Multidomain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting

Artem Pimkin, Alexander Samoylenko, Natalia Antipina, Anna Ovechkina, Andrey Golanov, Alexandra Dalechina, Mikhail Belyaev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Recent Metal Artifacts Reduction (MAR) methods for Computed Tomography are often based on image-to-image convolutional neural networks for adjustment of corrupted sinograms or images themselves. In this paper, we are exploring the capabilities of a multidomain method, which consists of both sinogram correction (projection domain step) and restored image correction (image-domain step). We formulate the first step problem directly as sinogram inpainting, which allows us to use methods of this specific field, such as partial convolutions. Moreover, we propose a synthetic data generation pipeline to avoid problems with overfitting to metal shapes set and an artifacts formation technique. The proposed method achieves state-of-the-art (-75% MSE) improvement in comparison with a classic benchmark - Li-MAR.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
DOIs
Publication statusPublished - Jul 2020
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

Keywords

  • Computed Tomography (CT) images
  • Convolutional Networks
  • Metal Artifacts Reduction
  • Partial Convolutions
  • Sinogram Inpainting

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