Acceleration of iterative tomographic image reconstruction by reference-based back projection

Chang-Chieh Cheng; Ping-Hui Li; Yu-Tai Ching

doi:10.1117/12.2216363

22 March 2016 Acceleration of iterative tomographic image reconstruction by reference-based back projection

Chang-Chieh Cheng, Ping-Hui Li, Yu-Tai Ching

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97834S (2016) https://doi.org/10.1117/12.2216363
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

The purpose of this paper is to design and implement an efficient iterative reconstruction algorithm for computational tomography. We accelerate the reconstruction speed of algebraic reconstruction technique (ART), an iterative reconstruction method, by using the result of filtered backprojection (FBP), a wide used algorithm of analytical reconstruction, to be an initial guess and the reference for the first iteration and each back projection stage respectively. Both two improvements can reduce the error between the forward projection of each iteration and the measurements. We use three methods of quantitative analysis, root-mean-square error (RMSE), peak signal to noise ratio (PSNR), and structural content (SC), to show that our method can reduce the number of iterations by more than half and the quality of the result is better than the original ART.

Citation Download Citation

Chang-Chieh Cheng, Ping-Hui Li, and Yu-Tai Ching "Acceleration of iterative tomographic image reconstruction by reference-based back projection", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97834S (22 March 2016); https://doi.org/10.1117/12.2216363

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available