30 March 2016 Texture-preserving Bayesian image reconstruction for low-dose CT
Author Affiliations +
Abstract
Markov random field (MRF) model has been widely used in Bayesian image reconstruction to reconstruct piecewise smooth images in the presence of noise, such as in low-dose X-ray computed tomography (LdCT). While it can preserve edge sharpness via edge-preserving potential function, its regional smoothing may sacrifice tissue image textures, which have been recognized as useful imaging biomarkers, and thus it compromises clinical tasks such as differentiating malignant vs. benign lesions, e.g., lung nodule or colon polyp. This study aims to shift the edge preserving regional noise smoothing paradigm to texture-preserving framework for LdCT image reconstruction while retaining the advantage of MRF’s neighborhood system on edge preservation. Specifically, we adapted the MRF model to incorporate the image textures of lung, bone, fat, muscle, etc. from previous full-dose CT scan as a priori knowledge for texture-preserving Bayesian reconstruction of current LdCT images. To show the feasibility of proposed reconstruction framework, experiments using clinical patient scans (with lung nodule or colon polyp) were conducted. The experimental outcomes showed noticeable gain by the a priori knowledge for LdCT image reconstruction with the well-known Haralick texture measures. Thus, it is conjectured that texture-preserving LdCT reconstruction has advantages over edge-preserving regional smoothing paradigm for texture-specific clinical applications.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hao Zhang, Hao Zhang, Hao Han, Hao Han, Yifan Hu, Yifan Hu, Yan Liu, Yan Liu, Jianhua Ma, Jianhua Ma, Lihong Li, Lihong Li, William Moore, William Moore, Zhengrong Liang, Zhengrong Liang, "Texture-preserving Bayesian image reconstruction for low-dose CT", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97834I (30 March 2016); doi: 10.1117/12.2217297; https://doi.org/10.1117/12.2217297
PROCEEDINGS
6 PAGES


SHARE
Back to Top