Gaussian diffusion sinogram inpainting for X-ray CT metal artifact reduction

• Published in: Biomedical engineering online Vol. 16; no. 1; p. 1
• Main Authors: Peng, Chengtao, Qiu, Bensheng, Li, Ming, Guan, Yihui, Zhang, Cheng, Wu, Zhongyi, Zheng, Jian
• Format: Journal Article
• Language: English
• Published: London BioMed Central 05.01.2017; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biotechnology; CAT scans; Dental Prosthesis; Diffusion; Engineering; Gaussian processes; Hip Prosthesis; Humans; Image processing; Image Processing, Computer-Assisted - methods; Metals; Methods; Normal Distribution; Tomography, X-Ray Computed; Sinogram inpainting; Prior image; X-ray CT; Gaussian diffusion; Metal artifact reduction
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-016-0292-9

Background Metal objects implanted in the bodies of patients usually generate severe streaking artifacts in reconstructed images of X-ray computed tomography, which degrade the image quality and affect the diagnosis of disease. Therefore, it is essential to reduce these artifacts to meet the clinical demands. Methods In this work, we propose a Gaussian diffusion sinogram inpainting metal artifact reduction algorithm based on prior images to reduce these artifacts for fan-beam computed tomography reconstruction. In this algorithm, prior information that originated from a tissue-classified prior image is used for the inpainting of metal-corrupted projections, and it is incorporated into a Gaussian diffusion function. The prior knowledge is particularly designed to locate the diffusion position and improve the sparsity of the subtraction sinogram, which is obtained by subtracting the prior sinogram of the metal regions from the original sinogram. The sinogram inpainting algorithm is implemented through an approach of diffusing prior energy and is then solved by gradient descent. The performance of the proposed metal artifact reduction algorithm is compared with two conventional metal artifact reduction algorithms, namely the interpolation metal artifact reduction algorithm and normalized metal artifact reduction algorithm. The experimental datasets used included both simulated and clinical datasets. Results By evaluating the results subjectively, the proposed metal artifact reduction algorithm causes fewer secondary artifacts than the two conventional metal artifact reduction algorithms, which lead to severe secondary artifacts resulting from impertinent interpolation and normalization. Additionally, the objective evaluation shows the proposed approach has the smallest normalized mean absolute deviation and the highest signal-to-noise ratio, indicating that the proposed method has produced the image with the best quality. Conclusions No matter for the simulated datasets or the clinical datasets, the proposed algorithm has reduced the metal artifacts apparently.