-portal venular shunts evaluated by 3D micro-CT imaging. J Anat 2014; 224: 724-31. https://doi.org/10.1111/joa.12178
  18. Yamamoto-Silva FP, de Oliveira Siqueira CF, Silva MA, Fonseca RB, Santos AA, Estrela C, et al. Influence of voxel size on cone-beam computed tomography-based detection of vertical root fractures in the presence of intracanal metallic posts. Imaging Sci Dent 2018; 48: 177-84. https://doi.org/10.5624/isd.2018.48.3.177
  19. Jung M, Lommel D, Klimek J. The imaging of root canal obturation using micro-CT. Int Endod J 2005; 38: 617-26. https://doi.org/10.1111/j.1365-2591.2005.00990.x
  20. Inarejos Clemente EJ, Tolend M, Junhasavasdikul T, Stimec J, Tzaribachev N, Koos B, et al. Qualitative and semi-quantitative assessment of temporomandibular joint MRI protocols for juvenile idiopathic arthritis at 1.5 and 3.0T. Eur J Radiol 2018; 98: 90-9. https://doi.org/10.1016/j.ejrad.2017.10.024
  21. Waarsing JH, Day JS, Weinans H. An improved segmentation method for in vivo microCT imaging. J Bone Miner Res 2004; 19: 1640-50. https://doi.org/10.1359/jbmr.040705
  22. Spina TV, de Miranda PA, Falcao AX. Hybrid approaches for interactive image segmentation using the live markers paradigm. IEEE Trans Image Process 2014; 23: 5756-69. https://doi.org/10.1109/TIP.2014.2367319
  23. Heckel F, Konrad O, Karl Hahn H, Peitgen HO. Interactive 3D medical image segmentation with energy-minimizing implicit functions. Comput Graph 2011; 35: 275-87. https://doi.org/10.1016/j.cag.2010.12.006
  24. EzEldeen M, Van Gorp G, Van Dessel J, Vandermeulen D, Jacobs R. 3-dimensional analysis of regenerative endodontic treatment outcome. J Endod 2015; 41: 317-24. https://doi.org/10.1016/j.joen.2014.10.023
  25. Zamparini F, Siboni F, Prati C, Taddei P, Gandolfi MG. Properties of calcium silicate-monobasic calcium phosphate materials for endodontics containing tantalum pentoxide and zirconium oxide. Clin Oral Investig 2019; 23: 445-57. https://doi.org/10.1007/s00784-018-2453-7
  26. Bauer JS, Sidorenko I, Mueller D, Baum T, Issever AS, Eckstein F, et al. Prediction of bone strength by ${\mu}CT$ and MDCTbased finite-element-models: how much spatial resolution is needed? Eur J Radiol 2014; 83: e36-42. https://doi.org/10.1016/j.ejrad.2013.10.024
  27. Lucas CP, Viapiana R, Bosso-Martelo R, Guerreiro-Tanomaru JM, Camilleri J, Tanomaru-Filho M. Physicochemical properties and dentin bond strength of a tricalcium silicate-based retrograde material. Braz Dent J 2017; 28: 51-6. https://doi.org/10.1590/0103-6440201701135
  28. Choi JC, Choi CA, Yeo IL. Spiral scanning imaging and quantitative calculation of the 3-dimensional screw-shaped bone-implant interface on micro-computed tomography. J Periodontal Implant Sci 2018; 48: 202-12. https://doi.org/10.5051/jpis.2018.48.4.202
  29. Marinozzi F, Bini F, Marinozzi A, Zuppante F, De Paolis A, Pecci R, et al. Technique for bone volume measurement from human femur head samples by classification of micro-CT image histograms. Ann Ist Super Sanita 2013; 49: 300-5.
  30. Chen H, van Eijnatten M, Wolff J, de Lange J, van der Stelt PF, Lobbezoo F, et al. Reliability and accuracy of three imaging software packages used for 3D analysis of the upper airway on cone beam computed tomography images. Dentomaxillofac Radiol 2017; 46: 20170043. https://doi.org/10.1259/dmfr.20170043
  31. Queiroz PM, Rovaris K, Gaeta-Araujo H, Marzola de Souza Bueno S, Freitas DQ, Groppo FC, et al. Influence of artifact reduction tools in micro-computed tomography images for endodontic research. J Endod 2017; 43: 2108-11. https://doi.org/10.1016/j.joen.2017.07.024
  32. Sumitani Y, Hamba H, Nakamura K, Sadr A, Nikaido T, Tagami J. Micro-CT assessment of comparative radiopacity of adhesive/composite materials in a cylindrical cavity. Dent Mater J 2018; 37: 634-41. https://doi.org/10.4012/dmj.2017-310
  33. Kim JE, Yi WJ, Heo MS, Lee SS, Choi SC, Huh KH. Three-dimensional evaluation of human jaw bone microarchitecture: correlation between the microarchitectural parameters of cone beam computed tomography and micro-computer tomography. Oral Surg Oral Med Oral Pathol Oral Radiol 2015; 120: 762-70. https://doi.org/10.1016/j.oooo.2015.08.022
  34. Kerckhofs G, Schrooten J, Van Cleynenbreugel T, Lomov SV, Wevers M. Validation of x-ray microfocus computed tomography as an imaging tool for porous structures. Rev Sci Instrum 2008; 79: 013711. https://doi.org/10.1063/1.2838584
  35. Huang Y, Celikten B, de Faria Vasconcelos K, Ferreira Pinheiro Nicolielo L, Lippiatt N, Buyuksungur A, et al. Micro-CT and nano-CT analysis of filling quality of three different endodontic sealers. Dentomaxillofac Radiol 2017; 46: 20170223. https://doi.org/10.1259/dmfr.20170223
  36. Orhan K, Jacobs R, Celikten B, Huang Y, de Faria Vasconcelos K, Nicolielo LF, et al. Evaluation of threshold values for root canal filling voids in micro-CT and nano-CT images. Scanning 2018; 2018: 9437569. https://doi.org/10.1155/2018/9437569
  37. Parkinson IH, Badiei A, Fazzalari NL. Variation in segmentation of bone from micro-CT imaging: implications for quantitative morphometric analysis. Australas Phys Eng Sci Med 2008; 31: 160-4. https://doi.org/10.1007/BF03178592
  38. Dietrich CA, Scheidegger CE, Schreiner J, Comba JL, Nedel LP, Silva CT. Edge transformations for improving mesh quality of marching cubes. IEEE Trans Vis Comput Graph 2009; 15: 150-9. https://doi.org/10.1109/TVCG.2008.60
  39. Foteinos PA, Chernikov AN, Chrisochoides NP. Guaranteed quality tetrahedral Delaunay meshing for medical images. Comput Geom 2014; 47: 539-62. https://doi.org/10.1016/j.comgeo.2013.11.009
  40. Luo W, Pingel T, Heo J, Howard A, Jung J. A progressive black top hat transformation algorithm for estimating valley volumes on Mars. Comput Geosci 2015; 75: 17-23. https://doi.org/10.1016/j.cageo.2014.11.003
  41. Dalili Z, Taramsari M, Mousavi Mehr SZ, Salamat F. Diagnostic value of two modes of cone-beam computed tomography in evaluation of simulated external root resorption: an in vitro study. Imaging Sci Dent 2012; 42: 19-24. https://doi.org/10.5624/isd.2012.42.1.19
  42. Choi JW. Factors affecting modulation transfer function measurements in cone-beam computed tomographic images. Imaging Sci Dent 2019; 49: 131-7. https://doi.org/10.5624/isd.2019.49.2.131

피인용 문헌

  1. Evaluation of curved root canals filled with a new bioceramic sealer: A microcomputed tomographic study using images with different voxel sizes and segmentation methods vol.84, pp.12, 2020, https://doi.org/10.1002/jemt.23855
  2. How do imaging protocols affect the assessment of root-end fillings? vol.47, 2020, https://doi.org/10.5395/rde.2022.47.e2