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An analytical iterative statistical algorithm for image reconstruction from projections

100%
Cierniak R.
International Journal of Applied Mathematics and Computer Science
|
2014
|
tom 24
|
nr 1
7-17
EN
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.
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A new approach to image reconstruction from projections using a recurrent neural network

100%
Cierniak R.
International Journal of Applied Mathematics and Computer Science
|
2008
|
tom 18
|
nr 2
147-157
EN
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.
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