Limited-angle tomography using artificial neural network

Sze-Fong Yau; Shun-Him Wong

doi:10.1117/12.234254

4 March 1996 Limited-angle tomography using artificial neural network

Sze-Fong Yau, Shun-Him Wong

Proceedings Volume 2664, Applications of Artificial Neural Networks in Image Processing; (1996) https://doi.org/10.1117/12.234254
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

Abstract

This paper considers the problem of limited angle tomography in which a complete sinogram is not available. This situation arises in many practical applications where tomographic projection over 180 degrees is either physically unrealizable or infeasible. When a complete sinogram is not available, it is well known that the reconstructed images using common reconstruction algorithms, such as convolution back projection (CBP), will have severe streak artifacts. In this paper, we present a linear artificial neural network to extrapolate the missing part of the sinogram. Once the complete sinogram is obtained via extrapolation, standard reconstruction techniques such as CBP can be used to generate artifact free reconstructions. The parameters of the neural network are designed using the sampling theory of signals with non-compact spectral support, the knowledge that complete sinograms have bowtie-shaped spectral support, and regularization. It is found that once designed, these parameters are data independent, especially for images of similar nature. For sinogram with 2N angular views, each having M raysum per view, if 2L views are available, the computational requirement of the neural network is 4MNL only. Hence, it is much more efficient than other iterative algorithms such as the method of projection onto convex sets, the Papoulis-Gerchberg's algorithm and the Clark-Palmer-Lawrence interpolation method.

Citation Download Citation

Sze-Fong Yau and Shun-Him Wong "Limited-angle tomography using artificial neural network", Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); https://doi.org/10.1117/12.234254

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