3-D compressed sensing optical coherence tomography using predictive coding

We present a compressed sensing (CS) algorithm and sampling strategy for reconstructing 3-D Optical Coherence Tomography (OCT) image volumes from as little as 10% of the original data. Reconstruction using the proposed method, Denoising Predictive Coding (DN-PC), is demonstrated for five clinically relevant tissue types including human heart, retina, uterus, breast, and bovine ligament. DN-PC reconstructs the difference between adjacent b-scans in a volume and iteratively applies Gaussian filtering to improve image sparsity. An a-line sampling strategy was developed that can be easily implemented in existing Spectral-Domain OCT systems and reduce scan time by up to 90%.

Automated summary

The study introduces a compressed sensing algorithm and sampling strategy to reconstruct 3-D OCT images from as little as 10% of the original data, demonstrating reconstruction for clinically relevant tissue types. The proposed method reduces scan time and improves image sparsity by reconstructing the difference between adjacent b-scans and applying Gaussian filtering iteratively.