A dual-stream deep convolutional network for reducing metal streak artifacts in CT images

Machine learning and deep learning are rapidly finding applications in the medical imaging field. In this paper, we address the long-standing problem of metal artifacts in computed tomography (CT) images by training a dual-stream deep convolutional neural network for streak removal. While many metal...

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Veröffentlicht in:	Physics in medicine & biology 2019-11, Vol.64 (23), p.235003-235003
Hauptverfasser:	Gjesteby, Lars, Shan, Hongming, Yang, Qingsong, Xi, Yan, Jin, Yannan, Giantsoudi, Drosoula, Paganetti, Harald, De Man, Bruno, Wang, Ge
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Deep Learning Humans Image Processing, Computer-Assisted - methods Machine Learning metal artifact reduction Metals Neural Networks, Computer Pedicle Screws Prostheses and Implants Proton Therapy Reproducibility of Results Tomography, X-Ray Computed Visible Human Projects x-ray computed tomography
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