Deep Learning Based Projection Domain Metal Segmentation for Metal Artifact Reduction in Cone Beam Computed Tomography

Deep Learning Based Projection Domain Metal Segmentation for Metal Artifact Reduction in Cone Beam Computed Tomography

Metal artifact correction is a challenging problem in cone beam computed tomography (CBCT) scanning. Metal implants inserted into the anatomy cause severe artifacts in reconstructed images. Widely used inpainting-based metal artifact reduction (MAR) methods require segmentation of metal traces in th...

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Bibliographic Details
Main Authors: Harshit Agrawal, Ari Hietanen, Simo Sarkka
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
CBCT
deep learning
synthetic data
X-ray simulation
metal artifact reduction
metal segmentation
Online Access:https://ieeexplore.ieee.org/document/10250444/

Internet

https://ieeexplore.ieee.org/document/10250444/

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