A Compton scattering suppression based image reconstruction method for Digital Breast Tomosynthesis

Author

Xu, Shiyu ; Chen, Ying

Author_Institution

Dept. of Electr. & Comput. Eng., Southern Illinois Univ., Carbondale, IL, USA

fYear

2011

fDate

4-6 Dec. 2011

Firstpage

190

Lastpage

193

Abstract

In this paper, we present a novel statistical reconstruction method based on Compton noise suppression for Digital Breast Tomosynthesis. The physical modeling for X-ray´s interaction with tissues was investigated. A Compton noise based statistical model with a conjugate prior was introduced. Results from our proposed method, Backprojection (BP) method, Simultaneous Algebraic Reconstruction Technique (SART) and Maximum Likelihood with Convex Algorithm (MLCA) were compared based on image quality analysis. Compared with other algorithms, our proposed method shows comparable performance. It also produces even better results in the Contrast to Noise Ratio (CNR) evaluation.

Keywords

algebra; backpropagation; cancer; computerised tomography; convex programming; image denoising; image reconstruction; interference suppression; mammography; maximum likelihood estimation; medical image processing; Compton noise based statistical model; Compton noise suppression; Compton scattering suppression; X-ray interaction; backprojection method; breast cancer detection; contrast-to-noise ratio evaluation; digital breast tomosynthesis; image quality analysis; image reconstruction method; mammography; maximum likelihood-with-convex algorithm; simultaneous algebraic reconstruction technique; statistical reconstruction method; Attenuation; Breast; Image quality; Image reconstruction; Noise; Reconstruction algorithms; Scattering; Bayesian estimation; Digital Breast tomosynthesis; Image quality; Image reconstruction; Maximum A Posteriori (MAP);

fLanguage

English

Publisher

ieee

Conference_Titel

Genomic Signal Processing and Statistics (GENSIPS), 2011 IEEE International Workshop on

Conference_Location

San Antonio, TX

ISSN

2150-3001

Print_ISBN

978-1-4673-0491-7

Electronic_ISBN

2150-3001

Type

conf

DOI

10.1109/GENSiPS.2011.6169477

Filename

6169477