Spectral Imaging Research Articles

Engineering Macrophage-Derived Exosome to Deliver Pirfenidone: A Novel Approach to Combat Silicotic Pulmonary Fibrosis.

Silicosis is a severe lung disease characterized by diffuse pulmonary fibrosis, for which there is currently no effective treatment. Pirfenidone (PFD) shows great antifibrotic potential but is clinically hindered by low bioavailability and gastrointestinal side effects. To address these limitations, this study develops a PFD delivery system (PFD-Exo) using J774A.1 macrophage-derived exosomes. Firstly, PFD is loaded via sonication, then PFD-Exo is characterized using Raman spectral imaging and UV absorption spectroscopy. Finally, in vitro and in vivo silicosis models are established to evaluate its antifibrotic effects. Results show that PFD-Exo outperforms free PFD in inhibiting TGF-β1-induced transdifferentiation of primary lung fibroblasts in vitro. In a mouse model of silicosis, PFD-Exo is found to be accumulated in the lungs following intratracheal administration and significantly ameliorates pulmonary inflammation and fibrosis while minimizing gastrointestinal side effects. Mechanistic studies reveal that PFD-Exo modulates the TGF-β signaling pathway by downregulating SMAD3 and upregulating SMAD7 and NOGGIN. In conclusion, this study provides the first evidence of macrophage-derived exosomes as an effective PFD delivery system for silicosis treatment and offers a promising strategy for other refractory pulmonary diseases.

Retraction Note: Application of intelligent trajectory analysis based on new spectral imaging technology in basketball match motion recognition

Retraction Note: Application of intelligent trajectory analysis based on new spectral imaging technology in basketball match motion recognition

Investigating deep learning strategies for fast denoising of 5D cardiac photon-counting micro-CT images


Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. 

Approach. 
We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. 

Main results. 
Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). 

Significance. 
Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.

REXPACO ASDI: joint unmixing and deconvolution of the circumstellar environment by angular and spectral differential imaging

ABSTRACT Angular and spectral differential imaging is an observational technique of choice to investigate the immediate vicinity of stars. By leveraging the relative angular motion and spectral scaling between on-axis and off-axis sources, post-processing techniques can separate residual star light from light emitted by surrounding objects such as circumstellar discs or point-like objects. This paper introduces a new algorithm that jointly unmixes these components and deconvolves disc images. The proposed algorithm is based on a statistical model of the residual star light, accounting for its spatial and spectral correlations. These correlations are crucial yet remain inadequately modelled by existing reconstruction algorithms. We employ dedicated shrinkage techniques to estimate the large number of parameters of our correlation model in a data-driven fashion. We show that the resulting separable model of the spatial and spectral covariances captures very accurately the star light, enabling its efficient suppression. We apply our method to data sets from the Very Large Telescope/Spectro-Polarimetry High-contrast Exoplanet REsearch instrument and compare its performance with standard algorithms (median subtraction, PCA, PACO). We demonstrate that considering the multiple correlations within the data significantly improves reconstruction quality, resulting in better preservation of both disc morphology and photometry. With its unique joint spectral modelling, the proposed algorithm can reconstruct discs with circular symmetry (e.g. rings, spirals) at intensities one million times fainter than the star, without needing additional reference data sets free from off-axis objects.

Compact staring-type underwater spectral imaging system utilizing k-Nearest neighbor-based interpolation for spectral reconstruction

Besides the challenges posed by the complex optical properties of water, underwater spectral imaging face significant issues, including system bulkiness, optical complexity, and high spectral resolution requirement. This study introduces a compact staring-type underwater spectral imaging system designed to overcome these limitations. Featuring a 6-channel rotary spectral camera, the system is optimized for optical simplicity, and spectral efficiency. It employs a k-Nearest Neighbor (KNN)-based spectral reconstruction method to substantially enhance spectral resolution. Auto-focusing and image registration techniques, utilizing the Tenengrad function and Scale-Invariant Feature Transform (SIFT) feature points, ensure sharp and aligned multi-channel imagery. Experimental validation demonstrates the system’s capability to accurately reconstruct spectral reflectance data across the 400 nm to 700 nm band, significantly increasing spectral resolution from 6 to 31 channels. The spectral reflectance reconstruction results, achieving an average Root Mean Square Error (RMSE) of 0.0651 and Goodness of Fit Coefficient (GFC) of 0.9871, highlight the system’s efficiency in precise spectral analysis. This advancement in spectral resolution and adaptability to diverse underwater applications marks a significant leap forward in underwater observation and monitoring through spectral imaging.

A Closer Look at Heritage Systems from Medieval Colors to Modern and Contemporary Artworks

This microreview, conducted by interdisciplinary teams, examines complex heritage material systems, such as medieval colors and modern and contemporary artworks. Our multi-analytical approach, a significant aspect of our research, is a means to this end. The conservation of works of art is our shared goal, as it ensures their accessibility and the transfer of cultural heritage to future generations. We seek to interpret the damage, usefulness, and innovation of the experimental design in this context. As Jan Wouters rightly points out, “The terminology used nowadays to describe the potential damage to objects caused by analysis should be refined beyond the destructiveness/non-invasiveness polarization. A terminology should include at least degree level intervention (low, medium, high), usefulness, and innovation”. Complementing micro- or sub-micro-sampling with the appropriate analytical methods is crucial, as exemplified in medieval, modern, and contemporary collections studies. Finally, a novel perspective for exploring the information contained in the multiscale heterogeneity of organic historical materials is envisaged, and it includes UV/Visible photoluminescence spectral imaging using a low-intensity ultraviolet synchrotron beam.

Photon-counting detector computed tomography in cardiac imaging.

Photon-counting detector computed tomography (PCD-CT) has emerged as arevolutionary technology in CT imaging. PCD-CT offers significant advancements over conventional energy-integrating detector CT, including increased spatial resolution, artefact reduction and inherent spectral imaging capabilities. In cardiac imaging, PCD-CT can offer amore accurate assessment of coronary artery disease, plaque characterisation and the in-stent lumen. Additionally, it might improve the visualisation of myocardial fibrosis through qualitative late enhancement imaging and quantitative extracellular volume measurements. The use of PCD-CT in cardiac imaging holds significant potential, positioning itself as avaluable modality that could serve as aone-stop-shop by integrating both angiography and tissue characterisation into asingle examination. Despite its potential, large-scale clinical trials, standardisation of protocols and cost-effectiveness considerations are required for its broader integration into clinical practice. This narrative review provides an overview of the current literature on PCD-CT regarding the possibilities and limitations of cardiac imaging.

A MLP architecture fusing RGB and CASSI for computational spectral imaging

The coded Aperture Snapshot Spectral Imaging (CASSI) system offers significant advantages in dynamically acquiring hyper-spectral images compared to traditional measurement methods. However, it faces the following challenges: (1) Traditional masks rely on random patterns or analytical design, limiting CASSI’s performance improvement. (2) Existing CASSI reconstruction algorithms do not fully utilize RGB information. (3) High-quality reconstruction algorithms are often slow and limited to offline scene reconstruction. To address these issues, this paper proposes a new MLP architecture, Spectral-Spatial MLP (SSMLP), which replaces the transformer structure with a network using CASSI measurements and RGB as multimodal inputs. This maintains reconstruction quality while significantly improving reconstruction speed. Additionally, we constructed a teacher-student network (SSMLP with a teacher, SSMLP-WT) to transfer the knowledge learned from a large model to a smaller network, further enhancing the smaller network’s accuracy. Extensive experiments show that SSMLP matches the performance of transformer-based structures in spectral image reconstruction while improving inference speed by at least 50%. The reconstruction quality of SSMLP-WT is further improved by knowledge transfer without changing the network, and the teacher boosts the performance by 0.92 dB (44.73 dB vs. 43.81 dB).

Assessment of submicron bone tissue composition in plastic-embedded samples using optical photothermal infrared (O-PTIR) spectral imaging and machine learning

Understanding the composition of bone tissue at the submicron level is crucial to elucidate factors contributing to bone disease and fragility. Here, we introduce a novel approach utilizing optical photothermal infrared (O-PTIR) spectroscopy and imaging coupled with machine learning analysis to assess bone tissue composition at 500 nm spatial resolution. This approach was used to evaluate thick bone samples embedded in typical poly(methyl methacrylate) (PMMA) blocks, eliminating the need for cumbersome thin sectioning. We demonstrate the utility of O-PTIR imaging to assess the distribution of bone tissue mineral and protein, as well as to explore the structure-composition relationship surrounding microporosity at a spatial resolution unattainable by conventional infrared imaging modalities. Using bone samples from wildtype (WT) mice and from a mouse model of osteogenesis imperfecta (OIM), we further showcase the application of O-PTIR spectroscopy to quantify mineral content, crystallinity, and carbonate content in spatially defined regions across the cortical bone. Notably, we show that machine learning analysis using support vector machine (SVM) was successful in identifying bone phenotypes (typical in WT, fragile in OIM) based on input of spectral data, with over 86% of samples correctly identified when using the collagen spectral range. Our findings highlight the potential of O-PTIR spectroscopy and imaging as valuable tools for exploring bone submicron composition.

An Improved Hyper Spectral Imaging for Accurate Disease Diagnosis in Sustainable Medical Environments

Hyper spectral imaging (HSI) has emerged as a powerful technique for accurate disease diagnosis in medical environments. It provides high-resolution images with detailed spectral information, making it possible to identify subtle differences between healthy and diseased tissues. However, current HSI systems face challenges in terms of accuracy and efficiency, limiting their widespread application in sustainable medical environments. To overcome these challenges, our team has developed an improved HSI system that utilizes state-of-the-art spectral imaging technology and machine learning algorithms. This system is capable of capturing and analyzing a wider range of spectral data, enabling more precise identification of disease-specific spectral signatures. Furthermore, our system has been optimized to operate with minimal power consumption, making it environmentally friendly and suitable for sustainable medical environments. The improved HSI system has been successfully tested in clinical settings and has shown promising results in accurately diagnosing various diseases such as cancer, dermatitis, and cardiovascular conditions. Its high accuracy and fast processing time make it a valuable tool for early disease detection and treatment planning. Moreover, the ability to operate with low energy consumption makes it a sustainable solution for medical facilities in resourcelimited areas. In addition to its accuracy and efficiency, our improved HSI system is also user-friendly and can be easily integrated into existing medical imaging systems.

Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction – A retrospective observational study

PurposeTo compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images from dual-energy computed tomography (DECT) for the evaluation of head and neck neoplasms with CT scans from a conventional single-energy protocol. MethodA total of 294 head and neck CT scans (98 VMIs operated at 60 keV, 102 MD iodine images, and 94 images from a 120 kVp single-energy CT (SECT) protocol) were retrospectively evaluated. VMIs and MD iodine images were generated using the Gemstone Spectral Imaging (GSI) mode using DLIR and metal artifact reduction (MAR) algorithms. SECT images were generated using adaptive statistical iterative reconstruction (ASIR-V). Images were scored by two independent readers on a 6-point Likert-type scale for overall image quality, vessel contrast, soft tissue contrast, noise texture, noise intensity, artifact reduction, and sharpness. ResultsSubjective overall image quality was rated as superior or excellent in 98 % of DLIR-based MD iodine images and VMIs, but only in 55 % of ASIR-V-based SECT images. For each individual quality criterion, image quality of VMIs and MD iodine images was rated as better than that of SECT images (p < 0.001 in each case). Noise texture and intensity were rated better in MD iodine images than in VMIs. ConclusionDECT using both DLIR and MAR for the generation of VMIs and MD iodine images resulted in higher subjective quality of oncologic head and neck images than ASIR-V-based SECT. Noise reduction and noise texture were best achieved with DLIR-based MD iodine images.

First Solar Orbiter observation of a dark halo in the solar atmosphere

Context. Solar active regions (ARs) are often surrounded by dark large areas of reduced emission compared to the quiet Sun, observed at various wavelengths corresponding to the chromosphere, transition region (TR), and corona, known as dark halos (DHs). The mechanisms behind the darker emission of DHs remain unclear and merit a wider scope of study. Aims. This study aims to investigate for the first time the fine structure of a DH observed by the EUV High Resolution Imager (HRIEUV) on board the ESA’s Solar Orbiter (SO) mission and its appearance in the TR. Aims. We utilized the extensive 1 hour dataset from SO on 19 March 2022, which includes high-resolution observations of NOAA 12967 and part of the surrounding DH. We analyzed the dynamics of the HRIEUV DH fine structure and its appearance in the HRILyα image. We also analyzed the Spectral Imaging of the Coronal Environment (SPICE) Lyβ, C III, N VI, O VI, and Ne VIII lines, which sample the TR in the log T(K) ∼ 4.0–5.8 range. This analysis was complemented with a simultaneous BLOS magnetogram taken by the High Resolution Telescope (HRT). Methods. We report the presence of a peculiar fine structure that has not been observed for the quiet Sun. It is characterized by combined bright EUV bundles and dark regions, arranged and interconnected in such a way that they cannot be clearly separated. They form a spatial continuum extending approximately radially from the AR core, suggesting a deep connection between the DH and the AR. Additionally, we find that the bright EUV bundles are observed in all the SPICE TR lines and the HRILyα band and present photospheric BLOS footprints in the HRT magnetogram. This spatial correlation indicates that the origin of the 174 Å DH may lie in the low atmosphere: the photosphere and chromosphere.

Using the Cartwheel CME to Predict Off-limb Observations of CMEs for New and Upcoming UV and EUV Spectrometers

Coronal mass ejections (CMEs) expel multithermal, magnetized plasma from the Sun, and when directed toward Earth, can cause extensive damage to space and ground-based electronics. To better understand the triggering, acceleration, and evolution of CMEs, it is critical to study CME plasma properties close to the Sun. High-resolution ultraviolet and extreme ultraviolet (UV-EUV) spectroscopy can give the most detailed plasma diagnostics of CMEs in the low solar corona. Unfortunately, very few spectrally resolved observations of CMEs in the low solar corona exist. However, with the recent launch of the Spectral Imaging of the Coronal Environment on board Solar Orbiter and the upcoming missions, including the EUV High-Throughput Solar Telescope (EUVST) on Solar-C and the Multi-slit Solar Explorer (MUSE), we will have the opportunity to obtain unprecedented, spectrally resolved CME observations. Using the only full EUV spectral observation of a CME by the Hinode/EUV Imaging Spectrometer, we predict the spectra that SPICE, EUVST, and MUSE are expected to observe during an off-limb CME eruption to investigate the diagnostic capabilities of each instrument. Finally, we provide a list of density-sensitive and temperature-sensitive ratios for CME plasma diagnostics along with the expected spectral atlas for each instrument to facilitate observing sequence planning.

Feasibility and potential of terahertz spectral and imaging technology for Apple Valsa canker detection: A preliminary investigation

Apple Valsa canker (AVC) caused by the Ascomycete Valsa mali, seriously constrains the production and quality of apple fruits. The symptomless incubation characteristics of Valsa mali make it highly challenging to detect AVC at an early infection stage. After infecting the wound of apple bark, the pathogenic hyphae of AVC will expand and colonize the phloem tissue. Meanwhile, various enzymes and toxic substances released by hyphae cause the decomposition of cellulose and lignin, and the generation of poisonous secondary metabolites in bark tissue. However, these early symptoms of AVC are invisible from the bark’s appearance. Fortunately, Terahertz Spectral Imaging (ThzSI) technology with the advantage of penetrating, and fingerprinting is promising for detecting hidden or slight symptoms of the fungal infection. This study is a preliminary investigation of terahertz frequency-domain spectra for AVC in the early stage of infection. Healthy and two-week-infected apple tree branches were prepared for capturing ThzS images, and the spectral data were preprocessed by Multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing (SG), and standard normal variate (SNV) respectively to remove data noise and improve data quality. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and random frog (RFROG) were employed to extract the spectral feature bands to eliminate redundant data and improve computational efficiency. Machine learning models were established based on the spectral features to detect AVC at an early infection stage, where 11 of them exhibited the best performance with F1-score of 99.72%. To further explore disease information in spatial spectra, imaging data were acquired using terahertz imaging technology. Based on imaging data, pseudo-color imaging, histogram equalization, and Otsu segmentation were employed to visualize early infection areas in apple barks. Furthermore, histogram feature (HF), shape feature (SF), and local binary pattern (LBP) extracted from terahertz spectral images were utilized to establish the SVM, RF, and KNN models. HF-SF-KNN and HF-SF-LBP-KNN with the best performance achieved F1-score of 98.82%. This study presents a preliminary application of terahertz spectral and imaging technology for early-stage AVC detection and demonstrates its feasibility. Additionally, it provides a new way to detect AVC, which expands the application of ThzSI technology in tree disease detection in orchards and lays the foundation for further research.

Enhancing 3D Models with Spectral Imaging for Surface Reflectivity.

The increasing demand for accurate and detailed 3D modeling in fields such as cultural heritage preservation, industrial inspection, and scientific research necessitates advanced techniques to enhance model quality. This paper addresses this necessity by incorporating spectral imaging data to improve the surface detail and reflectivity of 3D models. The methodology integrates spectral imaging with traditional 3D modeling processes, offering a novel approach to capturing fine textures and subtle surface variations. The experimental results of this paper underscore the advantages of incorporating spectral imaging data in the creation of 3D models, particularly in terms of enhancing surface detail and reflectivity. The achieved experimental results demonstrate that 3D models generated with spectral imaging data exhibit significant improvements in surface detail and accuracy, particularly for objects with intricate surface patterns. These findings highlight the potential of spectral imaging in enhancing 3D model quality. This approach offers significant advancements in 3D modeling, contributing to more precise and reliable representations of complex surfaces.

Simultaneous assessment of NAD(P)H and flavins with multispectral fluorescence lifetime imaging microscopy at a single excitation wavelength of 750nm.

Autofluorescence characteristics of the reduced nicotinamide adenine dinucleotide and oxidized flavin cofactors are important for the evaluation of the metabolic status of the cells. The approaches that involve a detailed analysis of both spectral and time characteristics of the autofluorescence signals may provide additional insights into the biochemical processes in the cells and biological tissues and facilitate the transition of spectral fluorescence lifetime imaging into clinical applications. We present the experiments on multispectral fluorescence lifetime imaging with a detailed analysis of the fluorescence decays and spectral profiles of the reduced nicotinamide adenine dinucleotide and oxidized flavin under a single excitation wavelength aimed at understanding whether the use of multispectral detection is helpful for metabolic imaging of cancer cells. We use two-photon spectral fluorescence lifetime imaging microscopy. Starting from model solutions, we switched to cell cultures treated by metabolic inhibitors and then studied the metabolism of cells within tumor spheroids. The use of a multispectral detector in combination with an excitation at a single wavelength of 750nm allows the identification of fluorescence signals from three components: free and bound NAD(P)H, and flavins based on the global fitting procedure. Multispectral data make it possible to assess not only the lifetime but also the spectral shifts of emission of flavins caused by chemical perturbations. Altogether, the informative parameters of the developed approach are the ratio of free and bound NAD(P)H amplitudes, the decay time of bound NAD(P)H, the amplitude of flavin fluorescence signal, the fluorescence decay time of flavins, and the spectral shift of the emission signal of flavins. Hence, with multispectral fluorescence lifetime imaging, we get five independent parameters, of which three are related to flavins. The approach to probe the metabolic state of cells in culture and spheroids using excitation at a single wavelength of 750nm and a fluorescence time-resolved spectral detection with the consequent global analysis of the data not only simplifies image acquisition protocol but also allows to disentangle the impacts of free and bound NAD(P)H, and flavin components evaluate changes in their fluorescence parameters (emission spectra and fluorescence lifetime) upon treating cells with metabolic inhibitors and sense metabolic heterogeneity within 3D tumor spheroids.

Bright compact ultrabroadband source by orthogonal laser-sustained plasma

Laser-sustained plasma (LSP) source featuring high brightness and broadband spectral coverage is found to be powerful in various fields of scientific and industrial applications. However, the fundamental limit of low conversion efficiency constrains the system compactness and widespread applications of such broadband light sources. In this paper, we propose an innovative orthogonal LSP to break through the conversion efficiency limitation. Driven by the elevated conversion efficiency from absorbed laser power to ultraviolet (UV) emission, a compact broadband source (250–1650 nm) with UV spectral radiance exceeding 210 mW/(mm2⋅sr⋅nm)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${mW}/({{mm}}^{2}\\,\\cdot\\, {sr}\\,\\cdot\\, {nm})$$\\end{document} is achieved with >100 W pump laser. With the plot of a two-dimensional refractive index model, we report an important conceptual advance that the orthogonal design eliminates the influence of the negative lensing effect on laser power density. Experimental results unambiguously demonstrate that we achieve a bright compact UV-VIS-NIR source with negligible thermal loss and the highest conversion efficiency to our knowledge. Significant enhancement of 4 dB contrast-to-noise ratio (CNR) in spectral single-pixel imaging has been demonstrated using the proposed ultrabroadband source. By establishing the quantitative link between pumping optics design and plasma absorption, this work presents a compact broadband source that combines superior conversion efficiency and unprecedented brightness, which is essential to high-speed inspection and spectroscopy applications.

Imaging Spectropolarimeter Using a Multifunctional Metasurface.

Spectral polarization imaging is critical for broad applications ranging from remote sensing to biomedicine. Here, we propose and experimentally demonstrate an imaging spectropolarimeter based on a single multifunctional metasurface. The designed metasurface accurately maps spectral and polarization information onto focal points and vortex beams, enabling simultaneous detection through intensity distributions. More specifically, spectral detection is achieved by determining the azimuthal angle of the strongest focal point, while polarization detection is accomplished by synthesizing the intensity of focal points and the interference pattern of output vortex beams. Experimental results indicate the successful reconstruction for six discrete wavelengths, with the average relative polarization error ranging from 7.85% to 13%. Additionally, the metasurface exhibits excellent imaging and edge detection capabilities owing to the focusing properties and the generation of vortex beams, achieving an imaging resolution of up to 1.4-fold wavelength and offering a new solution for a wide range of applications.

Snapshot computed tomographic microscopic imaging spectrometer and its video-level tracking of poisonous Microcystis aeruginosa cells in mixed algae

Conventional microscopic spectral imaging suffers from extended scanning times across wavelength or spatial dimension. To improve capabilities of dynamic microscopic spectral imaging, we developed a snapshot computed tomographic microscopic imaging spectrometer (CTMIS) based on the zeroth and first orders dispersive diffraction of a two-dimensional grating. Utilizing the CTMIS-UNET reconstruction algorithm, we can reconstruct a spectral cube (541x541x26) for each frame of micro spectral imaging video. Experimental results demonstrate a sub-4 μm spatial resolution achievable through a 20x objective lens and a spectral resolution better than 10 nm among 450–700 nm, while maintaining spectral cosine similarities exceeding 0.9989 when comparing reconstructed spectra with ground truth data. Spectral imaging videos of four species of algae and mixed algae were captured under 10 ms exposure time using the CTMIS system. Leveraging the self-developed UNET-SI26 algae recognition network, precise identification and tracking of four types of algae and poisonous microcysts aeruginosa in mixed algae were conducted. The pixel-level recognition accuracy exceeds 95 %, while the accuracy for counting the numbers of different types of cells surpasses 85 %, offering an efficient and accurate spectral imaging method for real-time monitoring and early warning of harmful algae at the cellular level.

Fourier coded aperture transform rapid spectral imaging

The Fourier coded aperture transform hyperspectral imaging system (FCTS), which we proposed in our previous work, has excellent physical interpretability compared to other encoding methods. Due to the excellent energy concentration characteristic of Fourier encoding, it can achieve high-quality spectral imaging even under undersampling conditions. However, since the grayscale encoding method limits the system's sampling speed, we propose a binary sampling Fourier-encoded spectral imaging method. Besides, to address the significant errors of reconstructed spectral image at low sampling rates, we construct a spectral image enhancement network, which introduces spatial and channel attention modules to successively enhance the spatial and spectral information of the reconstructed spectral images. During the spectral image refinement and enhancement stage, band grouping is performed to alleviate the difficulty in feature extraction and make the training process more stable. The effectiveness of our proposed method has been validated on both public datasets and our own datasets. When the sampling times are reduced by an order of magnitude, on the three public datasets, the PSNR improved by 10.79 dB, the SSIM increased by 0.35, and the SAM decreased by 12.1° on average. On our own datasets, the PSNR improved by 12 dB, the SSIM increased by 0.24, and the SAM decreased by 6.5°.