Adaptive optics stochastic optical reconstruction microscopy (AO-STORM) by particle swarm optimization

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.

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.

Marketing Archive Management of Drama Intangible Cultural Heritage Based on Particle Swarm Algorithm

Each Chinese region has its own ancient opera, which is a treasure of folk culture and a living fossil for studying the historical origins of a local culture. It has significant academic value and historical significance at the national and local levels, whether from the perspective of promoting and disseminating national culture or from the perspective of protecting the world’s intangible cultural heritage. Based on this practical significance, this paper conducts a study using the particle swarm algorithm to manage the marketing archives of drama intangible cultural heritage. The article employs the PSO algorithm to test the particle swarm optimization algorithm’s convergence and conditions. The effectiveness of the algorithm is analysed in the Sphere function, Rosenbrock function, Griewanks function, and Rastrigin noncont function, and then the algorithm is compared, including the calculation speed comparison between the algorithm in this paper and the three optimal fitness functions. The experimental results show that the PSO algorithm has the highest four items in the statistics of the Schwefel function experimental results. About 45.0379 is the best value and 70.5878 is the maximum precision. The optimal average value is 6.1524, while the average value is 56.15245. In comparison to the QPSO and PSO algorithms, the algorithm in this paper has a faster convergence speed and better search accuracy. The topic of the intersection of the disciplines of drama, intangible cultural heritage marketing, and archive management using the particle swarm algorithm is well-developed.

Active Aberration Correction with Adaptive Coefficient SPGD Algorithm for Laser Scanning Confocal Microscope

A laser scanning confocal microscope (LSCM) is an effective scientific instrument for studying sub-micron structures, and it has been widely used in the field of biological detection. However, the illumination depth of LSCMs is limited due to the optical aberrations introduced by living biological tissue, which acts as an optical medium with a non-uniform refractive index, resulting in a significant dispersion of the focus of LSCM illumination light and, hence, a loss in the resolution of the image. In this study, to minimize the effect of optical aberrations, an image-based adaptive optics technology using an optimized stochastic parallel gradient descent (SPGD) algorithm with an adaptive coefficient is applied to the optical path of an LSCM system. The effectiveness of the proposed aberration correction approach is experimentally evaluated in the LSCM system. The results illustrate that the proposed adaptive optics system with an adaptive coefficient SPGD algorithm can effectively reduce the interference caused by aberrations during depth imaging.

Adaptive optics in super-resolution microscopy

Fluorescence microscopy has become a routine tool in biology for interrogating life activities with minimal perturbation. While the resolution of fluorescence microscopy is in theory governed only by the diffraction of light, the resolution obtainable in practice is also constrained by the presence of optical aberrations. The past two decades have witnessed the advent of super-resolution microscopy that overcomes the diffraction barrier, enabling numerous biological investigations at the nanoscale. Adaptive optics, a technique borrowed from astronomical imaging, has been applied to correct for optical aberrations in essentially every microscopy modality, especially in super-resolution microscopy in the last decade, to restore optimal image quality and resolution. In this review, we briefly introduce the fundamental concepts of adaptive optics and the operating principles of the major super-resolution imaging techniques. We highlight some recent implementations and advances in adaptive optics for active and dynamic aberration correction in super-resolution microscopy.

Robust adaptive optics for localization microscopy deep in complex tissue

Single-Molecule Localization Microscopy (SMLM) provides the ability to determine molecular organizations in cells at nanoscale resolution, but in complex biological tissues, where sample-induced aberrations hamper detection and localization, its application remains a challenge. Various adaptive optics approaches have been proposed to overcome these issues, but the exact performance of these methods has not been consistently established. Here we systematically compare the performance of existing methods using both simulations and experiments with standardized samples and find that they often provide limited correction or even introduce additional errors. Careful analysis of the reasons that underlie this limited success enabled us to develop an improved method, termed REALM (Robust and Effective Adaptive Optics in Localization Microscopy), which corrects aberrations of up to 1 rad RMS using 297 frames of blinking molecules to improve single-molecule localization. After its quantitative validation, we demonstrate that REALM enables to resolve the periodic organization of cytoskeletal spectrin of the axon initial segment even at 50 μm depth in brain tissue.

In situ measurement of the isoplanatic patch for imaging through intact bone

Wavefront-shaping (WS) enables imaging through scattering tissues like bone, which is important for neuroscience and bone-regeneration research. WS corrects for the optical aberrations at a given depth and field-of-view (FOV) within the sample; the extent of the validity of which is limited to a region known as the isoplanatic patch (IP). Knowing this parameter helps to estimate the number of corrections needed for WS imaging over a given FOV. In this paper, we first present direct transmissive measurement of murine skull IP using digital optical phase conjugation based focusing. Second, we extend our previously reported phase accumulation ray tracing (PART) method to provide in-situ in-silico estimation of IP, called correlative PART (cPART). Our results show an IP range of 1 to 3 μm for mice within an age range of 8 to 14 days old and 1.00 ± 0.25 μm in a 12-week old adult skull. Consistency between the two measurement approaches indicates that cPART can be used to approximate the IP before a WS experiment, which can be used to calculate the number of corrections required within a given field of view.

Spatial frequency metrics for analysis of microscopic images of musculoskeletal tissues

Purpose: Imaging-based metrics for analysis of biological tissues are powerful tools that can extract information such as shape, size, periodicity, and many other features to assess the requested qualities of a tissue. Muscular and osseous tissues consist of periodic structures that are directly related to their function, and so analysis of these patterns likely reflects tissue health and regeneration.Methods: A method for assessment of periodic structures is by analyzing them in the spatial frequency domain using the Fourier transform. In this paper, we present two filters which we developed in the spatial frequency domain for the purpose of analyzing musculoskeletal structures. These filters provide information about 1) the angular orientation of the tissues and 2) their periodicity. We explore periodic structural patterns in the mitochondrial network of skeletal muscles that are reflective of muscle metabolism and myogenesis; and patterns of collagen fibers in the bone that are reflective of the organization and health of bone extracellular matrix.Results: We present an analysis of mouse skeletal muscle in healthy and injured muscles. We used a transgenic mouse that ubiquitously expresses fluorescent protein in their mitochondria and performed 2-photon microscopy to image the structures. To acquire the collagen structure of the bone we used non-linear SHG microscopy of mouse flat bone. We analyze and compare juvenile versus adult mice, which have different structural patterns.Conclusions: Our results indicate that these metrics can quantify musculoskeletal tissues during development and regeneration.

Phase diversity method based on an improved particle swarm algorithm used in co-phasing error detection

The phase diversity (PD) algorithm is an image-based co-phasing error detection method for segmented mirror synthetic aperture optical systems. Particle swarm optimization (PSO) is suitable for PD for its fast convergence speed and simple structure. However, with the increase of the numbers of subapertures, optimization of cost function formed by the PD algorithm becomes a high-dimension non-linear optimization problem, which would lead PSO to result in a premature solution. Regarding the problem above, this paper presented a modified PSO in the PD algorithm to co-phase the segmented primary mirror, which overcame drawbacks of the traditional PSO, such as premature convergence and deactivation of particles, and enhanced the dig ability of the algorithm. Vast simulation results show that the modified PSO in the PD algorithm can effectively restore the segmented primary mirror, reducing the peak-to-valley (PV) value to 0.0012λ and the root mean square error to 0.0007λ, and raising the Strehl ratio to over 0.999. A comparison was also conducted to show that the modified PSO has advantages over two existing algorithms in higher accuracy and faster convergence speed.

Three-Dimensional Single-Molecule Localization Microscopy in Whole-Cell and Tissue Specimens

Super-resolution microscopy techniques are versatile and powerful tools for visualizing organelle structures, interactions, and protein functions in biomedical research. However, whole-cell and tissue specimens challenge the achievable resolution and depth of nanoscopy methods. We focus on three-dimensional single-molecule localization microscopy and review some of the major roadblocks and developing solutions to resolving thick volumes of cells and tissues at the nanoscale in three dimensions. These challenges include background fluorescence, system- and sample-induced aberrations, and information carried by photons, as well as drift correction, volume reconstruction, and photobleaching mitigation. We also highlight examples of innovations that have demonstrated significant breakthroughs in addressing the abovementioned challenges together with their core concepts as well as their trade-offs.

Extended field-of-view adaptive optics in microscopy via numerical field segmentation

Sample-induced optical aberrations in microscopy are, in general, field dependent, limiting their correction via pupil adaptive optics (AO) to the center of the available field-of-view (FoV). This is a major hindrance, particularly for deep tissue imaging, where AO has a significant impact. We present a new wide-field AO microscopy scheme, in which the deformable element is located at the pupil plane of the objective. To maintain high-quality correction across its entirety, the FoV is partitioned into small segments, and a separate aberration estimation is performed for each via a modal-decomposition-based indirect wavefront sensing algorithm. A final full-field image is synthesized by stitching of the partitions corrected consecutively and independently via their respective measured aberrations. The performance and limitations of the method are experimentally explored on synthetic samples imaged via a custom-developed AO fluorescence microscope featuring an optofluidic refractive wavefront modulator.

Fully refractive adaptive optics fluorescence microscope using an optofluidic wavefront modulator

Adaptive optics (AO) represents a powerful range of image correction technologies with proven benefits for many life-science microscopy methods. However, the complexity of adding a reflective wavefront modulator and in some cases a wavefront sensor into an already complicated microscope has made AO prohibitive for its widespread adaptation in microscopy systems. We present here the design and performance of a compact fluorescence microscope using a fully refractive optofluidic wavefront modulator, yielding imaging performance on par with that of conventional deformable mirrors, both in correction fidelity and articulation. We combine this device with a modal sensorless wavefront estimation algorithm that uses spatial frequency content of acquired images as a quality metric and thereby demonstrate a completely in-line adaptive optics microscope that can perform aberration correction up to 4^th radial order of Zernike modes. This entirely new concept for adaptive optics microscopy may prove to extend the performance limits and widespread applicability of AO in life-science imaging.

Numerically Enhanced Stimulated Emission Depletion Microscopy with Adaptive Optics for Deep-Tissue Super-Resolved Imaging

In stimulated emission depletion (STED) nanoscopy, the major origin of decreased signal-to-noise ratio within images can be attributed to sample photobleaching and strong optical aberrations. This is due to STED utilizing a high-power depletion laser (increasing the risk of photodamage), while the depletion beam is very sensitive to sample-induced aberrations. Here, we demonstrate a custom-built STED microscope with automated aberration correction that is capable of 3D super-resolution imaging through thick, highly aberrating tissue. We introduce and investigate a state of the art image denoising method by block-matching and collaborative 3D filtering (BM3D) to numerically enhance fine object details otherwise mixed with noise and further enhance the image quality. Numerical denoising provides an increase in the final effective resolution of the STED imaging of 31% using the well established Fourier ring correlation metric. Results achieved through the combination of aberration correction and tailored image processing are experimentally validated through super-resolved 3D imaging of axons in differentiated induced pluripotent stem cells growing under an 80 μm thick layer of tissue with lateral and axial resolution of 204 and 310 nm, respectively.

Object-independent image-based wavefront sensing approach using phase diversity images and deep learning

This paper proposes an image-based wavefront sensing approach using deep learning, which is applicable to both point source and any extended scenes at the same time, while the training process is performed without any simulated or real extended scenes. Rather than directly recovering phase information from image plane intensities, we first extract a special feature in the frequency domain that is independent of the original objects but only determined by phase aberrations (a pair of phase diversity images is needed in this process). Then the deep long short-term memory (LSTM) network (a variant of recurrent neural network) is introduced to establish the accurate non-linear mapping between the extracted feature image and phase aberrations. Simulations and an experiment are performed to demonstrate the effectiveness and accuracy of the proposed approach. Some other discussions are further presented for demonstrating the superior non-linear fitting capacity of deep LSTM compared to Resnet 18 (a variant of convolutional neural network) specifically for the problem encountered in this paper. The effect of the incoherency of light on the accuracy of the recovered wavefront phase is also quantitatively discussed. This work will contribute to the application of deep learning to image-based wavefront sensing and high-resolution image reconstruction.

PGC-1α overexpression partially rescues impaired oxidative and contractile pathophysiology following volumetric muscle loss injury

Volumetric muscle loss (VML) injury is characterized by a non-recoverable loss of muscle fibers due to ablative surgery or severe orthopaedic trauma, that results in chronic functional impairments of the soft tissue. Currently, the effects of VML on the oxidative capacity and adaptability of the remaining injured muscle are unclear. A better understanding of this pathophysiology could significantly shape how VML-injured patients and clinicians approach regenerative medicine and rehabilitation following injury. Herein, the data indicated that VML-injured muscle has diminished mitochondrial content and function (i.e., oxidative capacity), loss of mitochondrial network organization, and attenuated oxidative adaptations to exercise. However, forced PGC-1α over-expression rescued the deficits in oxidative capacity and muscle strength. This implicates physiological activation of PGC1-α as a limiting factor in VML-injured muscle's adaptive capacity to exercise and provides a mechanistic target for regenerative rehabilitation approaches to address the skeletal muscle dysfunction.

Optical imaging featuring both long working distance and high spatial resolution by correcting the aberration of a large aperture lens

High-resolution optical imaging within thick objects has been a challenging task due to the short working distance of conventional high numerical aperture (NA) objective lenses. Lenses with a large physical diameter and thus a large aperture, such as microscope condenser lenses, can feature both a large NA and a long working distance. However, such lenses suffer from strong aberrations. To overcome this problem, we present a method to correct the aberrations of a transmission-mode imaging system that is composed of two condensers. The proposed method separately identifies and corrects aberrations of illumination and collection lenses of up to 1.2 NA by iteratively optimizing the total intensity of the synthetic aperture images in the forward and phase-conjugation processes. At a source wavelength of 785 nm, we demonstrated a spatial resolution of 372 nm at extremely long working distances of up to 1.6 mm, an order of magnitude improvement in comparison to conventional objective lenses. Our method of converting microscope condensers to high-quality objectives may facilitate increases in the imaging depths of super-resolution and expansion microscopes.

Efficient solution to the stagnation problem of the particle swarm optimization algorithm for phase diversity

The phase diversity (PD) technique needs optimization algorithms to minimize the error metric and find the global minimum. Particle swarm optimization (PSO) is very suitable for PD due to its simple structure, fast convergence, and global searching ability. However, the traditional PSO algorithm for PD still suffers from the stagnation problem (premature convergence), which can result in a wrong solution. In this paper, the stagnation problem of the traditional PSO algorithm for PD is illustrated first. Then, an explicit strategy is proposed to solve this problem, based on an in-depth understanding of the inherent optimization mechanism of the PSO algorithm. Specifically, a criterion is proposed to detect premature convergence; then a redistributing mechanism is proposed to prevent premature convergence. To improve the efficiency of this redistributing mechanism, randomized Halton sequences are further introduced to ensure the uniform distribution and randomness of the redistributed particles in the search space. Simulation results show that this strategy can effectively solve the stagnation problem of the PSO algorithm for PD, especially for large-scale and high-dimension wavefront sensing and noisy conditions. This work is further verified by an experiment. This work can improve the robustness and performance of PD wavefront sensing.