Reduction of metal artifacts in x-ray CT images using a convolutional neural network

Yanbo Zhang; Ying Chu; Hengyong Yu

doi:10.1117/12.2275592

25 September 2017 Reduction of metal artifacts in x-ray CT images using a convolutional neural network

Yanbo Zhang, Ying Chu, Hengyong Yu

Author Affiliations +

Proceedings Volume 10391, Developments in X-Ray Tomography XI; 103910V (2017) https://doi.org/10.1117/12.2275592
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

Patients usually contain various metallic implants (e.g. dental fillings, prostheses), causing severe artifacts in the x-ray CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past four decades, MAR is still one of the major problems in clinical x-ray CT. In this work, we develop a convolutional neural network (CNN) based MAR framework, which combines the information from the original and corrected images to suppress artifacts. Before the MAR, we generate a group of data and train a CNN. First, we numerically simulate various metal artifacts cases and build a dataset, which includes metal-free images (used as references), metal-inserted images and various MAR methods corrected images. Then, ten thousands patches are extracted from the databased to train the metal artifact reduction CNN. In the MAR stage, the original image and two corrected images are stacked as a three-channel input image for CNN, and a CNN image is generated with less artifacts. The water equivalent regions in the CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the metal affected projections, followed by the FBP reconstruction. Experimental results demonstrate the superior metal artifact reduction capability of the proposed method to its competitors.

Conference Presentation

Citation Download Citation

Yanbo Zhang, Ying Chu, and Hengyong Yu "Reduction of metal artifacts in x-ray CT images using a convolutional neural network", Proc. SPIE 10391, Developments in X-Ray Tomography XI, 103910V (25 September 2017); https://doi.org/10.1117/12.2275592

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