This PDF file contains the front matter associated with SPIE Proceedings Volume 12388, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Liquid crystal on silicon (LCoS) spatial light modulators (SLMs) are versatile scientific tools relevant to an increasingly wide variety of research and technological applications including digital holography, wavefront correction, optical tweezing, and non-mechanical beam steering to name a few. Since SLMs are used in a multitude of different ways, some aspects of device performance (e.g., response time) are crucial to certain applications while being irrelevant to others. In this work we couple our standard SLMs with a thermo-electric cooler, allowing for tunability of the device operating temperature from 0° to 75 °C. We show that there is an inherent tradeoff between the liquid crystal response time and the phase stability of an SLM, and that the operating temperature offers a means of controlling this tradeoff. Furthermore, this paper aims to provide the reader with a brief but thorough explanation of SLM operating principles and device structure, defines the performance metrics of the SLM, and provides a methodology for measuring the specifications. By allowing control over the SLM operating temperature and detailing how temperature affects device functionality, SLM users are afforded greater experimental flexibility and will be better able to tailor the performance of their device for the given project or application at hand.

Two photon microscopy has found one of its main applications in the study of deep brain regions thanks to its intrinsic optical sectioning and deep penetration capabilities. One of the main limitations of deep 3D imaging is the arise of aberrations that can be reduced if not totally corrected using Adaptive Optics (AO). In this work we present the result of using a tunable acoustic gradient lens in combination with a multi actuator deformable lens placed in the back aperture pupil of the objective lens to correct for the sample aberrations.

Light sheet fluorescence microscopy is an excellent tool for imaging larger and thicker model organisms such as zebrafish larvae. Samples can be imaged with high spatial and temporal resolution over large fields of view without photodamage. But light sheet microscopy still suffers from optical aberrations due to the thickness of the samples and the high resolution. We have previously demonstrated imaging of the zebrafish central nervous system and correction of aberrations using sensorless Adaptive Optics. Sensorless AO is slow because many images are required to achieve a correction. In this work, we demonstrate measuring the wavefront using a Shack-Hartmann wavefront sensor and a confocal spot as the guide star. This approach does not require special sample preparation or an additional laser. Here we demonstrate our approach by imaging fluorescent beads and inducing wavefront errors with the deformable mirror.

In this presentation we show a method capable of measuring and correcting field dependent aberration in a microscope setup without a dedicated wavefront sensor using a pupil conjugated deformable lenses in combination with anisoplanatic deconvolution.

Formation of complex light structures and three-dimensional complex holography through turbid media is a challenging task due to random variation in refractive index, scattering of light, and formation of speckle noise field. We present a binary phase based FLC-SLM in binary mode assisted GA algorithm where the R-squared optimization function is introduced for wavefront shaping. An advanced system design has been developed from scratch with FLC-SLM (ferroelectric liquid crystal spatial light modulator) and dual camera to construct multiple complex structures simultaneously in 3D volume independently without any co-linearity. The formation of multiple complex structures simultaneously in 3D volume has been demonstrated with our prototype system using fresh chicken tissue of thickness 612 µm as well as a 220 grit ground glass diffuser.

We present a rapid approach for the estimation of a wavefront shaping modulation from single-photon (linear) fluorescent sources. The approach uses non-invasive, guide star free feedback. While estimating a wavefront shaping correction from real tissue samples is a complex time-consuming task, iterative time reversal techniques, previously designed for coherent illumination, carry the potential to reduce this acquisition cost. For that, they assume the desired correction mask is an eigenvector of the tissue transmission operator, and compute it using a small number of power iterations. Unfortunately, under coherent illumination, this approach can only handle restricted targets. We extend this idea to incoherent fluorescent targets. We show that the incoherent summation allows the approach to handle a larger family of targets and also accelerates its convergence.

We demonstrate an extended-depth-of-field miniscope (EDoF-Miniscope) which utilizes an optimized binary diffractive optical element (DOE) to achieve a 2.8x axial elongation in twin foci when integrated on the pupil plane. We optimize our DOE through a genetic algorithm, which utilizes a Fourier optics forward model to consider the native aberrations of the primary gradient refractive index (GRIN) lens, optical property of the submersion media, the geometric effects of the target fluorescent sources and axial intensity loss from tissue scattering to create a robust EDoF. We demonstrate that our platform achieves high contrast signals that can be recovered through a simple filter across 5-μm and 10-μm beads embedded in scattering phantoms, and fixed mouse brain samples.

We exploit memory effect correlations in speckles for the imaging of incoherent fluorescent sources behind scattering tissue. These correlations are often weak when imaging thick scattering tissues and complex illumination patterns, both of which greatly limit the practicality of associated techniques. In this work, we introduce a spatial light modulator between the tissue sample and the imaging sensor and capture multiple modulations of the speckle pattern. We show that, by correctly designing the modulation patterns and the associated reconstruction algorithm, the statistical correlations in the measurements can be greatly enhanced. We exploit this to demonstrate the reconstruction of mega-pixel sized fluorescent patterns behind the scattering tissue.

The scattering of light was long thought to prevent imaging through opaque materials. However, scattering from static objects is deterministic, and in the last 15 years, a series of pioneering studies have shown us that it is possible to use a technique called wavefront shaping to characterise and subsequently cancel out complicated scattering effects. Light that has undergone multiple scattering can be ‘untangled’ to see through opaque media, such as frosted glass, biological tissue, or multimode optical fibres.

Transmission matrix measurements relating the electric field at the ends of standard step-index and graded-index multimode fibers promise to enable next generation miniaturized endoscopes. Relatively few measurements of specialty fibers and components have been demonstrated. Here, we present transmission matrix measurements and distal control through a variety of specialty fibers, including fibers for harsh environments, a polarization maintaining fiber, coreless fibers, a rectangular core fiber, multicore fibers, and a pump signal combiner. The calibration of these fibers and structures enables their dual-use for imaging and their original design application and allows control of the spatial profile of the light used in sensing, power delivery, and amplification.

Multimode fibers (MMFs) have a very large number of propagating modes per unit area and therefore allow for imaging with a very large number of pixels relative to their diameter. This makes MMFs perfect candidates for ultrathin endoscopes in applications such as deep brain imaging. However, the accuracy of the input-output relation that is needed, e.g., for distal spot scanning without moving parts, requires a new calibration after the fiber position or temperature has been significantly altered.

While neural networks have been used before to attempt to solve these challenges, we present an MMF-based imaging method that tolerates and classifies different fiber positions, using two single-layer fully-connected neural networks that only require the optical intensity without measuring the optical phase. One network learns the nonlinear relation between the input and output intensities and allows for image reconstruction in the presence of position changes, while the other network classifies that position change for different images. We show that our method is superior to memory-effect-based position sensing, both for small position changes where the relation between position change and output specklegram rotation angle is linear, as well as for larger position changes where this linearity and uniqueness break down. We also show that the position classification results are robust to temperature and polarization perturbations, and that our position classifier is able to effectively generalize. Likewise, we show that our imaging network also is robust to 30°C perturbations in temperature and 10° in polarization.

Endoscopes have been widely used for biomedical imaging applications like surgical guidance and diagnosis. In this project, we demonstrated a beam-shaping system to manipulate the illumination patterns at the distal tip of the multimode fiber by using the real-valued intensity transmission matrix of the MMF for endoscopic applications, which provides the potential to miniaturize the footprint of the structured illumination system and the endoscope geometry.

Hair-thin strands of multimode optical fibre (MMF) can operate as ultra-low footprint endoscopes–delivering sub-cellular resolution images from deep inside the body at the tip of a fine needle. However, images transmitted through MMFs are unrecognisably distorted. Here we present two new ways to unscramble this light and recover images. Firstly, we describe a new in-situ calibration technique requiring access to only the input end of the fibre–promising a way to image through flexible fibres. Secondly, we describe the design of a new optical element–an ‘optical inverter’–that can unscramble all modes in parallel, offering the potential of single-shot and super-resolution imaging through MMFs.

Wavefront shaping correction aims to image fluorescent particles deep inside scattering tissue. This requires determining a correction mask to be placed in both excitation and emission paths. Standard optimization-based approaches for finding this correction are prohibitively slow. To reduce acquisition cost, iterative phase conjugation techniques use the observation that the desired correction mask is an eigenvector of the tissue transmission operator. They then determine this eigenvector via optical implementations of the power iteration method, which require capturing orders of magnitude fewer images. Existing iterative phase conjugation techniques apply to fully-coherent imaging systems. We extend such techniques to the incoherent case for the first time. The fact that light emitted from different sources sums incoherently makes linear transmission operators inapplicable. We show that, surprisingly, the non-linearity due to incoherent summation results in an order-of-magnitude acceleration in the convergence of the phase conjugation iteration.

We are interested in measuring the volumetric scattering phase function of materials. While previous approaches have relied on computationally costly inverse rendering optimization, we suggest a simple closed-form approach, for acquiring scattering phase function from thick samples. We have built a prototype capable of measuring phase functions over a narrow angular cone. We test our approach using validation materials whose phase function is known; and we use it to capture a set of everyday materials.

Optical microscopy with adaptive optics (AO) allows high-resolution noninvasive imaging of subcellular structures in living organisms. As alternatives to hardware-based AO methods, supervised deep-learning approaches have recently been developed to estimate optical aberrations. However, these approaches are often limited in their generalizability due to discrepancies between training and imaging settings. Moreover, a corrective device is still required to compensate for aberrations in order to obtain high-resolution images. Here we describe a deep self-supervised learning approach for simultaneous aberration estimation and structural information recovery from a single 3D image stack acquired by widefield microscopy. The approach utilizes coordinate-based neural representations to represent highly complex structures. We experimentally validated our approach with directwavefront-sensing-based AO in the same samples and showed the approach is applicable to in vivo mouse brain imaging