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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE