Adaptive optics for high-resolution imaging

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems

Significance

Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging).

Aim

This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs.

Approach

Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended.

Conclusion

Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Subretinal fibrosis secondary to neovascular age-related macular degeneration: mechanisms and potential therapeutic targets

Subretinal fibrosis is the end-stage sequelae of neovascular age-related macular degeneration. It causes local damage to photoreceptors, retinal pigment epithelium, and choroidal vessels, which leads to permanent central vision loss of patients with neovascular age-related macular degeneration. The pathogenesis of subretinal fibrosis is complex, and the underlying mechanisms are largely unknown. Therefore, there are no effective treatment options. A thorough understanding of the pathogenesis of subretinal fibrosis and its related mechanisms is important to elucidate its complications and explore potential treatments. The current article reviews several aspects of subretinal fibrosis, including the current understanding on the relationship between neovascular age-related macular degeneration and subretinal fibrosis; multimodal imaging techniques for subretinal fibrosis; animal models for studying subretinal fibrosis; cellular and non-cellular constituents of subretinal fibrosis; pathophysiological mechanisms involved in subretinal fibrosis, such as aging, infiltration of macrophages, different sources of mesenchymal transition to myofibroblast, and activation of complement system and immune cells; and several key molecules and signaling pathways participating in the pathogenesis of subretinal fibrosis, such as vascular endothelial growth factor, connective tissue growth factor, fibroblast growth factor 2, platelet-derived growth factor and platelet-derived growth factor receptor-β, transforming growth factor-β signaling pathway, Wnt signaling pathway, and the axis of heat shock protein 70-Toll-like receptors 2/4-interleukin-10. This review will improve the understanding of the pathogenesis of subretinal fibrosis, allow the discovery of molecular targets, and explore potential treatments for the management of subretinal fibrosis.

Multiphoton fluorescence microscopy for in vivo imaging

Multiphoton fluorescence microscopy (MPFM) has been a game-changer for optical imaging, particularly for studying biological tissues deep within living organisms. MPFM overcomes the strong scattering of light in heterogeneous tissue by utilizing nonlinear excitation that confines fluorescence emission mostly to the microscope focal volume. This enables high-resolution imaging deep within intact tissue and has opened new avenues for structural and functional studies. MPFM has found widespread applications and has led to numerous scientific discoveries and insights into complex biological processes. Today, MPFM is an indispensable tool in many research communities. Its versatility and effectiveness make it a go-to technique for researchers investigating biological phenomena at the cellular and subcellular levels in their native environments. In this Review, the principles, implementations, capabilities, and limitations of MPFM are presented. Three application areas of MPFM, neuroscience, cancer biology, and immunology, are reviewed in detail and serve as examples for applying MPFM to biological research.

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

Sensorless adaptive optics in the second near-infrared window for deep vascular imaging in vivo

We have experimentally validated the use of sensorless adaptive optics (AO) to enhance laser scanning confocal microscopy in the second near-infrared (NIR II) spectral range, termed as AO-NIR II confocal microscopy. This approach harnesses a NIR II fluorophore, excited by an 808 nm wavelength and emitting beyond 1000 nm, to visualize intricate structures in deep brain tissues with the intact skull. By leveraging the reduced scattering and aberrations in the NIR II spectrum, we successfully captured a three-dimensional (3D) vascular structure map extending 310 µm beneath the skull. AO typically boosts the fluorescence signal by approximately 2-3 times, leading to a superior contrast and diminished smearing effects. Consequently, small blood vessels at various depths can be clearly visualized, which might otherwise remain undetectable without AO corrections.

Photon-efficient optical tweezers via wavefront shaping

Optical tweezers enable noncontact trapping of microscale objects using light. It is not known how tightly it is possible to three-dimensionally (3D) trap microparticles with a given photon budget. Reaching this elusive limit would enable maximally stiff particle trapping for precision measurements on the nanoscale and photon-efficient tweezing of light-sensitive objects. Here, we customize the shape of light fields to suit specific particles, with the aim of optimizing trapping stiffness in 3D. We show, theoretically, that the confinement volume of microspheres held in sculpted optical traps can be reduced by one to two orders of magnitude. Experimentally, we use a wavefront shaping-inspired strategy to passively suppress the Brownian fluctuations of microspheres in every direction concurrently, demonstrating order-of-magnitude reductions in their confinement volumes. Our work paves the way toward the fundamental limits of optical control over the mesoscopic realm.

Non-invasive and noise-robust light focusing using confocal wavefront shaping

Wavefront-shaping is a promising approach for imaging fluorescent targets deep inside scattering tissue despite strong aberrations. It enables focusing an incoming illumination into a single spot inside tissue, as well as correcting the outgoing light scattered from the tissue. Previously, wavefront shaping modulations have been successively estimated using feedback from strong fluorescent beads, which have been manually added to a sample. However, such algorithms do not generalize to neurons whose emission is orders of magnitude weaker. We suggest a wavefront shaping approach that works with a confocal modulation of both the illumination and imaging arms. Since the aberrations are corrected in the optics before the detector, the low photon budget is directed into a single sensor spot and detected with high signal-noise ratio. We derive a score function for modulation evaluation from mathematical principles, and successfully use it to image fluorescence neurons, despite scattering through thick tissue.

Closed-loop experiments and brain machine interfaces with multiphoton microscopy

In the field of neuroscience, the importance of constructing closed-loop experimental systems has increased in conjunction with technological advances in measuring and controlling neural activity in live animals. We provide an overview of recent technological advances in the field, focusing on closed-loop experimental systems where multiphoton microscopy-the only method capable of recording and controlling targeted population activity of neurons at a single-cell resolution in vivo-works through real-time feedback. Specifically, we present some examples of brain machine interfaces (BMIs) using in vivo two-photon calcium imaging and discuss applications of two-photon optogenetic stimulation and adaptive optics to real-time BMIs. We also consider conditions for realizing future optical BMIs at the synaptic level, and their possible roles in understanding the computational principles of the brain.

Live-cell imaging powered by computation

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

Learning-based lens wavefront aberration recovery

Wavefront aberration describes the deviation of a wavefront in an imaging system from a desired perfect shape, such as a plane or a sphere, which may be caused by a variety of factors, such as imperfections in optical equipment, atmospheric turbulence, and the physical properties of imaging subjects and medium. Measuring the wavefront aberration of an imaging system is a crucial part of modern optics and optical engineering, with a variety of applications such as adaptive optics, optical testing, microscopy, laser system design, and ophthalmology. While there are dedicated wavefront sensors that aim to measure the phase of light, they often exhibit some drawbacks, such as higher cost and limited spatial resolution compared to regular intensity measurement. In this paper, we introduce a lightweight and practical learning-based method, named LWNet, to recover the wavefront aberration for an imaging system from a single intensity measurement. Specifically, LWNet takes a measured point spread function (PSF) as input and recovers the wavefront aberration with a two-stage network. The first stage network estimates an initial wavefront aberration via supervised learning, and the second stage network further optimizes the wavefront aberration via self-supervised learning by enforcing the statistical priors and physical constraints of wavefront aberrations via Zernike decomposition. For supervised learning, we created a synthetic PSF-wavefront aberration dataset via ray tracing of 88 lenses. Experimental results show that even trained with simulated data, LWNet works well for wavefront aberration estimation of real imaging systems and consistently outperforms prior learning-based methods.

Electrically Reconfigurable Phase-Change Transmissive Metasurface

Programmable and reconfigurable optics hold significant potential for transforming a broad spectrum of applications, spanning space explorations to biomedical imaging, gas sensing, and optical cloaking. The ability to adjust the optical properties of components like filters, lenses, and beam steering devices could result in dramatic reductions in size, weight, and power consumption in future optoelectronic devices. Among the potential candidates for reconfigurable optics, chalcogenide-based phase change materials (PCMs) offer great promise due to their non-volatile and analogue switching characteristics. Although PCM have found widespread use in electronic data storage, these memory devices are deeply sub-micron-sized. To incorporate phase change materials into free-space optical components, it is essential to scale them up to beyond several hundreds of microns while maintaining reliable switching characteristics. This study demonstrated a non-mechanical, non-volatile transmissive filter based on low-loss PCMs with a 200 × 200 µm2 switching area. The device/metafilter can be consistently switched between low- and high-transmission states using electrical pulses with a switching contrast ratio of 5.5 dB. The device was reversibly switched for 1250 cycles before accelerated degradation took place. The work represents an important step toward realizing free-space reconfigurable optics based on PCMs.

Adaptive optical third-harmonic generation microscopy for in vivo imaging of tissues

Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant Brachypodium distachyon. In all instances, aberration correction led to significant enhancements in image quality.

Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography

BACKGROUND

In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput.

METHODS

Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study.

RESULTS

We show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges.

CONCLUSIONS

The results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting.

Adaptive Optics in an Oblique Plane Microscope

Adaptive optics (AO) can restore diffraction limited performance when imaging beyond superficial cell layers in vivo and in vitro, and as such is of interest for advanced 3D microscopy methods such as light-sheet fluorescence microscopy (LSFM). In a typical LSFM system, the illumination and detection paths are separate and subject to different optical aberrations. To achieve optimal microscope performance, it is necessary to sense and correct these aberrations in both light paths, resulting in a complex microscope system. Here, we show that in an oblique plane microscope (OPM), a type of LSFM with a single primary objective lens, the same deformable mirror can correct both the illumination and fluorescence detection. Besides reducing the complexity, we show that AO in OPM also restores the relative alignment of the light-sheet and focal plane, and that a projection imaging mode can stabilize and improve the wavefront correction in a sensorless AO format. We demonstrate OPM with AO on fluorescent nanospheres and by imaging the vasculature and cancer cells in zebrafish embryos embedded in a glass capillary, restoring diffraction limited resolution and improving the signal strength twofold.

Adaptive Optics Retinal Imaging in RDH12-Associated Early Onset Severe Retinal Dystrophy

Purpose

RDH12 is among the most common genes found in individuals with early-onset severe retinal (EOSRD). Adaptive optics scanning light ophthalmoscopy (AOSLO) enables resolution of individual rod and cone photoreceptors in the retina. This study presents the first AOSLO imaging of individuals with RDH12-associated EOSRD.

Methods

Case series of patients who attended Moorfields Eye Hospital (London, UK). Spectral-domain optical coherence tomography, near-infrared reflectance (NIR), and blue autofluorescence imaging were analyzed. En face image sequences of photoreceptors were recorded using either of two AOSLO modalities. Cross-sectional analysis was undertaken for seven patients and longitudinal analysis for one patient.

Results

Nine eyes from eight patients are presented in this case series. The mean age at the time of the assessment was 11.2 ± 6.5 years of age (range 7-29). A subfoveal continuous ellipsoid zone (EZ) line was present in eight eyes. Posterior pole AOSLO revealed patches of cone mosaics. Average cone densities at regions of interest 0.5° to the fovea ranged from 12,620 to 23,660 cells/mm2, whereas intercell spacing ranged from 7.0 to 9.7 µm.

Conclusions

This study demonstrates that AOSLO can provide useful high-quality images in patients with EOSRD, even during childhood, with nystagmus, and early macular atrophy. Cones at the posterior pole can appear as scattered islands or, possibly later in life, as a single subfoveal conglomerate. Detailed image analysis suggests that retinal pigment epithelial stress and dysfunction may be the initial step toward degeneration, with NIR being a useful tool to assess retinal well-being in RDH12-associated EOSRD.

Automatic MTF Conversion between Different Characteristics Caused by Imaging Devices

Depending on various design conditions, including optics and circuit design, the image-forming characteristics of the modulated transfer function (MTF), which affect the spatial resolution of a digital image, may vary among image channels within or between imaging devices. In this study, we propose a method for automatically converting the MTF to the target MTF, focusing on adjusting the MTF characteristics that affect the signals of different image channels within and between different image devices. The experimental results of MTF conversion using the proposed method for multiple image channels with different MTF characteristics indicated that the proposed method could produce sharper images by moving the source MTF of each channel closer to a target MTF with a higher MTF value. This study is expected to contribute to technological advancements in various imaging devices as follows: (1) Even if the imaging characteristics of the hardware are unknown, the MTF can be converted to the target MTF using the image after it is captured. (2) As any MTF can be converted into a target, image simulation for conversion to a different MTF is possible. (3) It is possible to generate high-definition images, thereby meeting the requirements of various industrial and research fields in which high-definition images are required.

Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed computational miniature mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

Daytime HyWFS approach for daylight adaptive optics wavefront sensing

Bright daylight photon noise and the saturation of wavefront sensors pose challenges to high-resolution daytime imaging. In this paper, a daytime hybrid wavefront sensor (HyWFS) approach for real-time wavefront sensing in daylight adaptive optics (AO) is described. The Shack-Hartmann wavefront sensor (SHWFS) algorithm is used to efficiently compensate large-scale wavefronts, while the pyramid wavefront sensor (PyWFS) algorithm offers highly sensitive correction of small wavefronts. Daylight closed-loop AO experiments were performed using the daytime HyWFS approach with both algorithms, respectively. The experiment results indicate that the proposed approach provides accurate daylight AO correction and allows for a simple switch between the two algorithms without increasing system complexity. The daytime HyWFS approach can serve as an alternative for daylight natural guide star AO, enabling high-resolution observation of resident space objects no longer limited to dawn and dusk.

Multifunctional adaptive optics optical coherence tomography allows cellular scale reflectometry, polarimetry, and angiography in the living human eye

Clinicians are unable to detect glaucoma until substantial loss or dysfunction of retinal ganglion cells occurs. To this end, novel measures are needed. We have developed an optical imaging solution based on adaptive optics optical coherence tomography (AO-OCT) to discern key clinical features of glaucoma and other neurodegenerative diseases at the cellular scale in the living eye. Here, we test the feasibility of measuring AO-OCT-based reflectance, retardance, optic axis orientation, and angiogram at specifically targeted locations in the living human retina and optic nerve head. Multifunctional imaging, combined with focus stacking and global image registration algorithms, allows us to visualize cellular details of retinal nerve fiber bundles, ganglion cell layer somas, glial septa, superior vascular complex capillaries, and connective tissues. These are key histologic features of neurodegenerative diseases, including glaucoma, that are now measurable in vivo with excellent repeatability and reproducibility. Incorporating this noninvasive cellular-scale imaging with objective measurements will significantly enhance existing clinical assessments, which is pivotal in facilitating the early detection of eye disease and understanding the mechanisms of neurodegeneration.

Reference-free in situ rapid regional calibration of phase-only spatial light modulators

Spatial light modulators (SLMs) have become an indispensable element in modern optics for their versatile performance in many applications. Among various types of SLMs, such as digital micromirror devices (DMD), liquid crystal-based phase-only spatial light modulators (LC-SLMs), and deformable mirrors (DM), LC-SLMs are often the method of choice due to their high efficiency, precise phase modulation, and abundant number of effective pixels. In general, for research grade applications, an additional SLM calibration step is required due to fabrication imperfection resulting in non-flat liquid crystal panels and varying phase responses over the SLM area. Here, we demonstrate a straightforward approach for reference-free orthogonal calibration of an arbitrary number of SLM subregions which only requires the same measurement time as global calibration. The proposed method requires minimal optical elements and can be applied to any optical setup as is. As a benchmark performance test, we achieved a 2.2-fold enhancement in correction efficiency for wavefront shaping through scattering media utilizing the calibrated 2160 subregions of the SLM, in comparison with a single global look-up table (LUT).

Dynamically Reconfigurable on-Chip Polarimeters Based on Nanoantenna Enabled Polarization Dependent Optoelectronic Computing

On-chip polarization detectors have attracted extensive research interest due to their filterless and ultracompact architecture. However, their polarization-dependent photoresponses cannot be dynamically adjusted, hindering the development toward intelligence. Here, we propose dynamically reconfigurable polarimetry based on in-sensor differentiation of two self-powered photoresponses with orthogonal polarization dependences and tunable responsivities. Such a device can be electrostatically configured in an ultrahigh polarization extinction ratio (PER) mode, where the PER tends to infinity, a Stokes parameter direct sensing mode, where the photoresponse is proportional to S1 or S2 with high accuracy (RMSES1 = 1.5%, RMSES2 = 2.0%), or a background suppressing mode, where the target-background polarization contrast is singularly enhanced. Moreover, the device achieves a polarization angle sensitivity of 0.51 mA·W-1·degree-1 and a specific polarization angle detectivity of 2.8 × 105 cm·Hz1/2·W·degree-1. This scheme is demonstrated throughout the near-to-long-wavelength infrared range, and it will bring a leap for next-generation on-chip polarimeters.

Single-shot digital optical fluorescence phase conjugation through forward multiple-scattering samples

Aberrations and multiple scattering in biological tissues critically distort light beams into highly complex speckle patterns. In this regard, digital optical phase conjugation (DOPC) is a promising technique enabling in-depth focusing. However, DOPC becomes challenging when using fluorescent guide stars for four main reasons: the low photon budget available, the large spectral bandwidth of the fluorescent signal, the Stokes shift between the emission and the excitation wavelength, and the absence of reference beam preventing holographic measurement. Here, we demonstrate the possibility to focus a laser beam through multiple-scattering samples by measuring speckle fields in a single acquisition step with a reference-free, high-resolution wavefront sensor. By taking advantage of the large spectral bandwidth of forward multiply scattering samples, digital fluorescence phase conjugation is achieved to focus a laser beam at the excitation wavelength while measuring the broadband speckle field arising from a micrometer-sized fluorescent bead.

Imaging actin organisation and dynamics in 3D

The actin cytoskeleton plays a critical role in cell architecture and the control of fundamental processes including cell division, migration and survival. The dynamics and organisation of F-actin have been widely studied in a breadth of cell types on classical two-dimensional (2D) surfaces. Recent advances in optical microscopy have enabled interrogation of these cytoskeletal networks in cells within three-dimensional (3D) scaffolds, tissues and in vivo. Emerging studies indicate that the dimensionality experienced by cells has a profound impact on the structure and function of the cytoskeleton, with cells in 3D environments exhibiting cytoskeletal arrangements that differ to cells in 2D environments. However, the addition of a third (and fourth, with time) dimension leads to challenges in sample preparation, imaging and analysis, necessitating additional considerations to achieve the required signal-to-noise ratio and spatial and temporal resolution. Here, we summarise the current tools for imaging actin in a 3D context and highlight examples of the importance of this in understanding cytoskeletal biology and the challenges and opportunities in this domain.

Accurate piecewise centroid calculation algorithm for wavefront measurement in adaptive optics

Adaptive optics using direct wavefront sensing (direct AO) is widely used in two-photon microscopy to correct sample-induced aberrations and restore diffraction-limited performance at high speeds. In general, the direct AO method employs a Sharked-Hartman wavefront sensor (SHWS) to directly measure the aberrations through a spot array. However, the signal-to-noise ratio (SNR) of spots in SHWS varies significantly within deep tissues, presenting challenges for accurately locating spot centroids over a large SNR range, particularly under extremely low SNR conditions. To address this issue, we propose a piecewise centroid calculation algorithm called GCP, which integrates three optimal algorithms for accurate spot centroid calculations under high-, medium-, and low-SNR conditions. Simulations and experiments demonstrate that the GCP can accurately measure aberrations over a large SNR range and exhibits robustness under extremely low-SNR conditions. Importantly, GCP improves the AO working depth by 150 µm compared to the conventional algorithm.

Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed Computational Miniature Mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

Measurement precision bounds on aberrated single molecule emission patterns

Single-Molecule Localization Microscopy (SMLM) has revolutionized the study of biological phenomena by providing exquisite nanoscale spatial resolution. However, optical aberrations induced by sample and system imperfections distort the single molecule emission patterns (i.e. PSFs), leading to reduced precision and resolution of SMLM, particularly in three-dimensional (3D) applications. While various methods, both analytical and instrumental, have been employed to mitigate these aberrations, a comprehensive analysis of how different types of commonly encountered aberrations affect single molecule experiments and their image formation remains missing. In this study, we addressed this gap by conducting a quantitative study of the theoretical precision limit for position and wavefront distortion measurements in the presence of aberrations. Leveraging Fisher information and Cramér-Rao lower bound (CRLB), we quantitively analyzed and compared the effects of different aberration types, including index mismatch aberrations, on localization precision in both biplane and astigmatism 3D modalities as well as 2D SMLM imaging. Furthermore, we studied the achievable wavefront estimation precision from aberrated single molecule emission patterns, a pivot step for successful adaptive optics in SMLM through thick specimens. This analysis lays a quantitative foundation for the development and application of SMLM in whole-cells, tissues and with large field of view, providing in-depth insights into the behavior of different aberration types in single molecule imaging and thus generating theoretical guidelines for developing highly efficient aberration correction strategies and enhancing the precision and reliability of 3D SMLM.

Tandem aberration correction optics (TACO) in wide-field structured illumination microscopy

Structured illumination microscopy (SIM) is a powerful super-resolution imaging technique that uses patterned illumination to down-modulate high spatial-frequency information of samples. However, the presence of spatially-dependent aberrations can severely disrupt the illumination pattern, limiting the quality of SIM imaging. Conventional adaptive optics (AO) techniques that employ wavefront correctors at the pupil plane are not capable of effectively correcting these spatially-dependent aberrations. We introduce the Tandem Aberration Correction Optics (TACO) approach that combines both pupil AO and conjugate AO for aberration correction in SIM. TACO incorporates a deformable mirror (DM) for pupil AO in the detection path to correct for global aberrations, while a spatial light modulator (SLM) is placed at the plane conjugate to the aberration source near the sample plane, termed conjugate AO, to compensate spatially-varying aberrations in the illumination path. Our numerical simulations and experimental results show that the TACO approach can recover the illumination pattern close to an ideal condition, even when severely misshaped by aberrations, resulting in high-quality super-resolution SIM reconstruction. The TACO approach resolves a critical traditional shortcoming of aberration correction for structured illumination. This advance significantly expands the application of SIM imaging in the study of complex, particularly biological, samples and should be effective in other wide-field microscopies.

Construction and use of an adaptive optics two-photon microscope with direct wavefront sensing

Two-photon microscopy, combined with the appropriate optical labelling, enables the measurement and tracking of submicrometer structures within brain cells, as well as the spatiotemporal mapping of spikes in individual neurons and of neurotransmitter release in individual synapses. Yet, the spatial resolution of two-photon microscopy rapidly degrades as imaging is attempted at depths of more than a few scattering lengths into tissue, i.e., below the superficial layers that constitute the top 300-400 µm of the neocortex. To obviate this limitation, we shape the focal volume, generated by the excitation beam, by modulating the incident wavefront via guidestar-assisted adaptive optics. Here, we describe the construction, calibration and operation of a two-photon microscope that incorporates adaptive optics to restore diffraction-limited resolution at depths close to 900 µm in the mouse cortex. Our setup detects a guidestar formed by the excitation of a red-shifted dye in blood serum, used to directly measure the wavefront. We incorporate predominantly commercially available optical, optomechanical, mechanical and electronic components, and supply computer-aided design models of other customized components. The resulting adaptive optics two-photon microscope is modular and allows for expanded imaging and optical excitation capabilities. We demonstrate our methodology in the mouse neocortex by imaging the morphology of somatostatin-expressing neurons that lie 700 µm beneath the pia, calcium dynamics of layer 5b projection neurons and thalamocortical glutamate transmission to L4 neurons. The protocol requires ~30 d to complete and is suitable for users with graduate-level expertise in optics.

Universal adaptive optics for microscopy through embedded neural network control

The resolution and contrast of microscope imaging is often affected by aberrations introduced by imperfect optical systems and inhomogeneous refractive structures in specimens. Adaptive optics (AO) compensates these aberrations and restores diffraction limited performance. A wide range of AO solutions have been introduced, often tailored to a specific microscope type or application. Until now, a universal AO solution - one that can be readily transferred between microscope modalities - has not been deployed. We propose versatile and fast aberration correction using a physics-based machine learning assisted wavefront-sensorless AO control (MLAO) method. Unlike previous ML methods, we used a specially constructed neural network (NN) architecture, designed using physical understanding of the general microscope image formation, that was embedded in the control loop of different microscope systems. The approach means that not only is the resulting NN orders of magnitude simpler than previous NN methods, but the concept is translatable across microscope modalities. We demonstrated the method on a two-photon, a three-photon and a widefield three-dimensional (3D) structured illumination microscope. Results showed that the method outperformed commonly-used modal-based sensorless AO methods. We also showed that our ML-based method was robust in a range of challenging imaging conditions, such as 3D sample structures, specimen motion, low signal to noise ratio and activity-induced fluorescence fluctuations. Moreover, as the bespoke architecture encapsulated physical understanding of the imaging process, the internal NN configuration was no-longer a "black box", but provided physical insights on internal workings, which could influence future designs.

Self-Modulated Ghost Imaging in Dynamic Scattering Media

In this paper, self-modulated ghost imaging (SMGI) in a surrounded scattering medium is proposed. Different from traditional ghost imaging, SMGI can take advantage of the dynamic scattering medium that originally affects the imaging quality and generate pseudo-thermal light through the dynamic scattering of free particles' Brownian motion in the scattering environment for imaging. Theoretical analysis and simulation were used to establish the relationship between imaging quality and particle concentration. An experimental setup was also built to verify the feasibility of the SMGI. Compared with the reconstructed image quality and evaluation indexes of traditional ghost imaging, SMGI has better image quality, which demonstrates a promising future in dynamic high-scattering media such as dense fog and turbid water.

Enhanced-resolution Shack-Hartmann wavefront sensing for extended objects

Adaptive optics systems for large-aperture solar telescopes, especially multiconjugate adaptive optics systems, suffer from a fundamental trade-off between wavefront sampling rate and sub-aperture resolution. We introduce an enhanced-resolution Shack-Hartmann wavefront sensing method that decouples sub-aperture resolution from the desired wavefront sampling rate. We experimentally verified the validity of this method. Results show that by synthesizing multiple low-spatial samplings, this method is capable to sense higher-frequency aberrations beyond any low-spatial sampling involved in the synthesis, and it allows higher sub-aperture resolution and higher operating bandwidths, which can better fulfill the needs of solar adaptive optics.

Deep learning-driven adaptive optics for single-molecule localization microscopy

The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-µm-thick brain tissue specimens.

Direct wavefront reconstruction with the cone wavefront sensor using the inverse radon transform

The cone wavefront sensor consists of a cone (or axicon) placed at the focal plane of the imaging system, from which an annular intensity image is formed. Typically, the wavefront phase is estimated using inversion of an interaction matrix relating the intensity image to different aberration modes. In this paper, we show that the intensity image for the cone wavefront sensor is related to the radon transform of the wavefront phase. A reconstruction method using the inverse radon transform (filtered back-projection) is shown to be able to directly approximate the wavefront phase without the need for an interaction matrix for small wavefront aberrations.

Laser-Written Tunable Liquid Crystal Aberration Correctors

In this Article, we present a series of novel laser-written liquid crystal (LC) devices for aberration control for applications in beam shaping or aberration correction through adaptive optics. Each transparent LC device can correct for a chosen aberration mode with continuous greyscale tuning up to a total magnitude of more than 2π radians phase difference peak to peak at a wavelength of λ = 660 nm. For the purpose of demonstration, we present five different devices for the correction of five independent Zernike polynomial modes (although the technique could readily be used to manufacture devices based on other modes). Each device is operated by a single electrode pair tuned between 0 and 10 V. These devices have potential as a low-cost alternative to spatial light modulators for applications where a low-order aberration correction is sufficient and transmissive geometries are required.

Controlling the transmission of broadband light through scattering media using a digital micromirror device

Wavefront shaping has emerged as a valuable technique in complex photonics, wherein the various eigenmodes of the disordered medium are selectively excited to control the overall transmission through the medium. The process utilizes active optical devices such as liquid crystal-based spatial light modulators (LC-SLM), deformable mirrors (DM), and digital micromirror devices (DMD). Among these, the latter is preferred for imaging through dynamic scattering media such as living biological tissues due to their high-speed refresh rate and increased resolution. This study employs a genetic algorithm along with binary amplitude modulation generated by a digital micromirror device to spatially and spectrally control the large spectral bandwidth through a scattering medium. We illustrate spatial single-point focusing of broadband light, multipoint focusing of broadband light, and programmable spectral filtering of the same through disordered samples.

NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media

Diffraction-limited optical imaging through scattering media has the potential to transform many applications such as airborne and space-based imaging (through the atmosphere), bioimaging (through skin and human tissue), and fiber-based imaging (through fiber bundles). Existing wavefront shaping methods can image through scattering media and other obscurants by optically correcting wavefront aberrations using high-resolution spatial light modulators-but these methods generally require (i) guidestars, (ii) controlled illumination, (iii) point scanning, and/or (iv) statics scenes and aberrations. We propose neural wavefront shaping (NeuWS), a scanning-free wavefront shaping technique that integrates maximum likelihood estimation, measurement modulation, and neural signal representations to reconstruct diffraction-limited images through strong static and dynamic scattering media without guidestars, sparse targets, controlled illumination, nor specialized image sensors. We experimentally demonstrate guidestar-free, wide field-of-view, high-resolution, diffraction-limited imaging of extended, nonsparse, and static/dynamic scenes captured through static/dynamic aberrations.

Metasurface-enabled polarization-independent LCoS spatial light modulator for 4K resolution and beyond

With the distinct advantages of high resolution, small pixel size, and multi-level pure phase modulation, liquid crystal on silicon (LCoS) devices afford precise and reconfigurable spatial light modulation that enables versatile applications ranging from micro-displays to optical communications. However, LCoS devices suffer from a long-standing problem of polarization-dependent response in that they only perform phase modulation on one linear polarization of light, and polarization-independent phase modulation-essential for most applications-have had to use complicated polarization-diversity optics. We propose and demonstrate, for the first time, an LCoS device that directly achieves high-performance polarization-independent phase modulation at telecommunication wavelengths with 4K resolution and beyond by embedding a polarization-rotating metasurface between the LCoS backplane and the liquid crystal phase-modulating layer. We verify the device with a number of typical polarization-independent application functions including beam steering, holographical display, and in a key optical switching element - wavelength selective switch (WSS), demonstrating the significant benefits in terms of both configuration simplification and performance improvement.

Light-sheets and smart microscopy, an exciting future is dawning

Light-sheet fluorescence microscopy has transformed our ability to visualize and quantitatively measure biological processes rapidly and over long time periods. In this review, we discuss current and future developments in light-sheet fluorescence microscopy that we expect to further expand its capabilities. This includes smart and adaptive imaging schemes to overcome traditional imaging trade-offs, i.e., spatiotemporal resolution, field of view and sample health. In smart microscopy, a microscope will autonomously decide where, when, what and how to image. We further assess how image restoration techniques provide avenues to overcome these tradeoffs and how "open top" light-sheet microscopes may enable multi-modal imaging with high throughput. As such, we predict that light-sheet microscopy will fulfill an important role in biomedical and clinical imaging in the future.

Two-photon synthetic aperture microscopy for minimally invasive fast 3D imaging of native subcellular behaviors in deep tissue

Holistic understanding of physio-pathological processes requires noninvasive 3D imaging in deep tissue across multiple spatial and temporal scales to link diverse transient subcellular behaviors with long-term physiogenesis. Despite broad applications of two-photon microscopy (TPM), there remains an inevitable tradeoff among spatiotemporal resolution, imaging volumes, and durations due to the point-scanning scheme, accumulated phototoxicity, and optical aberrations. Here, we harnessed the concept of synthetic aperture radar in TPM to achieve aberration-corrected 3D imaging of subcellular dynamics at a millisecond scale for over 100,000 large volumes in deep tissue, with three orders of magnitude reduction in photobleaching. With its advantages, we identified direct intercellular communications through migrasome generation following traumatic brain injury, visualized the formation process of germinal center in the mouse lymph node, and characterized heterogeneous cellular states in the mouse visual cortex, opening up a horizon for intravital imaging to understand the organizations and functions of biological systems at a holistic level.

Modeling nonlinear optical microscopy in scattering media, part I. Propagation from lens to focal volume: tutorial

The development and application of nonlinear optical (NLO) microscopy methods in biomedical research have experienced rapid growth over the past three decades. Despite the compelling power of these methods, optical scattering limits their practical use in biological tissues. This tutorial offers a model-based approach illustrating how analytical methods from classical electromagnetism can be employed to comprehensively model NLO microscopy in scattering media. In Part I, we quantitatively model focused beam propagation in non-scattering and scattering media from the lens to focal volume. In Part II, we model signal generation, radiation, and far-field detection. Moreover, we detail modeling approaches for major optical microscopy modalities including classical fluorescence, multi-photon fluorescence, second harmonic generation, and coherent anti-Stokes Raman microscopy.

Retinal microvascular and neuronal pathologies probed in vivo by adaptive optical two-photon fluorescence microscopy

The retina, behind the transparent optics of the eye, is the only neural tissue whose physiology and pathology can be non-invasively probed by optical microscopy. The aberrations intrinsic to the mouse eye, however, prevent high-resolution investigation of retinal structure and function in vivo. Optimizing the design of a two-photon fluorescence microscope (2PFM) and sample preparation procedure, we found that adaptive optics (AO), by measuring and correcting ocular aberrations, is essential for resolving putative synaptic structures and achieving three-dimensional cellular resolution in the mouse retina in vivo. Applying AO-2PFM to longitudinal retinal imaging in transgenic models of retinal pathology, we characterized microvascular lesions with sub-capillary details in a proliferative vascular retinopathy model, and found Lidocaine to effectively suppress retinal ganglion cell hyperactivity in a retinal degeneration model. Tracking structural and functional changes at high-resolution longitudinally, AO-2PFM enables microscopic investigations of retinal pathology and pharmacology for disease diagnosis and treatment in vivo.

Adaptive optics for optical microscopy [Invited]

Optical microscopy is widely used to visualize fine structures. When applied to bioimaging, its performance is often degraded by sample-induced aberrations. In recent years, adaptive optics (AO), originally developed to correct for atmosphere-associated aberrations, has been applied to a wide range of microscopy modalities, enabling high- or super-resolution imaging of biological structure and function in complex tissues. Here, we review classic and recently developed AO techniques and their applications in optical microscopy.

Realizing the Full Potential of Advanced Microscopy Approaches for Interrogating Plant-Microbe Interactions

Microscopy has served as a fundamental tool for insight and discovery in plant-microbe interactions for centuries. From classical light and electron microscopy to corresponding specialized methods for sample preparation and cellular contrasting agents, these approaches have become routine components in the toolkit of plant and microbiology scientists alike to visualize, probe and understand the nature of host-microbe relationships. Over the last three decades, three-dimensional perspectives led by the development of electron tomography, and especially, confocal techniques continue to provide remarkable clarity and spatial detail of tissue and cellular phenomena. Confocal and electron microscopy provide novel revelations that are now commonplace in medium and large institutions. However, many other cutting-edge technologies and sample preparation workflows are relatively unexploited yet offer tremendous potential for unprecedented advancement in our understanding of the inner workings of pathogenic, beneficial, and symbiotic plant-microbe interactions. Here, we highlight key applications, benefits, and challenges of contemporary advanced imaging platforms for plant-microbe systems with special emphasis on several recently developed approaches, such as light-sheet, single molecule, super-resolution, and adaptive optics microscopy, as well as ambient and cryo-volume electron microscopy, X-ray microscopy, and cryo-electron tomography. Furthermore, the potential for complementary sample preparation methodologies, such as optical clearing, expansion microscopy, and multiplex imaging, will be reviewed. Our ultimate goal is to stimulate awareness of these powerful cutting-edge technologies and facilitate their appropriate application and adoption to solve important and unresolved biological questions in the field. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Optofluidic imaging meets deep learning: from merging to emerging

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

Nonlinear field-control of terahertz waves in random media for spatiotemporal focusing

Controlling the transmission of broadband optical pulses in scattering media is a critical open challenge in photonics. To date, wavefront shaping techniques at optical frequencies have been successfully applied to control the spatial properties of multiple-scattered light. However, a fundamental restriction in achieving an equivalent degree of control over the temporal properties of a broadband pulse is the limited availability of experimental techniques to detect the coherent properties (i.e., the spectral amplitude and absolute phase) of the transmitted field. Terahertz experimental frameworks, on the contrary, enable measuring the field dynamics of broadband pulses at ultrafast (sub-cycle) time scales directly. In this work, we provide a theoretical/numerical demonstration that, within this context, complex scattering can be used to achieve spatio-temporal control of instantaneous fields and manipulate the temporal properties of single-cycle pulses by solely acting on spatial degrees of freedom of the illuminating field. As direct application scenarios, we demonstrate spatio-temporal focusing, chirp compensation, and control of the carrier-envelope-phase (CEP) of a CP-stable, transform-limited THz pulse.

Nonlinear field-control of terahertz waves in random media for spatiotemporal focusing

Controlling the transmission of broadband optical pulses in scattering media is a critical open challenge in photonics. To date, wavefront shaping techniques at optical frequencies have been successfully applied to control the spatial properties of multiple-scattered light. However, a fundamental restriction in achieving an equivalent degree of control over the temporal properties of a broadband pulse is the limited availability of experimental techniques to detect the coherent properties (i.e., the spectral amplitude and absolute phase) of the transmitted field. Terahertz experimental frameworks, on the contrary, enable measuring the field dynamics of broadband pulses at ultrafast (sub-cycle) time scales directly. In this work, we provide a theoretical/numerical demonstration that, within this context, complex scattering can be used to achieve spatio-temporal control of instantaneous fields and manipulate the temporal properties of single-cycle pulses by solely acting on spatial degrees of freedom of the illuminating field. As direct application scenarios, we demonstrate spatio-temporal focusing, chirp compensation, and control of the carrier-envelope-phase (CEP) of a CP-stable, transform-limited THz pulse.

Guide to the construction and use of an adaptive optics two-photon microscope with direct wavefront sensing

Two-photon microscopy, combined with appropriate optical labeling, has enabled the study of structure and function throughout nervous systems. This methodology enables, for example, the measurement and tracking of sub-micrometer structures within brain cells, the spatio-temporal mapping of spikes in individual neurons, and the spatio-temporal mapping of transmitter release in individual synapses. Yet the spatial resolution of two-photon microscopy rapidly degrades as imaging is attempted at depths more than a few scattering lengths into tissue, i.e., below the superficial layers that constitute the top 300 to 400 µm of neocortex. To obviate this limitation, we measure the wavefront at the focus of the excitation beam and utilize adaptive optics that alters the incident wavefront to achieve an improved focal volume. We describe the constructions, calibration, and operation of a two-photon microscopy that incorporates adaptive optics to restore diffraction-limited resolution throughout the nearly 900 µm depth of mouse cortex. Our realization utilizes a guide star formed by excitation of red-shifted dye within the blood serum to directly measure the wavefront. We incorporate predominantly commercial optical, optomechanical, mechanical, and electronic components; computer aided design models of the exceptional custom components are supplied. The design is modular and allows for expanded imaging and optical excitation capabilities. We demonstrate our methodology in mouse neocortex by imaging the morphology of somatostatin-expressing neurons at 700 µm beneath the pia, calcium dynamics of layer 5b projection neurons, and glutamate transmission to L4 neurons.

Contribution of Intravital Neuroimaging to Study Animal Models of Multiple Sclerosis

Multiple sclerosis (MS) is a complex and long-lasting neurodegenerative disease of the central nervous system (CNS), characterized by the loss of myelin within the white matter and cortical fibers, axonopathy, and inflammatory responses leading to consequent sensory-motor and cognitive deficits of patients. While complete resolution of the disease is not yet a reality, partial tissue repair has been observed in patients which offers hope for therapeutic strategies. To address the molecular and cellular events of the pathomechanisms, a variety of animal models have been developed to investigate distinct aspects of MS disease. Recent advances of multiscale intravital imaging facilitated the direct in vivo analysis of MS in the animal models with perspective of clinical transfer to patients. This review gives an overview of MS animal models, focusing on the current imaging modalities at the microscopic and macroscopic levels and emphasizing the importance of multimodal approaches to improve our understanding of the disease and minimize the use of animals.

Practical considerations for quantitative light sheet fluorescence microscopy

Fluorescence microscopy has evolved from a purely observational tool to a platform for quantitative, hypothesis-driven research. As such, the demand for faster and less phototoxic imaging modalities has spurred a rapid growth in light sheet fluorescence microscopy (LSFM). By restricting the excitation to a thin plane, LSFM reduces the overall light dose to a specimen while simultaneously improving image contrast. However, the defining characteristics of light sheet microscopes subsequently warrant unique considerations in their use for quantitative experiments. In this Perspective, we outline many of the pitfalls in LSFM that can compromise analysis and confound interpretation. Moreover, we offer guidance in addressing these caveats when possible. In doing so, we hope to provide a useful resource for life scientists seeking to adopt LSFM to quantitatively address complex biological hypotheses.

Computational adaptive holographic fluorescence microscopy based on the stochastic parallel gradient descent algorithm

Optical aberrations introduced by sample or system elements usually degrade the image quality of a microscopic imaging system. Computational adaptive optics has unique advantages for 3D biological imaging since neither bulky wavefront sensors nor complicated indirect wavefront sensing procedures are required. In this paper, a stochastic parallel gradient descent computational adaptive optics method is proposed for high-efficiency aberration correction in the fluorescent incoherent digital holographic microscope. The proposed algorithm possesses the advantage of parallelly estimating various aberrations with fast convergence during the iteration; thus, the wavefront aberration is corrected quickly, and the original object image is retrieved accurately. Owing to its high-efficiency adaptive optimization, the proposed method exhibits better performances for a 3D sample with complex and anisotropic optical aberration. The proposed method can be a powerful tool for the visualization of dynamic events that happen inside cells or thick tissues.