Issa Ibraheem^*

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Department of Biomedical Engineering, Damascus University, Damascus, Syria.
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Abstract: Cone-beam CT (CBCT) scanners are based on volumetric tomography, using a 2D extended digital array providing an area detector [1,2]. Compared to traditional CT, CBCT has many advantages, such as less X-ray beam limitation, and rapid scan time, etc. However, in CBCT images the x-ray beam has lower mean kilovolt (peak) energy, so the metal artifact is more pronounced on. The position of the shadowed region in other views can be tracked by projecting the 3D coordinates of the object. Automatic image segmentation was used to replace the pixels inside the metal object with the boundary pixels. The modified projection data, using synthetically Radon Transformation, were then used to reconstruct a new back projected CBCT image. In this paper, we present a method, based on the morphological, area and pixel operators, which we applied on the Radon transformed image, to reduce the metal artifacts in CBCT, then we built the Radon back project images using the radon invers transformation. The artifacts effects on the 3d-reconstruction is that, the soft tissues appears as bones or teeth. For the preprocessing of the CBCT images, two methods are used to recognize the noisy black areas that the first depends on thresholding and closing algorithm, and the second depends on tracing boundaries after using thresholding algorithm too. The intensity of these areas is the lowest in the image than other tissues, so we profit this property to detect the edges of these areas. These two methods are applied on phantom and patient image data. It deals with reconstructed CBCT dicom images and can effectively reduce such metal artifacts. Due to the data of the constructed images are corrupted by these metal artifacts, qualitative and quantitative analysis of CBCT images is very essential.

Keywords: CBCT; Artifact; Medical Image Processing; CT; Image Reconstruction

Cite this paper: Ibraheem, I. (2012) Reduction of artifacts in dental cone beam CT images to improve the three dimensional image reconstruction. Journal of Biomedical Science and Engineering, 5, 409-415. doi: 10.4236/jbise.2012.58052.

References

[1]   Farman, A.G. and Scarfe, W. C. (2009) The basics of maxillofacial cone beam computed tomography. Seminars in Orthodontics, 15, 2-13.

[2]   Jeong, K.Y. and Ra, J.B. (2009) Reduction of artifacts due to multiple metallic objects in computed tomography. Seminars in Orthodontics, 15, 110-114.

[3]   Wang, G.N. and Humphreys, R.G. (2005) Computation on programmable graphics hardware. IEEE Computer Graphics and Applications, 25, 1215.

[4]   Miracle, A.C. and Mukherji, S.K. (2009) Cone beam CT of the head and neck, part 1. Physical Principles American Journal of Neuroradiology, 30, 1088-1095. doi:10.3174/ajnr.A1653

[5]   (2008) Image Processing Toolbox-Version 5.0 (R14). The Math Works, Inc.

[6]   Fuchs, H., Kedem, Z.M. and Uselton, S.P. (1977) Optimal Surface Reconstruction from Planar Contours. Communications of the ACM, 20, 693-702. doi:10.1145/359842.359846

[7]   Keppel, E. (1975) Approximating complex surfaces by triangulation of contour lines. IBM Journal of Research and Development, 19, 2-11. doi:10.1147/rd.191.0002

[8]   Christiansen, H.N. and Sederberg, T.W. (1978) Conversion of complex contour line definitions into polygonal element meshes. Computer Graphics, 12, 187-192. doi:10.1145/965139.807388

[9]   Bloch, P. and Udupa, J.K. (1983) Application of computerized tomography to radiation therapy and surgical planning. Proceedings of the IEEE, 71, 351-355. doi:10.1109/PROC.1983.12593

[10]   Hemmy, D.C., David, D.J. and Herman, G.T. (1983) Three-dimensional reconstruction of craniofacial deformity using computed tomography. Neurosurgery, 13, 534-541. doi:10.1227/00006123-198311000-00009

[11]   Vannier, M.W., Marsh, J.L. and Warren, J.O. (1984) Three dimensional ct reconstruction images for craniofacial surgical planning and evaluation. Radiology, 150, 179-184.

[12]   Benameur, S., et al. (2001) 3D biplanar reconstruction of sco liotic vertebrae using statistical models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 577-582.

[13]   Laporte, S., et al. (2003) A biplanar reconstruction method based on 2D and 3D contours: application to the distal femur. Computer Methods in Biomechanics and Biomedical Engineering, 6, 1-6. doi:10.1080/1025584031000065956

[14]   Le Bras, A., et al. (2004) 3D reconstruction of the proxiraphy. Computer Aided Surgery, 51-57.

[15]   Pomero, V., et al. (2004) Fast accurate stereoradiographic 3D-reconstruction of the spine using a combined geometric and statistic model. Clinical Biomechanics, 19, 240- 247. doi:10.1016/j.clinbiomech.2003.11.014

[16]   Cline, H.E., Dumoulin, C.L., Lorensen, W.E., Hart, H.R., and Ludke, S. (1987) 3D reconstruction of the brain from magnetic resonance images. Magnetic Resonance Imaging, 5, 345-352. doi:10.1016/0730-725X(87)90124-X

[17]   Cline, H.E., Lorensen, W.E., Ludke, S, Crawford, C.R. and Teeter, B.C. (1987) High-resolution three-dimensional reconstruction of tomograms. Medical Physics, 53.

[18]   Cook, L.T., Dwyer, S.J., Batnitzky, S. and Lee, K.R.A. (1983) Three-dimensional display system for diagnostic imaging applications. IEEE Computer Graphics and Applications, 3, 13-19. doi:10.1109/MCG.1983.263180

[19]   Farrell, E.J. (1983) Color display and interactive interpretation of three-dimensional data. IBM Journal of Research and Development, 27, 356-366. doi:10.1147/rd.274.0356

[20]   Farrell, E.J., Zappulla, R. and Yang, W.C. (1984) Color 3D imaging of normal and pathologic intracranial structures. IEEE Computer Graphics and Applications, 4, 5- 17.

[21]   Fuchs, H., Kedem, Z.M. and Uselton, S.P. (1977) Optimal Surface Reconstruction from Planar Contours. Communication of the ACM, 20, 693-702. doi:10.1145/359842.359846

[22]   Gordon, D. and Reynolds, R.A. (1985) Image space shading of 3-dimensional objects. Computer Graphics and Image Processing, 29, 361-376. doi:10.1016/0734-189X(85)90132-X