Recon-all-clinical：异构临床脑MRI的皮层表面重建与分析
Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI
AI总结：
本文提出recon-all-clinical，结合域随机化CNN预测SDF与经典几何处理，实现任意分辨率和对比临床脑MRI的皮层重建、配准、分区及厚度估计。
摘要：
基于表面的大脑皮层分析在人类MRI神经影像研究中十分普遍，对皮层配准、分区和厚度估计至关重要。传统上，这些分析需要具有良好灰白质对比的高分辨率各向同性扫描，通常是1mm T1加权扫描。这排除了大多数临床MRI扫描，因为它们往往是各向异性的，且缺乏必要的T1对比。为利用海量临床数据开展大规模神经影像研究，我们提出recon-all-clinical，这是一种用于任意分辨率和对比的脑MRI扫描中皮层重建、配准、分区和厚度估计的新方法。我们的方法采用混合分析方式，将经域随机化训练、用于预测有符号距离函数（SDF）的卷积神经网络（CNN）与经典几何处理相结合，以在保持拓扑和几何约束的同时精确放置表面。该方法无需针对不同采集方式重新训练，从而简化了异构临床数据集的分析。我们在多个数据集（包括超过19,000次临床扫描）上测试了recon-all-clinical。在不同MRI对比和分辨率下，该方法始终能生成精确的皮层重建和较高的分区准确率。皮层厚度估计足够精确，可独立于MRI对比捕捉衰老效应，不过准确率会随层厚变化。我们的方法已公开发布于https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical，使研究人员能够对大量已存在的临床MRI扫描进行详细皮层分析。这一进展对于研究缺乏科研级MRI数据的罕见疾病和代表性不足人群可能尤其有价值。
英文摘要：
Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for cortical registration, parcellation, and thickness estimation. Traditionally, these analyses require high-resolution, isotropic scans with good gray-white matter contrast, typically a 1mm T1-weighted scan. This excludes most clinical MRI scans, which are often anisotropic and lack the necessary T1 contrast. To enable large-scale neuroimaging studies using vast clinical data, we introduce recon-all-clinical, a novel method for cortical reconstruction, registration, parcellation, and thickness estimation in brain MRI scans of any resolution and contrast. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions (SDFs) and classical geometry processing for accurate surface placement while maintaining topological and geometric constraints. The method does not require retraining for different acquisitions, thus simplifying the analysis of heterogeneous clinical datasets. We tested recon-all-clinical on multiple datasets, including over 19,000 clinical scans. The method consistently produced precise cortical reconstructions and high parcellation accuracy across varied MRI contrasts and resolutions. Cortical thickness estimates are precise enough to capture aging effects independently of MRI contrast, although accuracy varies with slice thickness. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical, enabling researchers to perform detailed cortical analysis on the huge amounts of already existing clinical MRI scans. This advancement may be particularly valuable for studying rare diseases and underrepresented populations where research-grade MRI data is scarce.
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