Limited-Angle CT Reconstruction with Generative Adversarial Network Sinogram Inpainting and Unsupervised Artifact Removal

نویسندگان

چکیده

High-quality limited-angle computed tomography (CT) reconstruction is in high demand the medical field. Being unlimited by pairing of sinogram and reconstructed image, unsupervised methods have attracted wide attention from researchers. The limit existing methods, however, to use [0°, 120°] projection data, quality still has room for improvement. In this paper, we propose a CT generative adversarial network based on inpainting artifact removal further reduce angle range improve image quality. We collected large number lung head images Radon transformed them into missing sinograms. Sinogram developed complete sinograms, which filtered back algorithm can output with most artifacts removed; then, these are mapped artifact-free using network. Finally, generated results sized 512×512 that comparable full-scan only 90°] limited data. Compared current proposed method reconstruct higher

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2022

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app12126268