Journal of microscopy最新文献

Structured illumination microscopy (SIM) enables superresolution imaging of biological samples but suffers from artefacts, noise, and loss of high-frequency details in low-light conditions. These problems arise due to limitations in traditional reconstruction methods such as single-scale upsampling and pixel-wise losses that fail to capture SIM's multi-scale frequency patterns. We propose Att-SIM-LapSRN, a hybrid deep learning framework that integrates Attention U-Net with Laplacian pyramid super-resolution network (LapSRN) to address these challenges. Attention gates at skip connections selectively enhance salient feature representations corresponding to moiré patterns while attenuating background noise, producing sharper reconstructions precise localisation of cell structures. The LapSRN component employs progressive multiscale upsampling across pyramid levels to reduce the bicubic interpolation. Additionally, we introduce an FFT-based loss function that explicitly targets spatial frequency patterns, ensuring structural consistency, contrast enhancement and edge sharpness critical for SIM imaging. Our model was evaluated on the BioSR dataset, demonstrating superior performance over state-of-the-art methods, with significant improvements in PSNR, SSIM, and perceptual quality metrics. Att-SIM-LapSRN achieves enhanced lateral resolution and structural fidelity, making it a robust solution for high-quality SIM reconstruction in biological imaging applications.

Measuring the bismuth (Bi) content of ternary gallium arsenide bismuthide (GaAsBi) alloys is important because it sensitively influences their bandgap, and Bi is known to segregate vertically to the surface and sometimes also laterally during growth, so elemental distribution maps need to be quantified. A suitable method is mapping of characteristic X-rays based on energy-dispersive X-ray spectroscopy (EDXS) in a scanning transmission electron microscope (STEM). One of the key problems in this alloy system that there are several overlaps of characteristic X-ray lines from the corresponding elements, namely of As Kα with Bi Lα and of a sum peak of Ga Lα and As Lα with Bi Mα, which no standard solid-state detector could distinguish. Routine quantification procedures thus often fail, exhibiting unacceptably large scatter. Here, an iterative procedure using k*-factors is outlined, leading to improved quantification using sets of different X-ray line pairs to be consistent within better than 1% bismuth coverage of the group V sub-lattice, for a range up to 14%.

X-ray phase contrast imaging (XPCI), when implemented in micro-computed tomography (micro-CT) mode, offers high-contrast 3D imaging of weakly-attenuating material samples. In the so-called single-mask edge illumination approach, a mask with periodically spaced transmitting apertures is used to split the x-ray beam into narrow beamlets; when the beamlets are aligned with the boundaries ('edges') between detector pixels, their refraction-induced deviation can be detected and used to form images. A shortcoming is that the mask reduces the x-ray flux, necessitating longer exposures and therefore longer acquisition times. We show that the demand on exposure time can be relaxed by integrating the deep learning-based denoising technique Noise2Inverse into the image processing workflow. The applicability of Noise2Inverse to single-mask edge illumination XPCI micro-CT is demonstrated, and its performance at severe noise levels is explored. Taking advantage of the distinct imaging system characteristics, we also propose an adaptation of Noise2Inverse, called Noise2Phase, which does not rely on splitting the CT dataset by projections.

The eutectic area fraction is a critical indicator of metallographic properties and directly influences the mechanical properties of superalloys. Rapid and accurate detection of eutectic regions is essential for optimising material properties and structural analysis. In this work, a dataset was established for identifying eutectic regions, with images acquired using an optical microscope, which provides high-resolution imaging and detailed microstructural visualisation. Optical microscopy enables precise detection of eutectic regions by capturing contrast variations between eutectic and matrix phases, ensuring high-quality image inputs for segmentation tasks. A multiple attention mechanism UNet (MAM-UNet) for eutectic segmentation of superalloys is proposed, which incorporates efficient channel attention (ECA) and a convolutional block attention module (CBAM) to enhance feature extraction from optical microscopy eutectic images. The experimental results show that the PA and MIoU for eutectic segmentation of nickel-based superalloys can achieve 99.1% PA and 87.56% MIoU, which demonstrates that the proposed MAM-UNet method has excellent segmentation capability compared with other image segmentation networks.

Mitochondria are double-membrane organelles whose architecture enables ATP (Adenosine Triphosphate) production, redox signalling, calcium homeostasis, and apoptosis. Visualisation of mitochondria requires imaging technologies across spatial and temporal scales. Conventional fluorescence microscopy techniques, such as wide-field, confocal, spinning-disk, and light-sheet microscopy, enable the real-time observation of mitochondrial networks and dynamics in live cells. Super-resolution methods, including structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), photoactivated localisation microscopy (PALM), stochastic optical reconstruction microscopy (STORM), and expansion microscopy, provide access to fine sub-mitochondrial structures, such as cristae, overcoming the diffraction limit. Additionally, proximity-based approaches such as FRET (Förster Resonance Energy Transfer), split-fluorescent proteins, and proximity ligation assays allow researchers to probe sub-compartmental interactions and organelle contact sites with nanometre-level sensitivity. Electron microscopy (EM) complements optical techniques by offering near-molecular resolution of mitochondrial ultrastructure, including membranes, cristae, and inter-organelle interfaces. In this review, we comprehensively examined the principles, capabilities, and limitations of these diverse imaging modalities, with a focus on recent advances. We highlight the development of novel fluorescent probes, integrated correlative techniques, and computational analysis pipelines to expand the utility of mitochondrial imaging. By placing these innovations in historical and theoretical contexts, we aim to clarify how each method works and why it is suited to biological questions. Finally, we explore how mitochondrial imaging has revolutionised our understanding of physiology and pathology.

The microstructure distribution of multiphase porous media has important effects on its macroscopic properties. In this paper, we propose a framework for multiphase reconstruction based on entropy statistical descriptor, which is used as morphological information to perform on two-dimensional (2D) and three-dimensional (3D) reconstruction. The accuracy of the 2D reconstruction is evaluated using the lineal-path function and two-point cluster function, which reflect the connectivity of the microstructure. From the result of the 2D reconstructions, it is noted that the reconstructed three-phase microstructures have the similar morphological information with that of the original images and their lineal-path function and two-point cluster function are closely similar to each other. For the reconstructed 3D microstructures, the accuracy of the reconstructions is also quantified by the lineal-path function and two-point cluster function, which reflect the comprehensive information of the 3D connectivity of the system. The comparison of the lineal-path function and two-point cluster function between all the slices of the reconstructed 3D structures in three directions and that of the reference images shows that the proposed method can capture the prominent features of the original images. The lineal-path function and two-point cluster function of the reconstructed 3D structures are also compared with that of their original 3D structures, which shows that the reconstructed 3D structures have the similar distribution with that of the original 3D structures. This indicates that our proposed method has the ability to capture salient morphological information of multiphase system.

Cellulose materials are suitable to replace plastic in food packaging. They are hydrophilic and may have poor barrier properties that affect the shelf life of the food due to the migration of contaminants. The wet lamination of microfibrillated cellulose films on cellulose materials appears as a promising way to improve their barrier properties by forming a bilayer material. These barrier properties depend on the microstructural properties (porosity, pores connectivity, specific surface area or contact surface area between the two layers) of both layers, notably the film, which are poorly known. Therefore, a multiscale approach is proposed to estimate such microstructural parameters by combining three 3D imaging methods: synchrotron X-ray micro-/nanotomography and FIB-SEM tomography. The 3D microstructure of two different bilayer materials obtained with two Microfibrillated Cellulose (MFC) grades is investigated. For the first time, a full 3D representation of such material is presented. Regardless of the scale under consideration, our results showed that both films present a dense structure with very low porosity and no pore connectivity along the thickness. The MFC film produced with the smallest MFC fibrils led to a more homogeneous and less porous layer with a larger contact surface to the paper.