Optimizing numerical k-sampling for swept-source optical coherence tomography angiography.

High-quality swept-source optical coherence tomography (SS-OCT) requires accurate k-sampling, which is equally vital for optical coherence tomography angiography (OCTA). Most SS-OCT systems are equipped with hardware-driven k-sampling. However, this conventional approach raises concerns over system cost, optical alignment, imaging depth, and stability in the clocking circuit. This work introduces an optimized numerical k-sampling method to replace the additional k-clock hardware. Using this method, we can realize high axial resolution (4.9-µm full-width-half-maximum, in air) and low roll-off (2.3 dB loss) over a 4-mm imaging depth. The high axial resolution and sensitivity achieved by this simple numerical method can reveal anatomic and microvascular structures with structural OCT and OCTA in both macular and deeper tissues, including the lamina cribrosa, suggesting its usefulness in imaging retinopathy and optic neuropathy.

Deep learning-based method for non-uniform motion-induced error reduction in dynamic microscopic 3D shape measurement.

The non-uniform motion-induced error reduction in dynamic fringe projection profilometry is complex and challenging. Recently, deep learning (DL) has been successfully applied to many complex optical problems with strong nonlinearity and exhibits excellent performance. Inspired by this, a deep learning-based method is developed for non-uniform motion-induced error reduction by taking advantage of the powerful ability of nonlinear fitting. First, a specially designed dataset of motion-induced error reduction is generated for network training by incorporating complex nonlinearity. Then, the corresponding DL-based architecture is proposed and it contains two parts: in the first part, a fringe compensation module is developed as network pre-processing to reduce the phase error caused by fringe discontinuity; in the second part, a deep neural network is employed to extract the high-level features of error distribution and establish a pixel-wise hidden nonlinear mapping between the phase with motion-induced error and the ideal one. Both simulations and real experiments demonstrate the feasibility of the proposed method in dynamic macroscopic measurement.

Deep-learning-based approach for strain estimation in phase-sensitive optical coherence elastography.

In this Letter, a deep-learning-based approach is proposed for estimating the strain field distributions in phase-sensitive optical coherence elastography. The method first uses the simulated wrapped phase maps and corresponding phase-gradient maps to train the strain estimation convolution neural network (CNN) and then employs the trained CNN to calculate the strain fields from measured phase-difference maps. Two specimens with different deformations, one with homogeneous and the other with heterogeneous, were measured for validation. The strain field distributions of the specimens estimated by different approaches were compared. The results indicate that the proposed deep-learning-based approach features much better performance than the popular vector method, enhancing the SNR of the strain results by 21.6 dB.

Surface acoustic wave propagation in Y- and Z-cut 0.67PbMgNbO(3)-0.33PbTiO(3) single crystals.

Surface acoustic wave (SAW) propogation in Y-cut and Z-cut relaxor-based 0.67Pb(Mg(13)Nb(23))O(3)-0.33PbTiO(3) (PMN-33%PT) ferroelectric single crystals poled along [001](c) cubic direction has been analyzed theoretically. Characteristic curves were obtained for the phase velocity, electromechanical coupling coefficient and PFA of SAW in Y-cut and Z-cut PMN-33%PT single crystals. It was found that the phase velocity is lower, while the electromechanical coupling coefficient is much higher, than that of traditional piezoelectric materials. The power flow angle (PFA) can be zero in certain directions, which makes this crystal system a promising material for future SAW devices with smaller size and enhanced performance. The directions near 45 degrees and 135 degrees of Y-cut PMN-33%PT crystals are found to be optimum directions for SAW device applications.

Surface acoustic wave propagation properties in 0.67Pb(Mg(13)Nb(23))O(3)-0.33PbTiO(3) single crystal poled along [111](c).

Surface acoustic wave (SAW) propagation properties in relaxor-based 0.67Pb(Mg(13)Nb(23))O(3)-0.33PbTiO(3) (PMN-33%PT) ferroelectric single crystals poled along [111](c) has been analyzed theoretically. We found that the X-cut PMN-33%PT has lower phase velocity and higher electromechanical coupling coefficient compared to traditional piezoelectric materials. The power flow angle (PFA) can be zero in specific directions, which could drastically improve the performance of SAW devices. Our theoretical results indicate that the direction about 5 degrees canted from [111](c) is the optimum direction for the X-cut [111](c) poled crystals in SAW device applications. Characteristic curves were also obtained for the phase velocity, electromechanical coupling coefficient, and PFA in Z-cut single-domain PMN-33%PT single crystals.