ArticleJournal of applied clinical medical physics2026
A bias field correction workflow based on generative adversarial network for abdominal cancers treated with 0.35T MR-LINAC.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A bias field correction workflow based on generative adversarial network for abdominal cancers treated with 0.35T MR-LINAC.Journal of applied clinical medical physics · 2026Article
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Abstract
purposeIn this study, a bias field correction workflow was proposed to improve the flexibility and generalizability of the generative adversarial network (GAN) model for abdominal cancer patients treated with a 0.35T magnetic resonance imaging linear accelerator (MR-LINAC) system.
methodsModel training was performed using brain MR images acquired on a 3T diagnostic scanner, while model testing was performed using abdominal MR images obtained using a 0.35T MR-LINAC system. The performance of the proposed workflow was first compared with the GAN model using root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). To assess the impact of the workflow on image segmentation, it was also compared with the N4ITK algorithm. Segmentation was performed using the k-means clustering algorithm with three clusters corresponding to air, fat, and soft tissue. Segmentation accuracy was then evaluated using the Dice similarity coefficient (DSC).
resultsThe RMSE values were 30.59, 12.06, 10.37 for the bias field-corrupted images (I
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