ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2026
[Swin-ResViT network for real-time generation of high-quality localization MR images from low-quality cine-MR].
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesTo obtain high-quality pre-treatment localization MR (sMR) images from dynamic cine-MR using the Swin-ResViT network for target tracking in MRgRT.
methodsWe propose a ResViT model fused with a Swin Transformer module (Swin-ResViT) with an optimized bottleneck layer structure for enhancing feature extraction efficiency. Seventeen liver cancer patients were retrospectively enrolled from Sun Yat-sen University Cancer Center from February to July 2024, and 12 of them were assigned to the training set (using intra-treatment cine-MR and pre-treatment planning MR), with the remaining 5 patients as the test set. Image generation quality and model performance were comprehensively evaluated by quantifying the normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), motion marker point error, and model inference speed between sMR and reference localization MR.
resultsRegarding image quality, Swin-ResViT reduced NRMSE and LPIPS by 90% and 82% compared to cine-MR (
conclusionsThe Swin-ResViT model can synthesize high-quality sMR from cine-MR images. This method combines computational efficiency with significant image enhancement advantages, and thus has important clinical significance for real-time MRgRT.
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