Abstract
In this paper, a full depth 2D CS-SDOCT approach is proposed, which combines two-dimensional (2D) compressive sensing spectral-domain optical coherence tomography (CS-SDOCT) and dispersion encoding (ED) technologies, and its applications in structural imaging and functional sensing of bio-tissues are studied. Specifically, by introducing a large dispersion mismatch between the reference arm and sample arm in SD-OCT system, the reconstruction of the under-sampled A-scan data and the removal of the conjugated images can be achieved simultaneously by only two iterations. The under-sampled B-scan data is then reconstructed using the classic CS reconstruction algorithm. For a 5 mm × 3.2 mm fish-eye image, the conjugated image was reduced by 31.4 dB using 50% × 50% sampled data (250 depth scans and 480 spectral sampling points per depth scan), and all A-scan data was reconstructed in only 1.2 s. In addition, we analyze the application performance of the CS-SDOCT in functional sensing of locally homogeneous tissue. Simulation and experimental results show that this method can correctly reconstruct the extinction coefficient spectrum under reasonable iteration times. When 8 iterations were used to reconstruct the A-scan data in the imaging experiment of fisheye, the extinction coefficient spectrum calculated using 50% × 50% data was approximately consistent with that obtained with 100% data.
Highlights
Spectral-domain optical coherence tomography (SD-OCT) is a rapidly advancing optical frequency-domain imaging modality, which provides non-invasive high-resolution three-dimensional (3D) images of biological tissue [1,2,3,4], and quantification of chromophores in tissues [5,6,7]
In order to overcome these problems, the compressive sensing SD-OCT (CS-SDOCT) technique was proposed [9,10,11,12,13], and it has been demonstrated that a high-fidelity OCT image can be reconstructed using only 37.5% of k-domain spectral data [9]
In order to reduce the data processing time, and to remove the conjugate images [17,18], we propose to add the dispersion coding (DE) method to the recovery of under-sampled A-scan data in 2D CS-SDOCT in this work
Summary
Spectral-domain optical coherence tomography (SD-OCT) is a rapidly advancing optical frequency-domain imaging modality, which provides non-invasive high-resolution three-dimensional (3D) images of biological tissue [1,2,3,4], and quantification of chromophores in tissues [5,6,7]. Imaging speed and imaging depth are two significant performance indicators of SD-OCT for imaging bio-tissues, especially in vivo imaging and functional activity measurements. The improvement of the two indicators comes at a cost: an expensive, large arrays, high speed camera with a high sampling rate. In order to overcome these problems, the compressive sensing SD-OCT (CS-SDOCT) technique was proposed [9,10,11,12,13], and it has been demonstrated that a high-fidelity OCT image can be reconstructed using only 37.5% of k-domain spectral data [9]. To further compress the data volume, 2D and 3D CS-SDOCT are investigated successively, in which under-sampling is performed in two or three directions of spectral data [14,15]. In most existing 2D or 3D CS-SDCT methods, the CS algorithm is applied to each
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