Cross-attention learning enables real-time nonuniform rotational distortion correction in OCT
This work addresses a bottleneck in endoscopic OCT imaging for medical applications, offering a significant speed improvement over existing methods.
The paper tackled the problem of slow nonuniform rotational distortion correction in endoscopic optical coherence tomography by proposing a cross-attention learning method, which achieved a ~3× speedup to real-time performance at 26±3 fps and superior correction results.
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.