ArticleMedical & biological engineering & computing2023
Inpainting truncated areas of CT images based on generative adversarial networks with gated convolution for radiotherapy.
Article in Medical & biological engineering & computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Novel extended field-of-view reconstruction algorithms in computed tomography simulation for imaging and volumetric modulated arc therapy dosimetry.Quantitative imaging in medicine and surgery · 2026Article
- Clinical applications of generative artificial intelligence in radiology: image translation, synthesis, and text generation.BJR artificial intelligence · 2024Review
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Authors and funding
10 authors.
Funding
Abstract
This study aimed to inpaint the truncated areas of CT images by using generative adversarial networks with gated convolution (GatedConv) and apply these images to dose calculations in radiotherapy. CT images were collected from 100 patients with esophageal cancer under thermoplastic membrane placement, and 85 cases were used for training based on randomly generated circle masks. In the prediction stage, 15 cases of data were used to evaluate the accuracy of the inpainted CT in anatomy and dosimetry based on the mask with a truncated volume covering 40% of the arm volume, and they were compared with the inpainted CT synthesized by U-Net, pix2pix, and PConv with partial convolution. The results showed that GatedConv could directly and effectively inpaint incomplete CT images in the image domain. For the results of U-Net, pix2pix, PConv, and GatedConv, the mean absolute errors for the truncated tissue were 195.54, 196.20, 190.40, and 158.45 HU, respectively. The mean dose of the planning target volume, heart, and lung in the truncated CT was statistically different (p < 0.05) from those of the ground truth CT ([Formula: see text]). The differences in dose distribution between the inpainted CT obtained by the four models and [Formula: see text] were minimal. The inpainting effect of clinical truncated CT images based on GatedConv showed better stability compared with the other models. GatedConv can effectively inpaint the truncated areas with high image quality, and it is closer to [Formula: see text] in terms of image visualization and dosimetry than other inpainting models.
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