Next Article in Journal
Cytoreductive Surgical Treatment of Pleural Mesothelioma in a Porcine Model Using Magnetic-Resonance-Guided Focused Ultrasound Surgery (MRgFUS) and Radiofrequency Ablation (RFA)
Previous Article in Journal
Quantification of Left Atrial Size and Function in Cardiac MR in Correlation to Non-Gated MR and Cardiovascular Risk Factors in Subjects without Cardiovascular Disease: A Population-Based Cohort Study
 
 
Article

Statistical Interior Tomography via L1 Norm Dictionary Learning without Assuming an Object Support

1
Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710048, China
2
The Institute of Image Processing and Pattern Recognition, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Emilio Quaia
Tomography 2022, 8(5), 2218-2231; https://doi.org/10.3390/tomography8050186
Received: 23 June 2022 / Revised: 20 August 2022 / Accepted: 22 August 2022 / Published: 2 September 2022
Interior tomography of X-ray computed tomography (CT) has many advantages, such as a lower radiation dose and lower detector hardware cost compared to traditional CT. However, this imaging technique only uses the projection data passing through the region of interest (ROI) for imaging; accordingly, the projection data are truncated at both ends of the detector, so the traditional analytical reconstruction algorithm cannot satisfy the demand of clinical diagnosis. To solve the above limitations, in this paper we propose a high-quality statistical iterative reconstruction algorithm that uses the zeroth-order image moment as novel prior knowledge; the zeroth-order image moment can be estimated in the projection domain using the Helgason–Ludwig consistency condition. Then, the L1norm of sparse representation, in terms of dictionary learning, and the zeroth-order image moment constraints are incorporated into the statistical iterative reconstruction framework to construct an objective function. Finally, the objective function is minimized using an alternating minimization iterative algorithm. The chest CT image simulated and CT real data experimental results demonstrate that the proposed approach can remove shift artifacts effectively and has superior performance in removing noise and persevering fine structures than the total variation (TV)-based approach. View Full-Text
Keywords: dictionary learning; direct current component; interior tomography; statistical iterative reconstruction dictionary learning; direct current component; interior tomography; statistical iterative reconstruction
Show Figures

Figure 1

MDPI and ACS Style

Wu, J.; Wang, X.; Mou, X. Statistical Interior Tomography via L1 Norm Dictionary Learning without Assuming an Object Support. Tomography 2022, 8, 2218-2231. https://doi.org/10.3390/tomography8050186

AMA Style

Wu J, Wang X, Mou X. Statistical Interior Tomography via L1 Norm Dictionary Learning without Assuming an Object Support. Tomography. 2022; 8(5):2218-2231. https://doi.org/10.3390/tomography8050186

Chicago/Turabian Style

Wu, Junfeng, Xiaofeng Wang, and Xuanqin Mou. 2022. "Statistical Interior Tomography via L1 Norm Dictionary Learning without Assuming an Object Support" Tomography 8, no. 5: 2218-2231. https://doi.org/10.3390/tomography8050186

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop