Metal artifact reduction in CT using unsupervised sinogram manifold learning

Junbo Peng; Chih-Wei Chang; Huiqiao Xie; Mingdong Fan; Tonghe Wang; Justin Roper; Richard L. J. Qiu; Xiangyang Tang; Xiaofeng Yang

doi:10.1117/12.3006947

1 April 2024 Metal artifact reduction in CT using unsupervised sinogram manifold learning

Junbo Peng, Chih-Wei Chang, Huiqiao Xie, Mingdong Fan, Tonghe Wang, Justin Roper, Richard L. J. Qiu, Xiangyang Tang, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129252G (2024) https://doi.org/10.1117/12.3006947
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Computed tomography (CT) imaging is widely used for medical diagnosis and image guidance for treatment. Metal artifacts are observed on the reconstructed CT images if metal implants are carried by patients due to the beam hardening effects. In this condition, the acquired projection data cannot be used for analytical reconstruction as they do not meet Tuy's data sufficiency condition. Numerous deep learning-based methods have been developed for metal artifact reduction (MAR), providing superior performance. Nevertheless, all the reported models are data-driven and require large-size referenced images for the manifold approximation. In this work, we propose a physics-driven sinogram manifold learning method, which fully exploits the projection data correlation in CT scanning for MAR, and the proposed method is ready to be extended to other data-incomplete CT reconstruction problems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junbo Peng, Chih-Wei Chang, Huiqiao Xie, Mingdong Fan, Tonghe Wang, Justin Roper, Richard L. J. Qiu, Xiangyang Tang, and Xiaofeng Yang "Metal artifact reduction in CT using unsupervised sinogram manifold learning", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129252G (1 April 2024); https://doi.org/10.1117/12.3006947

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

Includes PDF, HTML & Video, when available