Paper
18 March 2015 CBCT reconstruction via a penalty combining total variation and its higher-degree term
Nanbo Sun, Tao Sun, Jing Wang, Shan Tan
Author Affiliations +
Abstract
Penalized weighted least-squares (PWLS) iterative algorithm with a total variation penalty (PWLS-TV) has shown potential to improve cone-beam CT (CBCT) image quality, particularly in suppressing noise and preserving edges. However, it sometimes suffers from the well-known staircase effect, which produces piece-wise constant areas in images. In order to remove the staircase effect, there is an increasing interest in replacing TV by higher-order derivative operations such as Hessian. Unfortunately, Hessian tends to blur the edges in the reconstruction results. In this study, we proposed a new penalty, namely the TV-H penalty, which combines the TV penalty and the Hessian penalty for CBCT reconstruction. The TV-H penalty retains some of the most favorable properties of the TV penalty like suppressing noise and preserving edges and has a better ability in preserving the structures of gradual intensity transition in images. The penalized weighted least-squares (PWLS) criterion with the majorization-minimization (MM) approach was used to minimize the objective function. Two simulated digital phantoms were used to compare the performance of TV, Hessian penalty and TV-H penalties. Our experiments indicated that the TV-H penalty outperformed the TV penalty and the Hessian penalty.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nanbo Sun, Tao Sun, Jing Wang, and Shan Tan "CBCT reconstruction via a penalty combining total variation and its higher-degree term", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94123T (18 March 2015); https://doi.org/10.1117/12.2082445
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Reconstruction algorithms

3D image reconstruction

Algorithm development

Sensors

3D image processing

Cancer

Image processing

Back to Top