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Metal artifact correction in head computed tomography based on a homographic adaptation convolution neural network

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Abstract

In dental treatment, an increasing number of patients choose metal-implant surgery to treat oral conditions. Computed tomography (CT) images of patients with implanted foreign bodies such as dentures and metal clips are difficult to interpret correctly owing to the presence of high-density metal artifacts. In severe cases, these artifacts may even lead to misdiagnosis, potentially affecting subsequent treatment. Therefore, metal artifact reduction remains an important concern. We propose a novel homographic adaptation convolutional neural network (HACNN) algorithm to solve the problem of metal artifacts in the mouth in head CT. In an experiment, we use a 17-layer CNN as a framework for deep learning, in conjunction with the VGG19 network, to extract the features of CT images, including the original CT, reference CT, and CT images processed by the CNN network. Then, to solve the problem of data misalignment, the improved contextual loss is used as the loss function in the network, and the parameters are adjusted to produce the best results. In contrast to the results of similar experiments, the metal artifacts were removed, details of the CT image were well conserved, and generation of new artifacts was avoided without introducing image blurring.

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Notes

  1. The density of a CT image can be displayed on a gray scale, and the absorption coefficient of the tissue with respect to X-rays can also be used to describe the density. In a clinical setting, the absorption coefficient is converted to the CT value, which is used to describe density. The unit of measurement is HU.

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Acknowledgments

The authors are very grateful to the anonymous reviewers for their constructive comments and unique evaluations, which have considerably improved the way our research is presented. We will also endeavor to make the work available to everyone. We also thank those who have helped and supported us, in particular Jay K. Udupa and Yubing Tong, who provided data and advice. This work was supported by the University Natural Science Research Project of Jiangsu Province (Grant No. 17KJB510038), the Primary Research & Development Plan of Jiangsu Province (Grant No. BE217616), and NUPTSF (Grant No. NY219043).

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  1. Nanjing University of Posts and Telecommunications, College of Telecommunications & Information Engineering, Nanjing, 210003, Jiangsu, China

    Shipeng Xie & Zhenrong Song

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  1. Shipeng Xie
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Correspondence to Shipeng Xie.

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Xie, S., Song, Z. Metal artifact correction in head computed tomography based on a homographic adaptation convolution neural network. Multimed Tools Appl 81, 13045–13064 (2022). https://doi.org/10.1007/s11042-022-12194-7

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