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)校正对于内窥镜光学相干断层扫描(OCT)成像及其功能扩展（如血管成像和弹性成像）至关重要。当前的NURD校正方法需要耗时地跟踪特征或执行互相关计算，因此会牺牲时间分辨率。为此，我们提出了一种用于OCT中NURD校正的交叉注意力学习方法。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。通过利用其对长距离依赖关系的建模能力，我们可直接获得任意距离下OCT A线间的相关性，从而加速NURD校正。我们构建了一个端到端的堆叠式交叉注意力网络，并设计了三种优化约束条件。我们在两个公开的内窥镜OCT数据集以及一个自研内窥镜OCT系统采集的私有数据集上，将所提方法与两种传统基于特征的方法和一种基于CNN的方法进行了对比。我们的方法实现了约3倍的速度提升，达到实时处理水平（26±3帧/秒），并获得更优的校正性能。