Truncation Artifact Reduction in Stationary Inverse-Geometry Digital Tomosynthesis Using Deep Convolutional Generative Adversarial Network
Stationary inverse-geometry digital tomosynthesis (s-IGDT) causes truncation artifacts in reconstructed images due to its geometric characteristics. This study introduces a deep convolutional generative adversarial network (DCGAN)-based out-painting method for mitigating truncation artifacts in s-IG...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/7699 |
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| Summary: | Stationary inverse-geometry digital tomosynthesis (s-IGDT) causes truncation artifacts in reconstructed images due to its geometric characteristics. This study introduces a deep convolutional generative adversarial network (DCGAN)-based out-painting method for mitigating truncation artifacts in s-IGDT images. The proposed network employed an encoder–decoder architecture for the generator, and a dilated convolution block was added between the encoder and decoder. A dual-discriminator was used to distinguish the artificiality of generated images for truncated and non-truncated regions separately. During network training, the generator was able to selectively learn a target task for the truncated regions using binary mask images. The performance of the proposed method was compared to conventional methods in terms of signal-to-noise ratio (SNR), normalized root-mean-square error (NRMSE), peak SNR (PSNR), and structural similarity (SSIM). The results showed that the proposed method led to a substantial reduction in truncation artifacts. On average, the proposed method achieved 62.31, 16.66, and 14.94% improvements in the SNR, PSNR, and SSIM, respectively, compared to the conventional methods. Meanwhile, the NRMSE values were reduced by an average of 37.22%. In conclusion, the proposed out-painting method can offer a promising solution for mitigating truncation artifacts in s-IGDT images and improving the clinical availability of the s-IGDT. |
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| ISSN: | 2076-3417 |