The problem of Cortical Surface Reconstruction from magnetic resonance imaging has been traditionally addressed using lengthy pipelines of image processing techniques like FreeSurfer, CAT, or CIVET. These frameworks require very long runtimes deemed unfeasible for real-time applications and unpractical for large-scale studies. Recently, supervised deep learning approaches have been introduced to speed up this task cutting down the reconstruction time from hours to seconds. Using the state-of-the-art CorticalFlow model as a blueprint, this paper proposes three modifications to improve its accuracy and interoperability with existing surface analysis tools, while not sacrificing its fast inference time and low GPU memory consumption. First, we employ a more accurate ODE solver to reduce the diffeomorphic mapping approximation error. Second, we devise a routine to produce smoother template meshes avoiding mesh artifacts caused by sharp edges in CorticalFlow's convex-hull based template. Last, we recast pial surface prediction as the deformation of the predicted white surface leading to a one-to-one mapping between white and pial surface vertices. This mapping is essential to many existing surface analysis tools for cortical morphometry. We name the resulting method CorticalFlow$^{++}$. Using large-scale datasets, we demonstrate the proposed changes provide more geometric accuracy and surface regularity while keeping the reconstruction time and GPU memory requirements almost unchanged.

翻译：从磁共振成像学的表面整治问题传统上一直通过使用FreeSurfer、CAT或CIVET等图像处理技术的长长管道来解决。 这些框架需要非常长期的时间,认为实时应用程序不可行,大规模研究则不切实际。 最近,引入了监督的深层学习方法,以加快这项工作,将重建时间从几个小时缩短到几秒钟。 使用最先进的CorticalFlow模型作为蓝图,本文建议进行三项修改,以提高其准确性和与现有表面分析工具的互操作性,同时不牺牲其快速的推断时间和低的GPU记忆消耗量。 首先,我们使用更精确的 ODE 求解器来减少地貌图的近似误差。 其次,我们设计了一种常规的方法来制作更滑动的模板,将重建时间从几小时缩短到几秒。 最后,我们重新将平流表面预测的预测作为白表表面的变形,导致白平面和平面的近一比一比的精确的精确度要求,而我们用一个更精确的内存的内存的内存的内存的内存内存内存的内存的内存内存的内存内存内存的内存。 我们设计的地平平平平平平平平平平平平平平平平平平平平平的平的地图是用来用来进行现有的地表的平流的平面分析。