Architecture-driven quantitative nanoscopy maps cytoskeleton remodeling.

Cytoskeleton remodeling which generates force and orchestrates signaling and trafficking to govern cell migration remains poorly understood, partly due to a lack of an investigation tool with high system flexibility, spatiotemporal resolution, and computational sensitivity. Herein, we developed a multimodal superresolution imaging system-based architecture-driven quantitative (ADQ) framework in spatiotemporal-angular hyperspace to enable both identification of the optimal imaging mode with well-balanced fidelity and phototoxicity and accurate postcharacterization of microtubule remodeling. In the ADQ framework, a pixel/voxel-wise metric reflecting heterogeneous intertubule alignment was proposed with improved sensitivity over previous efforts and further incorporated with temporal features to map dynamic microtubule rearrangements. The ADQ framework was verified by assessing microtubule remodeling in drug-induced (de)polymerization, lysosome transport, and migration. Different remodeling patterns from two migration modes were successfully revealed by the ADQ framework, with a front-rear polarization for individual directed migration and a contact site-centered polarization for cell-cell interaction-induced migration in an immune response model. Meanwhile, these migration modes were found to have consistent orientation changes, which exhibited the potential of predicting migration trajectory.

Toward Integrated Multifunctional Laser-Induced Graphene-Based Skin-Like Flexible Sensor Systems.

The burgeoning demands for health care and human-machine interfaces call for the next generation of multifunctional integrated sensor systems with facile fabrication processes and reliable performances. Laser-induced graphene (LIG) with highly tunable physical and chemical characteristics plays vital roles in developing versatile skin-like flexible or stretchable sensor systems. This Progress Report presents an in-depth overview of the latest advances in LIG-based techniques in the applications of flexible sensors. First, the merits of the LIG technique are highlighted especially as the building blocks for flexible sensors, followed by the description of various fabrication methods of LIG and its variants. Then, the focus is moved to diverse LIG-based flexible sensors, including physical sensors, chemical sensors, and electrophysiological sensors. Mechanisms and advantages of LIG in these scenarios are described in detail. Furthermore, various representative paradigms of integrated LIG-based sensor systems are presented to show the capabilities of LIG technique for multipurpose applications. The signal cross-talk issues are discussed with possible strategies. The LIG technology with versatile functionalities coupled with other fabrication strategies will enable high-performance integrated sensor systems for next-generation skin electronics.

Deep learning permits imaging of multiple structures with the same fluorophores.

Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

Compact Chromatic Confocal Lens with Large Measurement Range.

Spectral confocal sensors are effective for measuring displacements. The core of the spectral confocal measurement system is a dispersive objective lens that uses optical dispersion to establish a one-to-one correspondence between the focusing position and wavelength, achieving high-resolution measurements in the longitudinal direction. Despite significant progress in dispersive objective lenses for spectral confocal sensor systems, challenges such as a limited dispersion range, high cost, and insufficient measurement accuracy persist. To expand the measurement range and improve the accuracy of the spectral confocal sensor, we designed a compact, long-axial dispersion objective lens. This lens has a simple structure that requires only six lens elements, two of which form cemented doublets. The system length is 58 mm, with a working distance of 46 ± 6 mm and a dispersion range of 12 mm within the wavelength range of 450-656 nm. The lens has an object-side numerical aperture (NA) of 0.22 and an image-side NA between 0.198 and 0.24, ensuring high light energy utilization. Finally, a spectral confocal measurement system was constructed based on the designed dispersive objective lens, and performance evaluation tests were conducted. The test results showed that the system achieved a resolution of 0.15 µm and a maximum linear error of ±0.7 µm, demonstrating high-precision measurement capabilities. The proposed lens design enables the development of more portable and cost-effective spectral confocal sensors.

Untrained neural network enabling fast and universal structured-illumination microscopy.

Structured-illumination microscopy (SIM) offers a twofold resolution enhancement beyond the optical diffraction limit. At present, SIM requires several raw structured-illumination (SI) frames to reconstruct a super-resolution (SR) image, especially the time-consuming reconstruction of speckle SIM, which requires hundreds of SI frames. Considering this, we herein propose an untrained structured-illumination reconstruction neural network (USRNN) with known illumination patterns to reduce the amount of raw data that is required for speckle SIM reconstruction by 20 times and thus improve its temporal resolution. Benefiting from the unsupervised optimizing strategy and CNNs' structure priors, the high-frequency information is obtained from the network without the requirement of datasets; as a result, a high-fidelity SR image with approximately twofold resolution enhancement can be reconstructed using five frames or less. Experiments on reconstructing non-biological and biological samples demonstrate the high-speed and high-universality capabilities of our method.

Light and matter co-confined multi-photon lithography.

Mask-free multi-photon lithography enables the fabrication of arbitrary nanostructures low cost and more accessible than conventional lithography. A major challenge for multi-photon lithography is to achieve ultra-high precision and desirable lateral resolution due to the inevitable optical diffraction barrier and proximity effect. Here, we show a strategy, light and matter co-confined multi-photon lithography, to overcome the issues via combining photo-inhibition and chemical quenchers. We deeply explore the quenching mechanism and photoinhibition mechanism for light and matter co-confined multiphoton lithography. Besides, mathematical modeling helps us better understand that the synergy of quencher and photo-inhibition can gain a narrowest distribution of free radicals. By using light and matter co-confined multiphoton lithography, we gain a 30 nm critical dimension and 100 nm lateral resolution, which further decrease the gap with conventional lithography.

Deep learning enables contrast-robust super-resolution reconstruction in structured illumination microscopy.

Structured illumination microscopy (SIM) is a powerful technique for super-resolution (SR) image reconstruction. However, conventional SIM methods require high-contrast illumination patterns, which necessitate precision optics and highly stable light sources. To overcome these challenges, we propose a new method called contrast-robust structured illumination microscopy (CR-SIM). CR-SIM employs a deep residual neural network to enhance the quality of SIM imaging, particularly in scenarios involving low-contrast illumination stripes. The key contribution of this study is the achievement of reliable SR image reconstruction even in suboptimal illumination contrast conditions. The results of our study will benefit various scientific disciplines.

Full field-of-view hexagonal lattice structured illumination microscopy based on the phase shift of electro-optic modulators.

High throughput has become an important research direction in the field of super-resolution (SR) microscopy, especially in improving the capability of dynamic observations. In this study, we present a hexagonal lattice structured illumination microscopy (hexSIM) system characterized by a large field of view (FOV), rapid imaging speed, and high power efficiency. Our approach employs spatial light interference to generate a two-dimensional hexagonal SIM pattern, and utilizes electro-optical modulators for high-speed phase shifting. This design enables the achievement of a 210-µm diameter SIM illumination FOV when using a 100×/1.49 objective lens, capturing 2048 × 2048 pixel images at an impressive 98 frames per second (fps) single frame rate. Notably, this method attains a near 100% full field-of-view and power efficiency, with the speed limited only by the camera's capabilities. Our hexSIM demonstrates a substantial 1.73-fold improvement in spatial resolution and necessitates only seven phase-shift images, thus enhancing the imaging speed compared to conventional 2D-SIM.

Ultrahigh-printing-speed photoresists for additive manufacturing.

Printing technology for precise additive manufacturing at the nanoscale currently relies on two-photon lithography. Although this methodology can overcome the Rayleigh limit to achieve nanoscale structures, it still operates at too slow of a speed for large-scale practical applications. Here we show an extremely sensitive zirconium oxide hybrid-(2,4-bis(trichloromethyl)-6-(4-methoxystyryl)-1,3,5-triazine) (ZrO2-BTMST) photoresist system that can achieve a printing speed of 7.77 m s-1, which is between three and five orders of magnitude faster than conventional polymer-based photoresists. We build a polygon laser scanner-based two-photon lithography machine with a linear stepping speed approaching 10 m s-1. Using the ZrO2-BTMST photoresist, we fabricate a square raster with an area of 1 cm2 in ~33 min. Furthermore, the extremely small chemical components of the ZrO2-BTMST photoresist enable high-precision patterning, leading to a line width as small as 38 nm. Calculations assisted by characterizations reveal that the unusual sensitivity arises from an efficient light-induced polarity change of the ZrO2 hybrid. We envisage that the exceptional sensitivity of our organic-inorganic hybrid photoresist may lead to a viable large-scale additive manufacturing nanofabrication technology.

Phase retrieval for X-ray differential phase contrast radiography with knowledge transfer learning from virtual differential absorption model.

Grating-based X-ray phase contrast radiography and computed tomography (CT) are promising modalities for future medical applications. However, the ill-posed phase retrieval problem in X-ray phase contrast imaging has hindered its use for quantitative analysis in biomedical imaging. Deep learning has been proved as an effective tool for image retrieval. However, in practical grating-based X-ray phase contrast imaging system, acquiring the ground truth of phase to form image pairs is challenging, which poses a great obstacle for using deep leaning methods. Transfer learning is widely used to address the problem with knowledge inheritance from similar tasks. In the present research, we propose a virtual differential absorption model and generate a training dataset with differential absorption images and absorption images. The knowledge learned from the training is transferred to phase retrieval with transfer learning techniques. Numerical simulations and experiments both demonstrate its feasibility. Image quality of retrieved phase radiograph and phase CT slices is improved when compared with representative phase retrieval methods. We conclude that this method is helpful in both X-ray 2D and 3D imaging and may find its applications in X-ray phase contrast radiography and X-ray phase CT.

Sub-diffraction optical beam lithography based on a center-non-zero depletion laser.

Photoinhibition (PI) mechanisms have been introduced in nanofabrication which allows breaking the diffraction limit by large factors. Donut-shaped laser is usually selected as a depletion beam to reduce linewidth, but the parasitic process has made the results of the experiment less than expected. As a result, the linewidth is difficult to achieve below 50 nm with 780 nm femtosecond and 532 nm continuous-wave lasers. Here, we propose a new, to the best of our knowledge, method based on a center-non-zero (CNZ) depletion laser to further reduce linewidth. By constructing a smaller zone of action under the condition of keeping the maximum depletion intensity constant, a minimum linewidth of 30 nm (λ / 26) was achieved. Two ways to construct CNZ spots were discussed and experimented, and the results show the advantages of our method to reduce the parasitic process to further improve the writing resolution.

A multimodal image feature extraction method for x-ray grating phase contrast computed tomography based on monogenic signal.

Traditional computed tomography (CT) based on x-ray absorption imaging has made great progress in clinical medicine, and CT combined with x-ray phase contrast imaging (XPCI) technology has become a new research hotspot in recent years. XPCI can separate the attenuation, refraction, and scattering signals of the object and retrieve three types of feature images known as absorption contrast image, differential phase contrast image, and dark field contrast image. However, the quality of CT images is always degraded due to noise and reconstruction artifacts, which makes feature recognition methods for CT images necessary. Most of the existing CT image recognition algorithms are focused on AC-CT images, with little attention paid to other contrast images. Herein, a new method is proposed, named the variable kernel multi-scale adaptive monogenic signal phase consistency model (VK-MA PC model), which constructs monogenic signals with corresponding filters according to the characteristics of different contrast images. The model obtains better image features by using multi-scale analysis and optional pre-decomposition, which make images decomposed into different levels. Experiments on 4D extended cardiac-torso (XCAT) human body simulation data and laboratory fish XPCI-CT data demonstrate the potential applicability of the VK-MA PC model in the field of XPCI-CT.

High-throughput multiplexed fluorescence lifetime microscopy.

Fluorescence lifetime microscopy has been widely used in quantifying cellular interaction or histopathological identification of different stained tissues. A novel, to the best of our knowledge, approach for high-throughput multiplexed fluorescence lifetime imaging is presented. To establish a high-throughput fluorescence lifetime acquisition system, a uniformed illumination optical focus array was generated by a novel computer-generated hologram algorithm based on matrix triple product. This, in conjunction with an array detector and multichannel time-correlated single-photon counting, enables the full use of the acquisition ability of each detector. By utilizing interval segmentation of photon time detection, a high-throughput multiplexed fluorescence lifetime imaging is achieved. Experimental results demonstrate that this method achieves a fivefold increase in the collection throughput of fluorescence lifetime and is capable of simultaneous dual-target fluorescence lifetime measurement.

A novel fusion method for X-ray phase contrast imaging based on fast adaptive bidimensional empirical mode decomposition.

BACKGROUNDS: X-ray phase contrast imaging (XPCI) can separate the attenuation, refraction, and scattering signals of the object. The application of image fusion enables the concentration of distinctive information into a single image. Some methods have been applied in XPCI field, but wavelet-based decomposition approaches often result in loss of original data. OBJECTIVE: To explore the application value of a novel image fusion method for XPCI system and computed tomography (CT) system. METHODS: The means of fast adaptive bidimensional empirical mode decomposition (FABEMD) is considered for image decomposition to avoid unnecessary information loss. A parameter δ is proposed to guide the fusion of bidimensional intrinsic mode functions which contain high-frequency information, using a pulse coupled neural network with morphological gradients (MGPCNN). The residual images are fused by the energy attribute fusion strategy. Image preprocessing and enhancement are performed on the result to ensure its quality. The effectiveness of other image fusion methods has been compared, such as discrete wavelet transforms and anisotropic diffusion fusion. RESULTS: The δ-guided FABEMD-MGPCNN method achieved either the first or second position in objective evaluation metrics with biological samples, as compared to other image fusion methods. Moreover, comparisons are made with other fusion methods used for XPCI. Finally, the proposed method applied in CT show expected results to retain the feature information. CONCLUSIONS: The proposed δ-guided FABEMD-MGPCNN method shows potential feasibility and superiority over traditional and recent image fusion methods for X-ray differential phase contrast imaging and computed tomography systems.

3D nanoprinting for fiber-integrated achromatic diffractive lens.

Achromatic performance is crucial for numerous multi-wavelength optical fiber applications, including endoscopic imaging and fiber sensing. This paper presents the design and nanoprinting of a fiber-integrated achromatic diffractive lens within the visible spectrum (450-650 nm). The 3D nanoprinting is achieved by a high-resolution direct laser writing technology, overcoming limitations in the optical performance caused by the lack of an arbitrary 3D structure writing capability and an insufficient feature resolution in the current manufacturing technology for visible light broadband achromatic diffractive lenses. A three-step optimization algorithm is proposed to effectively balance optical performance with writing difficulty. The characterization results demonstrate excellent achromatic focusing performance, paving the way towards the development of nanoprinted flat optical devices for applications such as optical fiber traps, miniature illumination systems, and integrated photonic chips.

Three-dimensional multi-color optical nanoscopy at sub-10-nm resolution based on small-molecule organic probes.

Achieving nanometer-scale resolution remains challenging in expansion microscopy due to photon loss. To address this concern, here we develop a multi-color expansion stimulated emission depletion technique based on small-molecule probes to realize high labeling density and intensity. Our method substantially lowers the barrier to visualizing diverse intracellular proteins and their interactions in three dimensions. It enables us to achieve sub-10-nm resolution in structures such as microfilaments, lysosomes, and mitochondria, providing new insights into cell biology.

DMD-based compact SIM system with hexagonal-lattice-structured illumination.

In this study, we developed a novel, compact, and efficient structured illumination microscopy (SIM) system, to our best knowledge. A binary hexagonal lattice pattern was designed and implemented on a digital micromirror device (DMD), resulting in a projection-based structured-light generation. By leveraging the combination of the high-speed switching capability of the DMD with a high-speed CMOS camera, the system can capture 1024×1024 pixels images at a 200 fps frame rate when provided with sufficient illumination power. The loading of the hexagonal lattice pattern reduces the number of images required for reconstruction to seven, and by utilizing the DMD modulating characteristics on the illumination path, there is no need to use bulky mechanical structures for phase shifting. We designed a compact system with 110m m×150m m×170m m dimensions that displayed a 1.61 resolution enhancement for fluorescent particle and biological sample imaging.

High-resolution 3D nanoprinting based on two-step absorption via an integrated fiber-coupled laser diode.

Three-dimensional (3D) laser nanoprinting with high resolution and low cost is highly desirable for fabricating arbitrary 3D structures with fine feature size. In this work, we use a 405-nm integrated fiber-coupled continuous wave (cw) laser diode to establish an easy-to-build 3D nanoprinting system based on two-step absorption. Two-dimensional (2D) gratings with a sub-150-nm period and 3D woodpile nanostructures with a lateral period of 350 nm have been printed at a low speed. At a faster scan velocity of 1000 µm/s, 2D gratings with sub-200-nm resolution and sub-50-nm linewidth can still be fabricated with laser power less than 1 mW. The two-step absorption of the used benzil initiator enables us to use a second cw laser with 532-nm wavelength to enhance the polymerization with sub-100-nm feature size when starting with insufficient 405-nm laser power, which possess the potential to find applications in high-speed high-resolution parallel-writing and in situ manipulation.

Tri-directional x-ray phase contrast multimodal imaging using one hexagonal mesh modulator.

Objective. X-ray phase contrast imaging is a promising technique for future clinical diagnostic as it can provide enhanced contrast in soft tissues compared to traditional x-ray attenuation-contrast imaging. However, the strict requirements on the x-ray coherence and the precise alignment of optical elements limit its applications towards clinical use. To solve this problem, mesh-based x-ray phase contrast imaging method with one hexagonal mesh is proposed for easy alignment and better image visualization.Approach. The mesh produces structured illuminations and the detector captures its distortions to reconstruct the absorption, differential phase contrast (DPC) and dark-field (DF) images of the sample. In this work, we fabricated a hexagonal mesh to simultaneously retrieve DPC and DF signals in three different directions with single shot. A phase retrieval algorithm to obtain artifacts-free phase from DPC images with three different directions is put forward and false color dark-field image is also reconstructed with tri-directional images. Mesh-shifting method based on this hexagonal mesh modulator is also proposed to reconstruct images with better image quality at the expense of increased dose.Main results. In numerical simulations, the proposed hexagonal mesh outperforms the traditional square mesh in image evaluation metrics performance and false color visualization with the same radiation dose. The experimental results demonstrate its feasiblity in real imaging systems and its advantages in quantitive imaging and better visualization. The proposed hexagonal mesh is easy to fabricate and can be successfully applied to x-ray source with it spot size up to 300µm.Significance. This work opens new possibilities for quantitative x-ray non-destructive imaging and may also be instructive for research fields such as x-ray structured illumination microscopy (SIM), x-ray spectral imaging and x-ray phase contrast and dark-field computed tomography (CT).