Fully convolutional networks based sinogram correction for metal artifact reduction

Linlin Zhu; Yu Han; Ziheng Li; Xiaoqi Xi; Lei Li; Bin Yan; Mingwan Zhu

doi:10.1117/12.2542909

18 December 2019 Fully convolutional networks based sinogram correction for metal artifact reduction

Linlin Zhu, Yu Han, Ziheng Li, Xiaoqi Xi, Lei Li, Bin Yan, Mingwan Zhu

Proceedings Volume 11342, AOPC 2019: AI in Optics and Photonics; 1134208 (2019) https://doi.org/10.1117/12.2542909
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

Abstract

Computed tomography (CT) has been extensively used in nondestructive testing, medical diagnosis, etc. In the field of modern medicine, metal implants are widely used in people's daily life, and the serious artifacts in CT reconstruction images caused by metal implants cannot be ignored. Sinogram contains the most realistic projection information of patients. Processing in the sinogram domain directly can make the effective information maximum extent preserved. In this paper, we propose a novel method based on full convolutional network (FCN) for metal artifact reduction in the sinogram domain. The networks we introduced use the complete sinogram data to learn a mapping function to correct the metal-corrupted sinogram data. The network takes the metal-corrupted sinogram as the input and takes the artifact-free sinogram as the target. Compared with the existing deep learning-based CT artifact reduction methods, our work just uses the sinogram information to correct the metal artifacts. The proposed network can process images of different sizes. Our initial results on a simulated dataset to demonstrate the potential effectiveness of this new approach to suppressing artifacts.

Citation Download Citation

Linlin Zhu, Yu Han, Ziheng Li, Xiaoqi Xi, Lei Li, Bin Yan, and Mingwan Zhu "Fully convolutional networks based sinogram correction for metal artifact reduction", Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 1134208 (18 December 2019); https://doi.org/10.1117/12.2542909

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