Unpaired-Paired Learning for Shading Correction in Cone-Beam Computed Tomography

Unpaired-Paired Learning for Shading Correction in Cone-Beam Computed Tomography

Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging applications. However, CBCT suffers from shading artifacts owing to several factors, including photon scattering and data truncation. This paper presents a deep-learning-based method for eliminating the shading a...

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Bibliographic Details
Main Authors: Hyoung Suk Park, Kiwan Jeon, Sang-Hwy Lee, Jin Keun Seo
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Computed tomography
shading correction
unpaired learning
generative adversarial network
Online Access:https://ieeexplore.ieee.org/document/9722839/

Internet

https://ieeexplore.ieee.org/document/9722839/

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