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.

磁共振成像（MRI）对诊断神经退行性疾病至关重要，然而准确评估轻度皮质萎缩仍然是一个挑战，因为其细微性质。我们介绍了随机皮质自重建（SCSR）的概念，通过使用 MRI 获得的厚度作为输入来创建一个个体特异的健康参考，并隐式考虑潜在的混淆因素。我们呈现了这个概念的三个实现：XGBoost 应用于分块，以及两个基于顶点级的自编码器 - 一个基于多层感知机，另一个是使用球面 U-Net。这些模型对来自英国生物库的健康个体进行了训练，随后在四个公共阿尔茨海默病数据集上进行了评估。最后，我们将该模型部署到临床自有数据上，在区分四种类型的痴呆症方面，偏离图的高空间分辨率有所帮助。

随机皮层自重建

Stochastic Cortical Self-Reconstruction

The thickness of the cortical band is linked to various neurological and
psychiatric conditions, and is often estimated through surface-based methods
such as Freesurfer in MRI studies. The DiReCT method, which calculates cortical
thickness using a diffeomorphic deformation of the gray-white matter interface
towards the pial surface, offers an alternative to surface-based methods.
Recent studies using a synthetic cortical thickness phantom have demonstrated
that the combination of DiReCT and deep-learning-based segmentation is more
sensitive to subvoxel cortical thinning than Freesurfer.
While anatomical segmentation of a T1-weighted image now takes seconds,
existing implementations of DiReCT rely on iterative image registration methods
which can take up to an hour per volume. On the other hand, learning-based
deformable image registration methods like VoxelMorph have been shown to be
faster than classical methods while improving registration accuracy. This paper
proposes CortexMorph, a new method that employs unsupervised deep learning to
directly regress the deformation field needed for DiReCT. By combining
CortexMorph with a deep-learning-based segmentation model, it is possible to
estimate region-wise thickness in seconds from a T1-weighted image, while
maintaining the ability to detect cortical atrophy. We validate this claim on
the OASIS-3 dataset and the synthetic cortical thickness phantom of Rusak et
al.

该研究提出了 CortexMorph 方法，该方法利用无监督深度学习直接回归 DiReCT 所需的变形场，结合深度学习的分割模型，可以在几秒钟内从 T1 加权图像中估计区域厚度，同时保持检测皮层萎缩的能力。