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An analytical iterative statistical algorithm for image reconstruction from projections

Cierniak R.

International Journal of Applied Mathematics and Computer Science

2014

Vol. 24, no. 1

7--17

The main purpose of the paper is to present a statistical model-based iterative approach to the problem of image reconstruction from projections. This originally formulated reconstruction algorithm is based on a maximum likelihood method with an objective adjusted to the probability distribution of measured signals obtained from an x-ray computed tomograph with parallel beam geometry. Various forms of objectives are tested. Experimental results show that an objective that is exactly tailored statistically yields the best results, and that the proposed reconstruction algorithm reconstructs an image with better quality than a conventional algorithm with convolution and back-projection.

A neural network method of image reconstruction from projections using grid-"friendly" projection angles

Cierniak R.

Image Processing & Communications

2009

Vol. 14, no 2-3

51-58

In this paper an original approach to the reconstruction problem using a recurrent neural network is presented. In our method parallel beam projections is used. To decrease the number of desired projections the grid "friendly" angles of the parallel projections are selected according to the discrete Radon transform (DRT). Performed computer simulations show that the presented neural network reconstruction algorithm outperforms the convolution/back-projection method in the reconstructed image quality.

A new approach to image reconstruction from projections using a recurrent neural network

Cierniak R.

International Journal of Applied Mathematics and Computer Science

2008

Vol. 18, no 2

147-157

A new neural network approach to image reconstruction from projections considering the parallel geometry of the scanner is presented. To solve this key problem in computed tomography, a special recurrent neural network is proposed. The reconstruction process is performed during the minimization of the energy function in this network. The performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in the obtained image quality.