Fogorvosi szemle, 2008 (101. évfolyam, 1-6. szám)

2008-10-01 / 5. szám

178 FOGORVOSI SZEMLE ■ 101. évf. 5. sz. 2008. 6. Erdélyi M: Árnyékfejtés - A számítógépes tomográfia mint a mod­ern orvostudomány eszköze. Fizikai Szemle 2005/7; 225-230. 7. Feldkamp LA, Davis LC and Kress JW: Practical cone-beam algo­rithm. J Opt Soc Am A 1984; 1(A), 612-619. 8. Hintze H, Wiese M and Wenzel A: Cone beam CT and conventional tomography for the detection of morphological temporomandibular joint changes. Dentomaxillofacial Radiology 2007; 36, 192-197. 9. Hounsfield GN: Computerized transverse axial scanning (tomog­raphy): Part I. Description of system. Br J Radiol 1973; 46, 1016— 1022. 10. Kalender WA: X-ray computed tomography. Phys Med Biol 2006 51 R29-R43. 11. Kobayashi K, Shimoda S, Nakagawa Y, Yamamoto A: Accuracy in measurement of distance using limited cone-beam computerized tomography. Int J Oral Maxillofac Implants 2004; 19:228-231. 12. Krol A, Kieffer J-C, Nees JA, Liming Chen, Toth R, Bixhue Hou, Kincaid RE, Coman IL, Chamberlain CC, Lipson ED, Mourou GA: De­velopment of novel ultrafast-laser-based micro-CT system for small­­animal imaging. Nuclear Science Symposium Conference Record, IEEE 2003; 3, 1993-1996 13. Mozzo P, Procacci C, Tacconi A, Tinazzi Martini P, Bergamo Andreis IA: A new volumetric CT machine for dental imaging based on the cone-beam technique: preliminary results. Eur Radiol 1998; 8, 1558-1564. 14. Siewerdsen JH, Daly MJ, Bakhtiar B, Moseley DJ, Richard S, Keller H, Jaffray DA: A simple, direct method for X-ray scatter esti­mation and correction in digital radiography and cone-beam CT. Med Phys 2006; 33 (1) 187-197. 15. Siewerdsen JH, Moseley DJ, Bakhtiar B, Richard S, Jaffray DA: The influence of antiscatter grids on soft-tissue detectability in cone­­beam computed tomography with flat-panel detectors. Med Phys 2004; 31 (12) 3506-3520. 16. Siewerdsena JH and Jaffray DA: Cone-beam computed tomogra­phy with a flat-panel imager: Magnitude and effects of x-ray scatter. Med Phys 2001 ; 28 (2), 220-231. 17. Siewerdsena JH and Jaffray DA: Optimization of x-ray imaging geometry (with specific application to flat-panel cone-beam computed tomography). Med Phys 2000; 27 (8), 1903-1914. 18. Silva MAG, Wolf U, Heinicke F, Bumann A, Visser H and Hirsch E: Cone-beam computed tomography for routine orthodontic treat­ment planning: A radiation dose evaluation. American Journal of Orthodontics and Dentofacial Orthopedics 2008; 133 (5): 640.e 1-640.e 5. 19. Van de Casteele E, Van Dyck D, Sobers J and Raman E: An en­ergy-based beam hardening model in tomography. Phys Med Biol 2002; 47: 4181-4190. 20. Wang G, Vannier MW and Cheng PC: Iterative X-ray cone-beam tomography for metal artifact reduction and local region reconstruc­tion. Microsc Microanal 1999; 5, 58-65. 21. Wang G, Yub H, De Man B: An outlook on x-ray CT research and development. Med Phys 2008; 35 (4). 22. Zhang Y, Zhang L, Zhu XR, Lee AK, Chambers M, Dong L: Re­ducing metal artifacts in cone-beam CT images by preprocessing projection data. Int J Radiation Oncology Biol Phys 2007; 67 (3) 924- 932. Kovács M, Dr. Fejérdy P, Dr. Dobó N. CS.: Metal artefact on head and neck cone-beam CT images There are only a few factors, where the properties of the CBCT is inferior compared to conventional CT. One of these properties is the low contrast resolution, which has an importance in the discrimination of different soft tissues. Another difference is the image quality degrading effect by metal objects. This latter factor has much higher importance in head and neck region CBCT application. The metal artifact is closely related to other types of artifacts, like beam-hard­ening and x-ray photon scattering artifacts. In some of the cases, metal artifacts can be avoided by the proper adjustment of the scanning parameters, but some­times the problem overgrows the possibilities. The current pre- and post-processing algorithms used for the correction of different artifacts can improve the image quality, but these algorithms are not the ultimate solution to the problem. The introduction of iterative reconstruction algorithms into the CBCT market will effectively reduce the most CT artifacts, however, the spread of this algorithms are set back because of the insufficient computational power of today’s PCs. Another advantage of the use of iterative algorithms is that the patient dose could be significantly reduced. Key words: cone-beam CT, CT artifacts, metal artifacts, reconstruction algorithms, iterative reconstruction

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