The reconstruction algorithms to be used for conventional sources are substantially different from the codes described in the previous section, due to the different acquisition geometry related to the conical shape of the beam (Burch and Lawrence, 1992 Feldkamp et al., 1984). [Pg.232]

To achieve that, an object reconstruction algorithm detects connected components (blobs) in the binary images of each material class. In a first step, blobs that do not meet pre-defined size restrictions are considered as classification errors and filtered out (e.g. the black line due to a faulty camera pixel in object D). The binary image of each material class (material and overlay) is then morphologically dilated [Pg.168]

Grangeat P. Description of a 3-D reconstruction algorithm for diverging X-ray beam., Radiol. Soc. North. America Conf Proc., Nov.1985. [Pg.220]

U van Stevendaal, J-P Schlomka, A Harding and M Grass (2003) A reconstruction algorithm for coherent scatter CT based on filtered back-projection. Med. Phys. 30, 2465-2474. [Pg.235]

Yorkey, T. J., Comparing reconstruction algorithms for electrical impedance tomography," PhD Thesis, University of Wisconsin (1986). [Pg.222]

Daube-Witherspoon, M.E., Muehllehner, G., 1986, An iterative image space reconstruction algorithm suitable for volume etc., IEEE Trans. Med. Imaging, 5, 61 [Pg.420]

Nielsen, S.A. Borum, K.K. and Gundtoft, H.E (1995). Verifying an ultrasonic reconstruction algorithm for non-destructive tomography. Proc. of 1st World Congress on Ultrasonics, Berlin, Vol. 1, 446-450. [Pg.207]

Finally, it is shown in terms of the presented example that the proposed adaptive reconstruction algorithm is valuable for image reconstruction from projections without any prior information even in the case of noisy data. The number of required projections can be determined by investigating the convergence properties of the reconstruction algorithm. [Pg.125]

In this section, two illustrative numerical results, obtained by means of the described reconstruction algorithm, are presented. Input data are calculated in the frequency range of 26 to 38 GHz using matrix formulas [8], describing the reflection of a normally incident plane wave from the multilayered half-space. [Pg.130]

E. E. Van Houten, M. I. Miga, J. B. Weaver, F. E. Kennedy and K. D. Paulsen, Three-dimensional subzone-based reconstruction algorithm for MR elastography, Magn. Reson. [Pg.242]

Tarantola, G., Zito, F. and Gerundini, P. PET instrumentation and reconstruction algorithms in whole-body applications. /. Nucl. Med. 44 756-769, 2003. [Pg.959]

The comparison of curve 1 and 2 in Fig. 3 yields, that the convergence with respect to the number of projections k is not strongly influenced by noise because of the properties of the reconstruction algorithm. Nevertheless, the noise increases the asymptotic value of o(n)/0 [Pg.125]

See also in sourсe #XX -- [ Pg.218 ]

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