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15 February 2021 Convolutional neural network based metal artifact reduction method in dental CT image
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Conference Poster
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.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Junhyun Ahn, Yunsu Choi, and Jongduk Baek "Convolutional neural network based metal artifact reduction method in dental CT image", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115952R (15 February 2021); https://doi.org/10.1117/12.2580125
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