Medical Imaging Systems Technology: Modalities by Leondes C.T. (ed.)

By Leondes C.T. (ed.)

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H. Lin, P. Cheng and D. M. Shinozaki, A general cone-beam reconstruction algorithm, IEEE Trans. Med. Imaging 12 (1993) 486–496. 44. D. L. Parker, Optimal short scan convolution reconstruction for fan-beam CT, Med. Phys. 9 (1982) 245–257. 45. M. D. Silver, A method for including redundant data in computed tomography, Med. Phys. 27 (2000) 773–774. 46. H. Kudo, T. Rodet, F. Noo and M. Defrise, Exact and approximate algorithm for helical cone-beam CT, Phys. Med. Biol. 49 (2004) 2913–2931. 47. P. Grangeat, Mathematical framework of cone-beam 3D reconstruction via the first derivative of the radon transform, in Mathematical Methods in Tomography, Lecture Notes in Mathematics (G.

13. Schematic illustration of the numerical phantom used in the aliasing studies described in the text. It comprises cylindrical disks of alternating high and low density. 20 CNaR (dB) 15 10 5 0 -5 -10 0 2 4 6 Pitch 8 Fig. 14. 175 mm−1 . Higher values of CNa R are better. This analysis suggests that pitches near 3, 4, and 5 are optimal and half-integer pitches are suboptimal for four-slice with narrow-kernel interpolation. 26 Patrick J. La Rivi` ere The analysis described implicitly blends sampling and interpolation effects.

Working under the multiple parallel fanbeam approximation, we computed a global contrast-to-aliased noise (CNa R) figure of merit for each pitch, which we plot in Fig. 14. Higher values of CNa R are better. As expected, the curve is far from monotonic. Local maxima are evident around pitches 3, 4, 5, and 6. Some complicated variation is evident between pitches 1 and 2, with pitch 2 being a clear minimum in the curve. Other minima occur at half-integer pitches. Fig. 13. Schematic illustration of the numerical phantom used in the aliasing studies described in the text.

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