Convolutional neural network based metal artifact reduction method in dental ct image

Junhyun Ahn, Yunsu Choi, Jongduk Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In dental CT, the presence of metal objects introduces various artifacts caused by photon starvation and beam hardening. Although several metal artifacts reduction methods have been proposed, they still have limitations in terms of reducing the metal artifacts. In this work, we proposed a method to reduce the metal artifacts with convolutional neural network (CNN). The proposed method is comprised of two steps. In STEP 1, we acquired a more accurate prior image, which is used in normalized metal artifact reduction (NMAR) technique through the CNN. The metal artifacts in output image from STEP 1 are reduced by CNN training, which provides more accurate prior images. In STEP 2, the NMAR is conducted with the acquired prior image from CNN result. To validate the proposed method, we used dental CT images containing metals and without metal to evaluate that the proposed method could significantly reduce the metal artifacts compared to the NMAR method.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationPhysics of Medical Imaging
EditorsHilde Bosmans, Wei Zhao, Lifeng Yu
PublisherSPIE
ISBN (Electronic)9781510640191
DOIs
Publication statusPublished - 2021
EventMedical Imaging 2021: Physics of Medical Imaging - Virtual, Online, United States
Duration: 2021 Feb 152021 Feb 19

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11595
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Physics of Medical Imaging
Country/TerritoryUnited States
CityVirtual, Online
Period21/2/1521/2/19

Bibliographical note

Funding Information:
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (NRF-2019R1A2C2084936, 2020R1A4A1016619).

Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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