139 research outputs found
Adversarial Sparse-View CBCT Artifact Reduction
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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