用常规影像引导，1分钟内实现无监督高分辨率定量MRI重建。
Self-Supervised Weighted Image Guided Quantitative MRI Super-Resolution
- 通过物理模型约束网络，利用加权图像引导低分辨率qMRI恢复高分辨率。
- 合成数据中高频误差降低45.4%，真实扫描中1分钟数据重建效果优于5分钟采集。
- 无需高分辨训练标签，可跨序列迁移，适合临床快速定量MRI应用。
目的：提出并评估自监督加权图像引导的定量MRI超分辨率（SWIG qMRI SR）框架，从快速低分辨率（LR）采集中恢复高分辨率（HR）qMRI，无需高分辨率训练目标。方法：采用卷积神经网络将获取的加权图像（wMRI）与由预测参数图生成的合成图像匹配，同时将这些参数图锚定于获取的低分辨率qMRI。训练与消融实验使用合成数据（n=27）；在三位志愿者中测试跨序列泛化能力，其在1分钟和5分钟静音3D MuPa-ZTE扫描下，使用临床T1w/T2w图像作为引导。结果：在合成数据中，双引导超分辨率使T1高频误差范数比基线降低45.4%；移除低分辨率qMRI锚点后，灰质T1误差从63ms上升至110ms。在体实验中，1分钟超分辨率重建的T1w图像（SSIM 0.93，PSNR 27.3dB）优于5分钟采集的合成结果（SSIM 0.83），且未被用作引导的T2-FLAIR图像合成质量也提升（SSIM 0.69→0.75）。讨论：该方法在无高分辨率监督的情况下恢复了高分辨率细节，并实现了不同定量MRI序列间的迁移。由于引导图像已常规获取，仅需增加1分钟的qMRI扫描即可实现临床实用的定量MRI集成。
原文摘要 · Abstract (English)
Object: To present and evaluate Self-supervised Weighted Image Guided quantitative MRI Super-Resolution (SWIG qMRI SR), a physics-informed framework recovering high-resolution (HR) qMRI from a rapid low-resolution (LR) acquisition guided by routine weighted images (wMRI), without HR training targets. Materials and Methods: A CNN matches acquired wMRI to images synthesized from predicted maps through forward signal models, while anchoring those maps to the acquired LR qMRI. Training and ablation used synthetic data (n = 27); cross-sequence generalizability was tested in three volunteers scanned with silent 3D MuPa-ZTE at 1 and 5min, with clinical T1w/T2w guides. Results: In synthetic data, dual-guide SR reduced the T1 high-frequency error norm 45.4% below baseline; removing the LR qMRI anchor raised gray-matter T1 error from 63 to 110ms. In vivo, super-resolved 1-min maps synthesized T1w images (SSIM 0.93, PSNR 27.3dB) surpassing synthesis from 5-min maps (SSIM 0.83), and improved synthesis of T2-FLAIR, never used as a guide (SSIM 0.69 to 0.75). Discussion: HR detail was recovered without HR supervision, and the network transferred across a different qMRI sequence. Because the guides are already acquired, adding a 1-min qMRI scan to a standard exam offers a practical route to routine clinical qMRI integration.
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