  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.
