Statistical image reconstruction for lesion detection

Author

Qi, Jinyi

Author_Institution

Dept. of Biomed. Eng., California Univ., Davis, CA, USA

Volume

1

fYear

2004

fDate

7-10 Nov. 2004

Firstpage

153

Abstract

Detecting cancerous lesions is one major application in emission tomography. In this paper, we study statistical image reconstruction for this important clinical task. Compared to analytical reconstruction methods, statistical approaches can improve the image quality by accurately modeling the photon detection process and data noise. To explore the full potential of statistical reconstruction for lesion detection, we derived simplified theoretical expressions that allow fast evaluation of the detectability of a random lesion. The theoretical results are used to design the regularization parameters for the maximum lesion detectability. Results are validated using Monte Carlo simulations.

Keywords

Monte Carlo methods; cancer; image reconstruction; medical image processing; Monte Carlo simulation; cancerous lesion detection; clinical task; data noise; emission tomography; image quality; photon detection process modeling; regularization parameter; statistical image reconstruction; Cancer detection; Event detection; Image quality; Image reconstruction; Lesions; Maximum likelihood detection; Maximum likelihood estimation; Reconstruction algorithms; Spatial resolution; Tomography;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on

Print_ISBN

0-7803-8622-1

Type

conf

DOI

10.1109/ACSSC.2004.1399110

Filename

1399110