Magnetic resonance imaging (MRI) is critical for diagnosing neurodegenerative diseases, yet accurately assessing mild cortical atrophy remains a challenge due to its subtlety. Automated cortex reconstruction, paired with healthy reference ranges, aids in pinpointing pathological atrophy, yet their generalization is limited by biases from image acquisition and processing. We introduce the concept of stochastic cortical self-reconstruction (SCSR) that creates a subject-specific healthy reference by taking MRI-derived thicknesses as input and, therefore, implicitly accounting for potential confounders. SCSR randomly corrupts parts of the cortex and self-reconstructs them from the remaining information. Trained exclusively on healthy individuals, repeated self-reconstruction generates a stochastic reference cortex for assessing deviations from the norm. We present three implementations of this concept: XGBoost applied on parcels, and two autoencoders on vertex level -- one based on a multilayer perceptron and the other using a spherical U-Net. These models were trained on healthy subjects from the UK Biobank and subsequently evaluated across four public Alzheimer's datasets. Finally, we deploy the model on clinical in-house data, where deviation maps' high spatial resolution aids in discriminating between four types of dementia.

翻译：磁共振成像（MRI）对于诊断神经退行性疾病至关重要，但由于轻度皮层萎缩的细微性，准确评估仍面临挑战。自动化皮层重建结合健康参考范围有助于识别病理性萎缩，然而其泛化性受限于图像采集和处理带来的偏差。我们提出"随机皮层自重构"（SCSR）概念——以MRI衍生的皮层厚度为输入，生成个体特异性健康参考，从而隐式处理潜在混杂因素。SCSR随机破坏部分皮层区域，再利用剩余信息进行自我重构。基于健康个体训练的模型通过重复自重构生成随机参考皮层，用于评估偏离正常值的程度。我们呈现了该概念的三种实现方式：基于分区的XGBoost、基于顶点水平的两种自编码器（多层感知机结构与球形U-Net结构）。这些模型在UK Biobank健康受试者数据上训练，随后在四个公开阿尔茨海默病数据集上评估。最后，我们将模型部署于临床内部数据，其偏差图的高空间分辨率有助于区分四种痴呆类型。