Metal artifact reduction in CT using unsupervised sinogram manifold learning Conference Paper

  


Authors: Peng, J.; Chang, C. W.; Xie, H.; Fan, M.; Wang, T.; Roper, J.; Qiu, R. L. J.; Tang, X.; Yang, X.
Title: Metal artifact reduction in CT using unsupervised sinogram manifold learning
Conference Title: SPIE Medical Imaging 2024: Physics of Medical Imaging
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 SPIE.
Keywords: computerized tomography; medical imaging; computed tomography images; diagnosis; learning systems; condition; metal artifacts; deep learning; image guidances; manifold learning; sinograms; tomography imaging; metal artifact reduction; beam hardening effects; projection data
Journal Title Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume: 12925
Conference Dates: 2024 Feb 19-22
Conference Location: San Deigo, CA
ISBN: 1605-7422
Publisher: SPIE  
Date Published: 2024-01-01
Start Page: 129252G
Language: English
DOI: 10.1117/12.3006947
PROVIDER: scopus
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193464442&doi=10.1117%2f12.3006947&partnerID=40&md5=79bd5e87ff6a3aafe82ce0de929ed043
Notes: Conference paper -- Source: Scopus
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MSK Authors
  1. Tonghe Wang
    51 Wang
  2. Huiqiao Xie
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