Harmonization methods such as ComBat and its variants are widely used to mitigate diffusion MRI (dMRI) site-specific biases. However, ComBat assumes that subject distributions exhibit a Gaussian profile. In practice, patients with neurological disorders often present diffusion metrics that deviate markedly from those of healthy controls, introducing pathological outliers that distort site-effect estimation. This problem is particularly challenging in clinical practice as most patients undergoing brain imaging have an underlying and yet undiagnosed condition, making it difficult to exclude them from harmonization cohorts, as their scans were precisely prescribed to establish a diagnosis. In this paper, we show that harmonizing data to a normative reference population with ComBat while including pathological cases induces significant distortions. Across 7 neurological conditions, we evaluated 10 outlier rejection methods with 4 ComBat variants over a wide range of scenarios, revealing that many filtering strategies fail in the presence of pathology. In contrast, a simple MLP provides robust outlier compensation enabling reliable harmonization while preserving disease-related signal. Experiments on both control and real multi-site cohorts, comprising up to 80% of subjects with neurological disorders, demonstrate that Robust-ComBat consistently outperforms conventional statistical baselines with lower harmonization error across all ComBat variants.

翻译：诸如ComBat及其变体的协调方法被广泛用于减轻扩散MRI（dMRI）中站点特异性偏差。然而，ComBat假设受试者分布呈现高斯分布。在实践中，患有神经系统疾病的患者常表现出与健康对照组显著不同的扩散指标，从而引入病理离群值，扭曲站点效应估计。这一问题在临床实践中尤为棘手，因为大多数接受脑成像的患者存在潜在且尚未确诊的疾病，这使得难以将其从协调队列中排除——恰恰是因为他们的扫描正是为了确立诊断而开具的。在本文中，我们证明：使用ComBat将数据协调至规范参考人群时，若包含病理病例，会引发显著扭曲。针对7种神经系统疾病，我们在广泛场景下评估了10种离群值剔除方法及4种ComBat变体，结果表明许多过滤策略在存在病理数据时会失效。相比之下，一个简单的MLP提供了鲁棒的离群值补偿，能够在保留疾病相关信号的同时实现可靠的协调。针对包含高达80%神经系统疾病受试者的对照队列和真实多中心队列的实验表明，鲁棒ComBat在所有ComBat变体下均持续优于传统统计基线，具有更低的协调误差。