Abstract

Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.

中文翻译：

---

使用数据驱动的超分辨率改善体内人类大脑皮层表面重建。

 抽象的

根据解剖磁共振 (MR) 图像准确自动重建体内人类大脑皮层表面，有助于皮层结构的定量分析。与标准 1 毫米各向同性分辨率相比，具有亚毫米各向同性空间分辨率的解剖 MR 图像可提高皮质表面和厚度估计的准确性。尽管如此，亚毫米分辨率采集需要多次重复进行平均才能实现足够的信噪比，因此采集时间较长且可能容易受到对象运动的影响。我们通过使用通过深度卷积神经网络实现的数据驱动的、基于监督机器学习的超分辨率方法，从标准 1 毫米各向同性分辨率图像合成亚毫米分辨率图像来解决这一挑战。我们使用大规模模拟数据集系统地描述我们的方法，并在经验数据中证明其有效性。超分辨率数据提供了改进的皮质表面，类似于从原始亚毫米分辨率数据获得的皮质表面。对于模拟数据，皮质表面定位和厚度估计的全脑平均绝对差异在单受试者水平上低于 100 μm，在组水平上低于 50 μm，在单受试者水平上低于 200 μm，在 100 以下µm 在组水平上用于经验数据，使得超分辨率得出的皮质表面的精度足以满足大多数应用。