Deep learning methods for CT image-domain metal artifact reduction

Author(s):  
Lars Gjesteby ◽  
Qingsong Yang ◽  
Yan Xi ◽  
Bernhard E. H. Claus ◽  
Yannan Jin ◽  
...  
Author(s):  
Gengsheng L. Zeng

AbstractMetal objects in X-ray computed tomography can cause severe artifacts. The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods. This paper proposes a projection-domain algorithm to reduce the metal artifacts. In this algorithm, the unknowns are the metal-affected projections, while the objective function is set up in the image domain. The data fidelity term is not utilized in the objective function. The objective function of the proposed algorithm consists of two terms: the total variation of the metal-removed image and the energy of the negative-valued pixels in the image. After the metal-affected projections are modified, the final image is reconstructed via the filtered backprojection algorithm. The feasibility of the proposed algorithm has been verified by real experimental data.


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