Often, the artifacts caused by high-density objects degrade the quality of the image with streaks and information loss in CT imaging. In recent years, machine learning has proven itself a powerful tool to resolve some of the challenges faced in reducing metal artifacts. In this work, a novel method of metal artifact reduction (MAR) without metal segmentation by using a CNN network is proposed. The approach focuses on removing the need for the sensitive metal segmentation step to improve robustness and aims to tackle beam hardening directly in the sinogram domain. In the proposed method, we trained the network with sinogram pairs that include metal objects and those that include virtual non-metal (VNM) replacement objects. A VNM object is designed to be less dense than metal but more dense than soft tissue. The novelty of this method lies in the sinogram-to-sinogram training without the need for metal segmentation by replacing the metal object to a virtual non-metal object in the sinogram to reduce beam hardening and successfully compensate for the information loss.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.