Optical coherence tomography (OCT), particularly Swept-Source OCT, is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities. However, Swept-Source OCT 3D imaging often suffers from stripe artifacts caused by unstable light sources, system noise, and environmental interference, posing challenges to real-time processing of large-scale datasets. To address this issue, this study introduces a real-time reconstruction system that integrates stripe-artifact suppression and parallel computing using a graphics processing unit. This approach employs a frequency-domain filtering algorithm with adaptive anti-suppression parameters, dynamically adjusted through an image quality evaluation function and optimized using a convolutional neural network for complex frequency-domain feature learning. Additionally, a graphics processing unit integrated 3D reconstruction framework is developed, enhancing data processing throughput and real-time performance via a dual-queue decoupling mechanism. Experimental results demonstrate significant improvements in structural similarity (0.92), peak signal-to-noise ratio (31.62 dB), and stripe suppression ratio (15.73 dB) compared with existing methods. On the RTX 4090 platform, the proposed system achieved an end-to-end delay of 94.36 milliseconds, a frame rate of 10.3 frames per second, and a throughput of 121.5 million voxels per second, effectively suppressing artifacts while preserving image details and enhancing real-time 3D reconstruction performance.
Dandan LIU (Sun,) studied this question.