Optimized number of the primary singular values for image reconstruction in reflection matrix based optical coherence tomography

Abstract

A reflection matrix based optical coherence tomography (OCT) is recently proposed and expected to extend the imaging-depth limit twice. However, the imaging depth and hence the image quality heavily depend on the number of primary singular values considered for image reconstruction. To this regard, we propose a method based on correlation between image pairs reconstructed from different number of singular values and corresponding remainders. The obtained correlation curve and another feature curve fetched from the former are then fed to a long short-term memory (LSTM) network classifier to identify the optimized number of primary singular values for image reconstruction. Simulated targets with different combinations of filling fraction and signal-to-noise ratio (SNR) are reconstructed by the developed method as well as two current adopted methods for comparison. The results demonstrate that the proposed method is robust to recover the image with satisfactory similarity close to the reference one. To our knowledge, this is the first comprehensive study on the optimized number of the primary singular values considered for image reconstruction in reflection matrix based OCT.

Ultra-deep three-dimensional optical coherence tomography in turbid media

“…The cells are marginally recovered in case of a thin scattering layer, while failed in case of a thicker scattering layer (Supplementary information II). This is agreed with the simulation results conducted on matrix OCT reconstruction of target with low signal-to-noise ratio (SNR) 30 . 3D distributed microbeads embedded in a tissue phantom were used as the second sample.…”

“…The 3D image with a noisy background shown in the third column of Fig. 4a is reconstructed from depth-independent uniform number of eigenstates of 50, while that shown in the last column is reconstructed by the depth-dependent adapted number of eigenstates proposed in our previous study 30 . With optimized number of eigenstates for the depth-dependent adaptive reconstruction, 3D distribution of the microbeads was well-recovered without noisy background.…”