Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning.

Journal: Medical physics
Published Date: Dec 1, 2019

Abstract

PURPOSE: Metal artifact is a quite common problem in diagnostic dental computed tomography (CT) images. Due to the high attenuation of heavy materials such as metal, severe global artifacts can occur in reconstructions. Typical metal artifact reduction (MAR) techniques segment out the metal regions and estimate the corrupted projection data by various interpolation methods. However, interpolations are not accurate and introduce new artifacts or even deform the teeth in the reconstructed image. This work presents a new strategy to take advantage of the power of deep learning for metal artifact reduction.

Authors

  • Kaichao Liang
    Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Hongkai Yang
    Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.
  • Yirong Yang
    Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.
  • Zhiqiang Chen
    Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.
  • Yuxiang Xing

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