标题： 基于知识蒸馏的多尺度MRI超分辨率扩散模型：让1.5T MRI重现辉煌 显示英文标题

标题： Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great Again

摘要： 磁共振成像（MRI）提供了关于微结构细节的关键见解，然而，标准1.5T成像系统的空间分辨率通常受到限制。 相比之下，7T MRI提供了显著增强的空间分辨率，能够更精细地可视化解剖结构。 尽管如此，7T MRI的高成本和有限的可用性阻碍了其在临床环境中的广泛应用。 为解决这一挑战，提出了一种新的超分辨率（SR）模型，以从标准1.5T MRI扫描中生成类似7T的MRI图像。 我们的方法利用基于扩散的架构，结合来自7T成像的梯度非线性校正和偏置场校正数据作为指导。 此外，为了提高可部署性，引入了一种渐进式蒸馏策略。 具体而言，学生模型通过步骤细化7T SR任务，利用教师模型推理阶段的特征图作为指导，旨在使学生模型在较小且可部署的模型大小下逐步实现7T SR性能。 实验结果表明，我们的基线教师模型实现了最先进的SR性能。 虽然学生模型轻量级，但性能损失很小。 此外，学生模型能够接受不同分辨率的MRI输入而无需重新训练，显著提高了部署的灵活性。 我们的方法的临床相关性通过马萨诸塞州总医院的临床数据得到了验证。 我们的代码可在https://github.com/ZWang78/SR获取。 显示英文摘要

摘要： Magnetic Resonance Imaging (MRI) offers critical insights into microstructural details, however, the spatial resolution of standard 1.5T imaging systems is often limited. In contrast, 7T MRI provides significantly enhanced spatial resolution, enabling finer visualization of anatomical structures. Though this, the high cost and limited availability of 7T MRI hinder its widespread use in clinical settings. To address this challenge, a novel Super-Resolution (SR) model is proposed to generate 7T-like MRI from standard 1.5T MRI scans. Our approach leverages a diffusion-based architecture, incorporating gradient nonlinearity correction and bias field correction data from 7T imaging as guidance. Moreover, to improve deployability, a progressive distillation strategy is introduced. Specifically, the student model refines the 7T SR task with steps, leveraging feature maps from the inference phase of the teacher model as guidance, aiming to allow the student model to achieve progressively 7T SR performance with a smaller, deployable model size. Experimental results demonstrate that our baseline teacher model achieves state-of-the-art SR performance. The student model, while lightweight, sacrifices minimal performance. Furthermore, the student model is capable of accepting MRI inputs at varying resolutions without the need for retraining, significantly further enhancing deployment flexibility. The clinical relevance of our proposed method is validated using clinical data from Massachusetts General Hospital. Our code is available at https://github.com/ZWang78/SR.