Nonuniform rotational distortion (NURD) correction is vital for endoscopic optical coherence tomography (OCT) imaging and its functional extensions, such as angiography and elastography. Current NURD correction methods require time-consuming feature tracking or cross-correlation calculations and thus sacrifice temporal resolution. Here we propose a cross-attention learning method for the NURD correction in OCT. Our method is inspired by the recent success of the self-attention mechanism in natural language processing and computer vision. By leveraging its ability to model long-range dependencies, we can directly obtain the correlation between OCT A-lines at any distance, thus accelerating the NURD correction. We develop an end-to-end stacked cross-attention network and design three types of optimization constraints. We compare our method with two traditional feature-based methods and a CNN-based method, on two publicly-available endoscopic OCT datasets and a private dataset collected on our home-built endoscopic OCT system. Our method achieved a $\sim3\times$ speedup to real time ($26\pm 3$ fps), and superior correction performance.

翻译：非均匀旋转畸变（NURD）校正对于内窥光学相干断层扫描（OCT）成像及其功能扩展（如血管成像和弹性成像）至关重要。当前NURD校正方法需要耗时的特征跟踪或互相关计算，从而牺牲了时间分辨率。本文提出了一种基于交叉注意力学习的OCT NURD校正方法。该方法受近期自注意力机制在自然语言处理和计算机视觉中成功应用的启发。通过利用其建模长距离依赖关系的能力，我们可直接获取任意距离OCT A-scan之间的相关性，从而加速NURD校正。我们开发了一种端到端的堆叠式交叉注意力网络，并设计了三种类型的优化约束条件。我们将该方法与两种传统基于特征的方法及一种基于CNN的方法，在两个公开内窥OCT数据集及一个自建内窥OCT系统采集的私有数据集上进行了比较。本方法实现了约3倍加速至实时水平（26±3帧/秒），并取得了更优的校正性能。