Limited angle reconstruction with two dictionaries

Author

Meng Cao ; Yuxiang Xing

Author_Institution

Dept. of Eng. Phys., Tsinghua Univ., Beijing, China

fYear

2013

fDate

Oct. 27 2013-Nov. 2 2013

Firstpage

1

Lastpage

4

Abstract

In this work, a two-dictionary learning (TDL) based algorithm is proposed to solve the problem of image reconstruction in X-ray computed tomography (CT) with limited angle projections. One dictionary trained from a high quality image is used to improve the reconstruction image quality, and the other dictionary trained from artifacts is to reduce the limited angle artifact. Experiments with simulated projections and real data were performed to evaluate the proposed algorithm. The results reconstructed using the proposed method shows improved image quality compared with the reconstructions using an ART-TV method.

Keywords

algebra; computerised tomography; image reconstruction; image representation; iterative methods; learning (artificial intelligence); medical image processing; minimisation; ART-TV method; X-ray computed tomography; algebraic reconstruction technique iterative reconstruction; image quality reconstruction improvement; limited angle artifact reduction; limited angle reconstruction; total variation minimization process; two-dictionary learning based algorithm; Computed tomography; Dictionaries; Image reconstruction; Phantoms; Training; Vectors; X-ray imaging;

fLanguage

English

Publisher

ieee

Conference_Titel

Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2013 IEEE

Conference_Location

Seoul

Print_ISBN

978-1-4799-0533-1

Type

conf

DOI

10.1109/NSSMIC.2013.6829229

Filename

6829229