Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States.
Harvard Medical School, Boston, MA, United States.
Cereb Cortex. 2021 Jan 1;31(1):463-482. doi: 10.1093/cercor/bhaa237.
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.
从解剖磁共振（MR）图像准确且自动重建活体人脑皮质表面，有助于皮质结构的定量分析。与标准的 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 图像提高了皮质表面和厚度估计的准确性。尽管如此，亚毫米分辨率的采集需要平均多次重复以获得足够的信噪比，因此时间长且容易受到受试者运动的影响。我们通过使用基于数据驱动的监督机器学习的超分辨率方法从标准的 1 毫米各向同性分辨率图像中合成亚毫米分辨率图像来解决这个挑战，该方法通过深度卷积神经网络来实现。我们使用大规模模拟数据集系统地描述了我们的方法，并在经验数据中证明了其有效性。超分辨率数据提供了改进的皮质表面，与从原始亚毫米分辨率数据获得的皮质表面相似。对于模拟数据，在单个受试者水平上，皮质表面定位和厚度估计的全脑平均绝对差异低于 100μm，在组水平上低于 50μm，对于经验数据，在单个受试者水平上低于 200μm，在组水平上低于 100μm，使得超分辨率得出的皮质表面的准确性足以满足大多数应用。
