Evaluation of the effect of filters on reconstructed image quality from cone beam CT system
Main Article Content
Abstract
: 3D Filtered Back Projection (FBP) is a three-dimensional reconstruction algorithm usually used in Cone Beam Computed Tomography (CBCT) system. FBP is one of the most popular algorithms due to its reconstruction is fast while quality of the result is acceptable. It can also handle a more considerable amount of data with same computer performance with other algorithms. However, the quality of a reconstructed image by the FBP algorithm strongly depends on spatial filters and denoising filters applied to projections. In this paper an evaluation of the reconstructed image quality of the CBCT system by using different denoising filters and spatial filters to find out the best filters for the CBCT system is performed. The result shows that, there is a significantly decrease of the noise of projection with the combination of Median and Gaussian filters. The reconstructed image has high resolution with Cosine filter and becomes more sharpen with Hanning filter.
Article Details
Keywords
Cone-Beam CT, FBP, filter, reconstruction
References
[2]. Jerrold T. Bushberg, J. Anthony Seibert, Edwin M. Leidholdt JR, John M. Boone, "The essential physics of medical imaging", 3rd edition, Lippncott William & Wilkins, p70-72, 91-92, 312-318, 2012.
[3]. J.P. Kruth, M. Bartscher, S. Carmignato, L. De Chiffre, A. Weckenmann, “Computed Tomography for dimensional metrology”, CIRP Annals – Manufacturing Technology 60, 655-677, 2011.
[4]. L. De Chiffre, S. Carmignato, J.P. Kruth, R. Schmitt, A. Weckenmann, “Industrial application of Computed Tomography”, CIRP Annals – Manufacturing Technology 63, 655-677, 2014.
[5]. Jia, X., Dong, B., Lou, Y., & Jiang, S. B., “GPU-based iterative cone-beam CT reconstruction using tight frame regularization”, Physics in Medicine & Biology, 56(13), 3787, 2011.
[6]. Hsieh, J., Nett, B., Yu, Z., Sauer, K., Thibault, J. B., & Bouman, C. A., “Recent advances in CT image reconstruction”, Current Radiology Reports, 1(1), 39-51, 2013.
[7]. Pack, J. D., Noo, F., & Clackdoyle, R., “Cone-beam reconstruction using the back projection of locally filtered projections”, IEEE Transactions on Medical Imaging, 24(1), 70-85, 2005.
[8]. Scherl, H., Koerner, M., Hofmann, H., Eckert, W., Kowarschik, M., & Hornegger, J., Implementation of the FDK algorithm for
cone-beam CT on the cell broadband engine architecture. In Medical Imaging 2007: Physics of Medical Imaging (Vol. 6510, p. 651058). International Society for Optics and Photonics, March 2007.
[9]. Mark Plachtovics, Janos Goczan, Katalin Nagy, “The effect of calibration and detector temperature on the reconstructed cone beam computed tomography image quality: a study for the workflow of the iCAT Classic equipment”, Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, Vol 119, Issue 4, p473-480, 2015.
[10]. Yuuki Houno, Toshimitsu Hishikawa, Ken-ichi Gotoh, Munetaka Naitoh, Akio Mitani, Toshihide Noguchi, Eiichiro Ariji, Yoshie Kodera, “Optimizing the reconstruction filter in cone-beam CT to improve periodontal ligament space visualization: An in vitro study”, Imaging Science Density, 47(3):199-207, September 2017.
[11]. Julia F. Barrnet, Nicholas Keat, “Artifact in CT: Recognition and Avoidance”, Radio graphic, 24, p1679-1691, 2004.
[12]. Linda G. Shapiro, George Stockman, “Computer Vision”, University of Washington, p154-155,168, 2000.
[13]. Avinash C. Kak, Malcoim Slaney, “Principle of Computerized Tomographic Imaging”, SIAM, p71-74,104-107, 1988.