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Two-dimensional compressive sensing in spectral domain optical coherence tomography

In this paper, we proposed a novel compressive sensing (CS) method in spectral domain optical coherence tomography (SD OCT), which reconstructs B-scan image using a subset of the spectral data that is under-sampled in both axial and lateral dimensions. Thus a fraction of the A-scans for a B-scan are acquired; the spectral data of each acquired A-scan is under-sampled. Compared with the previous studies, our method further reduces the overall size of the spectral measurements. Experimental results show that our approach can obtain high quality B-scan image using 25% spectral data, which takes 50% number of A-scans and acquires 50% spectral data for each selected A-scan.