Penalized weighted least-squares approach for multienergy computed tomography image reconstruction via structure tensor total variation regularization. (October 2016)

• Record Type: Journal Article
• Authors: Zeng, Dong; Gao, Yuanyuan; Huang, Jing; Bian, Zhaoying; Zhang, Hua; Lu, Lijun; Ma, Jianhua
• Is Part Of: Computerized medical imaging and graphics. Volume 53(2016)
• Page Start: 19
• Page End: 29
• Publication Date: 2016-10
• Subjects: Multienergy computed tomography -- Penalized weighted least-squares -- Structure tensor total variation -- Regularization; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2016.07.002
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: The STV regularization is derived by penalizing higher-order derivatives of the desired MECT images. A modified effective iterative algorithm was adapted to optimize the objective function of the present algorithm with acceptable results. A digital phantom and preclinical data were used to demonstrate the performance of the present algorithm in terms of several evaluation metrics. This is the first time that STV regularization is used in MECT images reconstruction. Abstract: Multienergy computed tomography (MECT) allows identifying and differentiating different materials through simultaneous capture of multiple sets of energy-selective data belonging to specific energy windows. However, because sufficient photon counts are not available in each energy window compared with that in the whole energy window, the MECT images reconstructed by the analytical approach often suffer from poor signal-to-noise and strong streak artifacts. To address the particular challenge, this work presents a penalized weighted least-squares (PWLS) scheme by incorporating the new concept of structure tensor total variation (STV) regularization, which is henceforth referred to as 'PWLS-STV' for simplicity. Specifically, the STV regularization is derived by penalizing higher-order derivatives of the desired MECT images. Thus it could provide more robust measures of image variation, which can eliminate the patchy artifacts often observed in total variation (TV) regularization. Subsequently, an alternating optimization algorithm was adopted to minimize the objective function. Extensive experiments with a digital XCAT phantom and meat specimen clearly demonstrate that the present PWLS-STV algorithm can achieve more gains than the existing TV-based algorithms and the conventional filtered backpeojection (FBP) algorithm in terms of both quantitative and visual quality evaluations.