Solution of the missing cone problem by artificial neural network

Author

Yu, Kai-Kou R. ; Yau, Sze-Fong

Author_Institution

Dept. of Electr. & Electron. Eng., Hong Kong Univ. of Sci. & Technol., Hong Kong

Volume

6

fYear

1994

fDate

30 May-2 Jun 1994

Firstpage

467

Abstract

This paper considers the well-known limited angle or missing cone problem. We present an alternative method to restore a complete set of projection data from limited angle of views without prior information of the scanning object, using two-dimensional sampling theory and the result by Rattey and Lindgren (1981) which shows that the spectral support of CT projection data is bowtie-shaped. A general matrices formulation is developed and the problem is posed as an least square optimization problem. This problem is then implemented by a nonstandard neural network. A novel training algorithm is proposed minimizing a modified error criterion. Computer simulation results are presented to demonstrate the validity of the algorithm

Keywords

computerised tomography; image sampling; iterative methods; learning (artificial intelligence); least squares approximations; matrix algebra; neural nets; optimisation; CT projection data; artificial neural network; least square optimization problem; limited angle problem; matrices formulation; missing cone problem; modified error criterion; training algorithm; two-dimensional sampling theory; Application software; Artificial neural networks; Computed tomography; Computer applications; Computer errors; Computer simulation; Image reconstruction; Least squares methods; Neural networks; Sampling methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1994. ISCAS '94., 1994 IEEE International Symposium on

Conference_Location

London

Print_ISBN

0-7803-1915-X

Type

conf

DOI

10.1109/ISCAS.1994.409627

Filename

409627