SIINR：用于临床质量扩散MRI数据集超分辨率及不确定性量化的结构信息隐式神经表示
SIINR: Structurally Informed Implicit Neural Representations for super-resolution with uncertainty quantification of clinical quality diffusion MRI datasets
- Eindhoven University of Technology（埃因霍温理工大学）
- University of South Carolina（南卡罗来纳大学）
- Brigham and Women’s Hospital, Harvard Medical School（布莱根妇女医院，哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对临床dMRI数据集平面外分辨率低的问题，提出SIINR框架，利用监督3D U-net和自监督INR结合，实现超分辨率及不确定性量化，在多数据集和临床病例实验中表现优异，为临床dMRI增强及指标解释提供方法。
AI中文摘要：
扩散磁共振成像（dMRI）是探测脑微结构的有力工具，但临床采集常受平面外低分辨率限制，导致结构信息退化及高级分析效用降低。我们引入SIINR（结构信息隐式神经表示），这是一个用于临床dMRI数据集超分辨率并量化重建输出不确定性的通用框架。SIINR利用监督3D U-net作为先验，并将其与融合高分辨率先验和原始低分辨率数据的自监督隐式神经表示（INR）相结合。INR实现跨空间和角度域的联合建模，强制数据一致性，并为下游不确定性量化提供解析近似后验分布。我们在各种开放获取的dMRI数据集上验证了该框架，表明SIINR在定量误差指标和定性解剖保真度方面均优于标准插值方法。对包括多发性硬化症和脑损伤患者在内的临床病例的实验，说明了该框架在具有挑战性的场景中传播强度变化和标记不确定区域的能力。SIINR灵活、模块化，可适应不同的上采样率和下游任务，为增强临床dMRI和支持对衍生神经影像指标的稳健解释提供了一种有原则的方法。
英文摘要：
Diffusion Magnetic Resonance Imaging (dMRI) is a powerful tool for probing brain microstructure, but clinical acquisitions are often limited by low out-of-plane resolution, resulting in degraded structural information and reduced utility for advanced analysis. We introduce SIINR (Structurally Informed Implicit Neural Representations), a general framework for super-resoltion of clinical dMRI datasets while quantifying uncertainty in the reconstructed outputs. SIINR utilizes a supervised 3D U-net as a prior and combines it with a self-supervised implicit neural representation (INR) that fuses the high-resolution prior and the original low-resolution data. The INR enables joint modeling across spatial and angular domains, enforces data consistency, and provides analytic approximate posterior distributions for downstream uncertainty quantification. We validate the framework on a diverse set of open-access dMRI datasets, demonstrating that SIINR outperforms standard interpolation methods in both quantitative error metrics and qualitative anatomical fidelity. Experiments on clinical cases, including subjects with multiple sclerosis and brain lesions, illustrate the framework its ability to propagate intensity changes and flag uncertain regions in challenging scenarios. SIINR is flexible, modular, and can be adapted to different upsampling ratios and downstream tasks, providing a principled approach for enhancing clinical dMRI and supporting robust interpretation of derived neuroimaging metrics.
