NDLI: An artificial neural network for tomographic reconstruction of time varying object

Content Provider	IEEE Xplore Digital Library
Author	Ying Ha Chiu Sze Fong Yau
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Hong Kong Univ. of Sci. & Technol., Kowloon, Hong Kong (Ying Ha Chiu; Sze Fong Yau)
Abstract	Presents a study investigating the potential of neural networks for the reconstruction of X-ray computer tomographic images of time-varying object. To obtain a good image of a time-varying object without motion artifact one requires a large number of consistent projections equally spaced in angle. A set of projections are consistent if all the projections relate to the same X-ray absorptivity distribution. This requires that all projections are measured during the same phase of the time-varying object. The authors introduce a new image reconstruction method based on a priori knowledge of the projections to achieve this objective. This method is implemented on a novel neural network and a new training algorithm is proposed. Computer simulation shows that this training algorithm is valid and minimizes the reconstruction mean square error.<>
Starting Page	409
Ending Page	412
File Size	318914
Page Count	4
File Format	PDF
ISBN	078031865X
DOI	10.1109/SIPNN.1994.344789
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-04-13
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Phase measurement Computer simulation Computed tomography Neural networks Artificial neural networks Image sampling Sampling methods Computer networks Image reconstruction X-ray imaging
Content Type	Text
Resource Type	Article

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