基于Swin-ResViT网络的低质量动态cine-MR至高质量定位MR图像实时生成研究OA

Objective To obtain high-quality pre-treatment localization MR(sMR)images from dynamic cine-MR using the Swin-ResViT network for target tracking in MRgRT.Methods We 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.Results Regarding image quality,Swin-ResViT reduced NRMSE and LPIPS by 90%and 82%compared to cine-MR(P<0.001),and improved PSNR,SSIM,and CNR by 157%,79%,and 181%(P<0.001),respectively.Regarding structural accuracy,the mean localization error of motion markers at the right hepatophrenic junction in the generated dynamic sMR sequences was 0.7695±0.7294 mm(P<0.05).Regarding model inference speed,for a single 224×224-pixel frame,the average processing time on an NVIDIA GeForce RTX 2080 Ti GPU was 15.5 ms for Swin-ResViT as compared with 41.4 ms for the ResViT network,demonstrating a 62%reduction.Conclusion The 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.