无需重训练，一键实现便携低场MRI的高精度皮层重建
From Low Field to High Value: Robust Cortical Mapping from Low-Field MRI
- 用合成数据训练3D U-Net预测皮层符号距离函数
- 3mm各向同性T2加权扫描4分钟内完成，皮层分割Dice达0.98
- 适用于便携式低场MRI，对尸体样本也表现稳健
从MRI进行三维皮层表面重建是理解脑结构的基础。尽管高场MRI（HF-MRI）在研究和临床中是标准，但其可用性受限。低场MRI（LF-MRI），尤其是便携系统，提供了低成本、易获取的替代方案。然而，现有皮层分析工具针对高分辨率HF-MRI优化，难以应对LF-MRI较低信噪比和分辨率的问题。本文提出一种机器学习方法，无需重训练即可实现多种对比度和分辨率下便携LF-MRI的3D皮层重建与分析。该方法使用在合成LF-MRI上训练的3D U-Net预测皮层符号距离函数，并通过几何处理确保拓扑准确性。我们在同一受试者配对的HF/LF-MRI扫描上评估，发现重建精度依赖于采集参数，包括对比类型（T1 vs T2）、方位（轴向 vs 各向同性）和分辨率。3mm各向同性T2加权扫描（<4分钟）与HF衍生表面高度一致：表面积相关系数r=0.96，皮层分区Dice=0.98，灰质体积相关系数r=0.93。皮层厚度仍具挑战，相关系数最高为r=0.70，反映出3mm体素下亚毫米精度困难。我们进一步在挑战性尸检LF-MRI上验证了方法的鲁棒性。本方法推动了便携式低场MRI实现皮层分析的可能。代码已公开：https://surfer.nmr.mgh.harvard.edu/fswiki/ReconAny
原文摘要 · Abstract (English)
Three-dimensional reconstruction of cortical surfaces from MRI for morphometric analysis is fundamental for understanding brain structure. While high-field MRI (HF-MRI) is standard in research and clinical settings, its limited availability hinders widespread use. Low-field MRI (LF-MRI), particularly portable systems, offers a cost-effective and accessible alternative. However, existing cortical surface analysis tools are optimized for high-resolution HF-MRI and struggle with the lower signal-to-noise ratio and resolution of LF-MRI. In this work, we present a machine learning method for 3D reconstruction and analysis of portable LF-MRI across a range of contrasts and resolutions. Our method works "out of the box" without retraining. It uses a 3D U-Net trained on synthetic LF-MRI to predict signed distance functions of cortical surfaces, followed by geometric processing to ensure topological accuracy. We evaluate our method using paired HF/LF-MRI scans of the same subjects, showing that LF-MRI surface reconstruction accuracy depends on acquisition parameters, including contrast type (T1 vs T2), orientation (axial vs isotropic), and resolution. A 3mm isotropic T2-weighted scan acquired in under 4 minutes, yields strong agreement with HF-derived surfaces: surface area correlates at r=0.96, cortical parcellations reach Dice=0.98, and gray matter volume achieves r=0.93. Cortical thickness remains more challenging with correlations up to r=0.70, reflecting the difficulty of sub-mm precision with 3mm voxels. We further validate our method on challenging postmortem LF-MRI, demonstrating its robustness. Our method represents a step toward enabling cortical surface analysis on portable LF-MRI. Code is available at https://surfer.nmr.mgh.harvard.edu/fswiki/ReconAny
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