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Shape estimation in computer tomography from minimal data

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Abstract

In computerized tomography, line integrals of the absorptivity are used to reconstruct the object. In some applications only the locations, sizes, and shapes of internal opacities or near-opacities are needed. For these applications it is unnecessary to reconstruct an image by convolution backprojection [O(N3)] or by direct Fourier methods [O(N2 log N)]. We propose an algorithm suitable for this problem that requires only O(N) operations (N is of the order of the number of views). We analyze and demonstrate the high performance of the algorithm but show that the performance of the algorithm depends strongly on an appropriate choice of system parameters. These parameters, the number of views Nθ, and the detector spacing Δs are shown to be linked, and a choice of Δs strongly constrains the choice of Nθ. We show how an optimum Nθ can be determined for a fixed Δs, including practical values of Δs. A series of experiments that reinforce the theory is simulated on a computer.

© 1988 Optical Society of America

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