Metal artifacts reduction in computed tomography by Fourier coefficient correction using convolutional neural network

Qi Mai; Justin W. L. Wan

doi:10.1117/12.2549380

10 March 2020 Metal artifacts reduction in computed tomography by Fourier coefficient correction using convolutional neural network

Qi Mai, Justin W. L. Wan

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113132I (2020) https://doi.org/10.1117/12.2549380
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Qi Mai and Justin W. L. Wan "Metal artifacts reduction in computed tomography by Fourier coefficient correction using convolutional neural network", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132I (10 March 2020); https://doi.org/10.1117/12.2549380

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KEYWORDS

Metals

Computed tomography

Fourier transforms

Convolutional neural networks

Data modeling

Visualization

Image quality

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