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Proceedings Paper

Metal artifacts reduction in computed tomography by Fourier coefficient correction using convolutional neural network
Author(s): Qi Mai; Justin W. L. Wan
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Paper Abstract

Metal artifacts are very common in CT scans since many patients have metal insertion or replacement to enhance functionality or mechanism of their bodies. These streaking artifacts could degrade CT image quality severely, and consequently, they could influence clinical diagnosis. In this paper, we propose to use the Fourier coefficients of a metal artifact-tainted image as the input to a convolutional neural network, and the Fourier coefficients of the corresponding clean image as target. We compare the performances of three convolutional neural network models with three kinds of inputs - sinograms with metal traces, images with streaks, and the Fourier coefficients of artifact-corrupted images. Using Fourier coefficients as inputs gives generally better artifacts reduction results in visualization and quantitative measures in different models.

Paper Details

Date Published: 10 March 2020
PDF: 8 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132I (10 March 2020); doi: 10.1117/12.2549380
Show Author Affiliations
Qi Mai, Univ. of Waterloo (Canada)
Justin W. L. Wan, Univ. of Waterloo (Canada)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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