Low-dose computed tomography image reconstruction via structure tensor total variation regularization

Junfeng Wu; Xuanqin Mou; Yongyi Shi; Ti Bai; Yang Chen

doi:10.1117/12.2293266

9 March 2018 Low-dose computed tomography image reconstruction via structure tensor total variation regularization

Junfeng Wu, Xuanqin Mou, Yongyi Shi, Ti Bai, Yang Chen

Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 105733D (2018) https://doi.org/10.1117/12.2293266
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

The X-ray computer tomography (CT) scanner has been extensively used in medical diagnosis. How to reduce radiation dose exposure while maintain high image reconstruction quality has become a major concern in the CT field. In this paper, we propose a statistical iterative reconstruction framework based on structure tensor total variation regularization for low dose CT imaging. An accelerated proximal forward-backward splitting (APFBS) algorithm is developed to optimize the associated cost function. The experiments on two physical phantoms demonstrate that our proposed algorithm outperforms other existing algorithms such as statistical iterative reconstruction with total variation regularizer and filtered back projection (FBP).

Citation Download Citation

Junfeng Wu, Xuanqin Mou, Yongyi Shi, Ti Bai, and Yang Chen "Low-dose computed tomography image reconstruction via structure tensor total variation regularization", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105733D (9 March 2018); https://doi.org/10.1117/12.2293266

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