Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals. However, conventional registration algorithms are computationally inefficient. Recently, learning-based registration algorithms have emerged as a promising solution, significantly improving processing efficiency. Nonetheless, there remains a gap in the development of a learning-based method that exceeds the state-of-the-art conventional methods simultaneously in computational efficiency, registration accuracy, and distortion control, despite the theoretically greater representational capabilities of deep learning approaches. To address the challenge, we present SUGAR, a unified unsupervised deep-learning framework for both rigid and non-rigid registration. SUGAR incorporates a U-Net-based spherical graph attention network and leverages the Euler angle representation for deformation. In addition to the similarity loss, we introduce fold and multiple distortion losses to preserve topology and minimize various types of distortions. Furthermore, we propose a data augmentation strategy specifically tailored for spherical surface registration to enhance the registration performance. Through extensive evaluation involving over 10,000 scans from 7 diverse datasets, we showed that our framework exhibits comparable or superior registration performance in accuracy, distortion, and test-retest reliability compared to conventional and learning-based methods. Additionally, SUGAR achieves remarkable sub-second processing times, offering a notable speed-up of approximately 12,000 times in registering 9,000 subjects from the UK Biobank dataset in just 32 min. This combination of high registration performance and accelerated processing time may greatly benefit large-scale neuroimaging studies.

中文翻译：

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SUGAR：用于皮质表面配准的球形超快图形注意框架

皮质表面配准在协调个体皮质功能和解剖特征方面发挥着至关重要的作用。然而，传统的配准算法计算效率低下。最近，基于学习的配准算法已成为一种有前景的解决方案，可显着提高处理效率。尽管如此，尽管深度学习方法在理论上具有更强的表征能力，但在计算效率、配准精度和失真控制方面同时超越最先进的传统方法的基于学习的方法的开发仍然存在差距。为了应对这一挑战，我们提出了 SUGAR，一个用于刚性和非刚性配准的统一无监督深度学习框架。 SUGAR 结合了基于 U-Net 的球形图注意力网络，并利用欧拉角表示进行变形。除了相似性损失之外，我们还引入了折叠和多重失真损失来保留拓扑并最大限度地减少各种类型的失真。此外，我们提出了一种专门针对球面配准定制的数据增强策略，以提高配准性能。通过对来自 7 个不同数据集的 10,000 多次扫描进行的广泛评估，我们表明，与传统方法和基于学习的方法相比，我们的框架在准确性、失真和重测可靠性方面表现出相当或更好的配准性能。此外，SUGAR 的处理时间达到了显着的亚秒级，在短短 32 分钟内从英国生物银行数据集中注册 9,000 名受试者，速度显着提高了约 12,000 倍。高配准性能和加速处理时间的结合可能会极大地有利于大规模神经影像研究。