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Proceedings Paper

A feasibility study on the improvement of CT image with the back propagation neural network
Author(s): Shih-Chieh Lin; Tse-Li Wang
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Paper Abstract

It had been a major concern about multi-slice X-ray CT for its high radiation dose delivered to a patient. In order to reduce the radiation dose, one can either limit the dose per projection, or reduce the number of projections, or both. However, it was shown that artifact will appear when limited projections were used. In this study, the feasibility of using back propagation type artificial neural network to improve the image reconstructed using the filtered back projection is studied. Two networks were trained to reconstructed image by input information calculated using the filtered back projection method from 32, and 64 projections respectively. A series tests are also conducted to evaluate the performance of the trained networks. The results show that if information of 32 or 64 projections was used, the reconstructed images are generally improved by the use of the trained network.

Paper Details

Date Published: 31 January 2013
PDF: 7 pages
Proc. SPIE 8759, Eighth International Symposium on Precision Engineering Measurement and Instrumentation, 87590H (31 January 2013); doi: 10.1117/12.2015212
Show Author Affiliations
Shih-Chieh Lin, National Tsing Hua Univ. (Taiwan)
Tse-Li Wang, National Tsing Hua Univ. (Taiwan)

Published in SPIE Proceedings Vol. 8759:
Eighth International Symposium on Precision Engineering Measurement and Instrumentation
Jie Lin, Editor(s)

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