发表机构
University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出3D残差小波扩散模型，用于超低场MRI超分辨率，通过小波重参数化、残差移位和域随机化，实现全脑后验采样并生成不确定性图，在保持体积准确性的同时处理解剖模糊性。
AI 中文摘要
超低场MRI扩展了神经影像的全球可及性，但产生的扫描图像信噪比低、对比度降低且切片较厚。虽然基于回归的超分辨率可以恢复用于分割的解剖细节，但它返回单一的确定性估计，无法指示低场输入使解剖结构欠定的区域。生成扩散模型通过采样合理高场图像的后验分布来量化这种解剖模糊性，提供了一种替代方案。然而，将其应用于3D全脑MRI受到内存瓶颈、采样速度慢和扫描仪域偏移的限制。我们提出了一种3D残差小波扩散模型，结合三个思路来克服这些障碍。无损小波重参数化缩小空间网格，使全脑适配于单个GPU；残差移位通过从低场输入开始加速采样；域随机化在无配对训练数据的情况下促进扫描仪泛化。由于高场参考并非体素对齐的真实值，我们评估下游体积一致性。在一个健康队列（n=19）中，以0.064T和3T成像，我们的方法在体积准确性上匹配领先的通用回归方法，同时额外生成逐体素不确定性图，突出欠定区域。此外，在一个认知障碍参与者的试点数据集（n=11）中，与疾病相关的萎缩得以保留，而非向健康先验归一化。我们的框架在不牺牲体积准确性的情况下，将全脑后验采样引入低场超分辨率。
英文摘要
Ultra low-field MRI expands global access to neuroimaging but produces scans with low signal-to-noise ratio, reduced contrast, and thick slices. While regression-based super-resolution can recover anatomical detail for segmentation, it returns a single deterministic estimate that gives no indication of regions where the low-field input leaves anatomy underdetermined. Generative diffusion models offer an alternative by sampling the posterior distribution of plausible high-field images, quantifying this anatomical ambiguity. However, applying them to 3D whole-brain MRI is restricted by memory bottlenecks, slow sampling, and scanner domain shifts. We propose a 3D residual wavelet diffusion model that combines three ideas to overcome these hurdles. A lossless wavelet reparameterisation shrinks the spatial grid to fit a whole brain on a single GPU, residual shifting accelerates sampling by starting from the low-field input, and domain randomisation promotes scanner generalisation without paired training data. As the high-field reference is not a voxel-aligned ground truth, we evaluate downstream volumetric agreement. On a healthy cohort (n=19) imaged at 0.064T and 3T, our method matches a leading general-purpose regression approach in volumetric accuracy while additionally generating per-voxel uncertainty maps highlighting underdetermined regions. Furthermore, on a pilot dataset (n=11) of participants with cognitive impairment, disease-relevant atrophy is preserved rather than normalised towards a healthy prior. Our framework brings whole-brain posterior sampling to low-field super-resolution without sacrificing volumetric accuracy.
Comments11 pages, 3 figures, 1 table. Accepted at SASHIMI 2026 (MICCAI 2026 workshop). This is the version submitted for peer review
