Intensity-enhanced deep network wavefront reconstruction in Shack-Hartmann sensors.

The Shack-Hartmann wavefront sensor (SH-WFS) is known to produce incorrect measurements of the wavefront gradient in the presence of non-uniform illumination. Moreover, the most common least-squares phase reconstructors cannot accurately reconstruct the wavefront in the presence of branch points. We therefore developed the intensity/slopes network (ISNet), a deep convolutional-neural-network-based reconstructor that uses both the wavefront gradient information and the intensity of the SH-WFS's subapertures to provide better wavefront reconstruction. We trained the network on simulated data with multiple levels of turbulence and compared the performance of our reconstructor to several other reconstruction techniques. ISNet produced the lowest wavefront error of the reconstructors we evaluated and operated at a speed suitable for real-time applications, enabling the use of the SH-WFS in stronger turbulence than was previously possible.

Super-resolution retinal imaging using optically reassigned scanning laser ophthalmoscopy.

Super-resolution optical microscopy techniques have enabled the discovery and visualization of numerous phenomena in physics, chemistry and biology1-3. However, the highest resolution super-resolution techniques depend on nonlinear fluorescence phenomena and are thus inaccessible to the myriad applications that require reflective imaging4,5. One promising super-resolution technique is optical reassignment6, which so far has only shown potential for fluorescence imaging at low speeds. Here, we present novel advances in optical reassignment to adapt it for any scanning microscopy, including reflective imaging, and enable an order of magnitude faster image acquisition than previous optical reassignment techniques. We utilized these advances to implement optically reassigned scanning laser ophthalmoscopy, an in vivo super-resolution human retinal imaging device not reliant on confocal gating. Using this instrument, we achieved high-resolution imaging of living human retinal cone photoreceptor cells (determined by minimum foveal eccentricity) without adaptive optics or chemical dilation of the eye7.

Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography.

Optical coherence tomography (OCT) has revolutionized diagnosis and prognosis of ophthalmic diseases by visualization and measurement of retinal layers. To speed up the quantitative analysis of disease biomarkers, an increasing number of automatic segmentation algorithms have been proposed to estimate the boundary locations of retinal layers. While the performance of these algorithms has significantly improved in recent years, a critical question to ask is how far we are from a theoretical limit to OCT segmentation performance. In this paper, we present the Cramèr-Rao lower bounds (CRLBs) for the problem of OCT layer segmentation. In deriving the CRLBs, we address the important problem of defining statistical models that best represent the intensity distribution in each layer of the retina. Additionally, we calculate the bounds under an optimal affine bias, reflecting the use of prior knowledge in many segmentation algorithms. Experiments using in vivo images of human retina from a commercial spectral domain OCT system are presented, showing potential for improvement of automated segmentation accuracy. Our general mathematical model can be easily adapted for virtually any OCT system. Furthermore, the statistical models of signal and noise developed in this paper can be utilized for the future improvements of OCT image denoising, reconstruction, and many other applications.