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Proceedings Paper

Texture-preserving Bayesian image reconstruction for low-dose CT
Author(s): Hao Zhang; Hao Han; Yifan Hu; Yan Liu; Jianhua Ma; Lihong Li; William Moore; Zhengrong Liang
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Paper 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.

Paper Details

Date Published: 30 March 2016
PDF: 6 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97834I (30 March 2016);
Show Author Affiliations
Hao Zhang, Stony Brook Univ. (United States)
Hao Han, Stony Brook Univ. (United States)
Yifan Hu, Stony Brook Univ. (United States)
Yan Liu, Stony Brook Univ. (United States)
Jianhua Ma, Southern Medical Univ. (China)
Lihong Li, College of Staten Island (United States)
William Moore, Stony Brook Univ. (United States)
Zhengrong Liang, Stony Brook Univ. (United States)

Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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