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Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Adversarial Sparse-View CBCT Artifact Reduction

Authors : Haofu Liao, Zhimin Huo, William J. Sehnert, Shaohua Kevin Zhou, Jiebo Luo

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

We present an effective post-processing method to reduce the artifacts from sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based on the state-of-the-art, image-to-image generative models with a perceptual loss as regulation. Unlike the traditional CT artifact-reduction approaches, our method is trained in an adversarial fashion that yields more perceptually realistic outputs while preserving the anatomical structures. To address the streak artifacts that are inherently local and appear across various scales, we further propose a novel discriminator architecture based on feature pyramid networks and a differentially modulated focus map to induce the adversarial training. Our experimental results show that the proposed method can greatly correct the cone-beam artifacts from clinical CBCT images reconstructed using 1/3 projections, and outperforms strong baseline methods both quantitatively and qualitatively.

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Footnotes
1
Identical to the 2D UNet used in this work with all the 2D convolutional and deconvolutional layers replaced by their 3D counterparts.
 
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Metadata
Title
Adversarial Sparse-View CBCT Artifact Reduction
Authors
Haofu Liao
Zhimin Huo
William J. Sehnert
Shaohua Kevin Zhou
Jiebo Luo
Copyright Year
2018
Book
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Print ISBN: 978-3-030-00927-4

Electronic ISBN: 978-3-030-00928-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00928-1_18

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