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Vannier, Low-contrast resolution in volumetric x-ray CT—Analytical comparison between conventional and spiral CT, Med. , 1997, in press. 42. J. Hsieh, Analysis of the temporal response of computed tomography fluoroscopy, Med. , 1997, in press. 21. G. L. Zheng and G. T. Gullberg, A cone-beam tomography algorithm for orthogonal circle-and-line orbit, Phys. Med. , 37: 563–577, 1992. 43. J. Hsieh, Image artifacts, causes, and correction, In L. W. Goldman and J. B. ), Medical CT and Ultrasound: Current Technology and Applications.

48. C. R. Crawford, K. F. King, C. J. Ritchie, and J. D. Godwin, Respiratory compensation in projection imaging using a magnification and displacement model, IEEE Trans. Med. Imaging, 15: 327–332, 1996. 27. R. B. Marr, C. Chen, and P. C. Lauterbur, On two approaches to 3D reconstruction in NMR zeugmatography. In G. T. Herman and F. ), Mathematical Aspects of Computerized Tomography, Berlin: Springer-Verlag, 1981. 49. G. Wang, D. L. Snyder, J. A. O’Sullivan, and M. W. Vannier, Iterative deblurring for CT metal artifact reduction, IEEE Trans.

In addition to the iterative algorithm described above, a statisticalmodel-based iterative algorithm was developed for X-ray CT (31). In this case, X-ray CT with low photon counts is viewed as an estimation problem, and it is solved in the maximum likelihood (ML) sense (31). ART-type iterative algorithms are also valuable (54). Theoretical and practical issues with itera- tive CT algorithms include regularization and acceleration (55–57). Progress in hardware will broaden horizons of CT applications.

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