ObjectiveThe accurate diagnosis of ophthalmic diseases largely depends on the comprehensive analysis of both structural and functional retinal features. Multimodal imaging techniques, such as multi-channel color fundus (MC) photography and fundus fluorescein angiography (FFA), provide complementary information. MC offers high-resolution anatomical details, whereas FFA highlights functional abnormalities like vascular leakage and non-perfusion areas (NPAs). However, existing deep-learning-based fusion methods have critical limitations. First, they fail to fully exploit the spectral properties of retinal tissues, which are essential for distinguishing subtle pathological features. Second, conventional approaches rely on color space conversion (e.g., YCbCr) to generate single-channel fusion results, leading to irreversible color distortion and the loss of multi-channel depth information. Third, the limited receptive fields of convolutional neural networks (CNNs) hinder the effective integration of multi-scale features, particularly for small but clinically significant structures such as capillaries (typically <5 μm in diameter) and microaneurysms. These challenges necessitate a novel fusion framework that preserves both high-frequency vascular details and low-frequency background textures while maintaining human visual consistency. This study addresses these gaps by proposing a diffusion-model-based fusion framework tailored for ophthalmic imaging, with the aim of enhancing diagnostic accuracy through high-fidelity multimodal integration.MethodsThe proposed framework integrates a denoising diffusion probabilistic model (DDPM) with frequency-adaptive dilated convolution (FA Conv) to achieve anatomically precise and perceptually consistent fusion of MC and FFA images. First, a four-channel input tensor is constructed by concatenating the three-channel MC and one-channel FFA images, bypassing color space conversion to prevent information loss. The DDPM learns the joint distribution of MC and FFA through a forward-reverse diffusion process (Fig. 2). In the forward phase, Gaussian noise is incrementally added to the input over 1000 timesteps, mapping the original data to a latent space. The reverse phase employs a U-Net-based denoising network with residual blocks and positional encoding to predict the noise at each timestep, effectively extracting the multi-scale diffusion features (Fig. 3). To address the tradeoff between bandwidth and receptive field, the framework incorporates Fourier decomposition and dynamic dilated convolution. The high-resolution features from the diffusion decoder are decomposed into four frequency bands using a discrete Fourier transform. Adaptive dilation rates are assigned per pixel based on high-frequency energy, enabling larger receptive fields for low-frequency regions (e.g., retinal background) and finer sampling for high-frequency structures (e.g., capillaries). The fusion weights are dynamically adjusted using a learnable frequency selection map, ensuring optimal integration of cross-modal features (Fig. 6). Finally, a multi-scale loss function combines gradient preservation, intensity alignment, and VGG-19 perceptual similarity to maintain color fidelity and structural coherence. The two-stage training strategy first optimizes the DDPM for noise prediction (Algorithm 1, Fig. 4) and then fine-tunes the fusion module with perceptual constraints (Algorithm 2, Fig. 7).Results and DiscussionsThe proposed method is evaluated on a dataset encompassing 75 eyes with 1500 image pairs. Qualitative comparisons (Figs. 8?11) highlight the superior performance of our method in preserving the intricate textures of the MC images and the pathological features of the FFA images. For instance, in cases of central serous chorioretinopathy, the fused images distinctly exhibit microaneurysms in the MC images alongside vascular leakage in the FFA images, offering a comprehensive depiction of the disease. Similarly, in retinal artery occlusion scenarios, the integrated images highlight obstructed blood vessels and changes in the surrounding tissue, aiding in precise diagnosis. Quantitative evaluations (Table 1) substantiate the advantages of our approach. Our method surpasses the leading fundus fusion techniques in multiple metrics, including entropy (EN, 6.669), standard deviation (SD, 67.497), correlation coefficient (CC, 0.899), multi-scale structural similarity index measure (MS-SSIM, 1.386), learned perceptual image patch similarity (LPIPS, 0.347), and Delta E 2000 (5.009). These results suggest that our approach produces fused images with richer informational content, heightened contrast, superior structural preservation, and more precise color representation. Furthermore, ablation studies (Table 2) are executed to evaluate the efficacy of the diffusion model and frequency-adaptive convolution. The findings reveal that omitting the diffusion model significantly compromises color accuracy, whereas eliminating the frequency-adaptive convolution reduces structural preservation. This highlights the pivotal role of both components in ensuring high-fidelity fusion of multimodal fundus images.ConclusionsThis paper introduces the first diffusion-based fundus image fusion framework capable of achieving high-fidelity multimodal fusion of MC and FFA modalities. By leveraging the multi-scale feature extraction capabilities of DDPM alongside the dynamic balancing mechanisms of frequency-adaptive convolution, the proposed method successfully integrates these two modalities while preserving both anatomical structures and lesion-specific information. This results in fused images that maintain visual consistency for human observers, making them more suitable for clinical diagnosis. These findings suggest that our proposed method has the potential to enhance the diagnostic accuracy and reliability in ophthalmology, particularly for diseases requiring multimodal analysis. Future research could explore the integration of additional retinal imaging techniques, such as optical coherence tomography angiography (OCTA) or three-dimensional (3D) modalities, to provide a more comprehensive visualization of the retina. Moreover, the proposed framework can be adapted to other medical imaging applications where multimodal image fusion is crucial for precise diagnosis and treatment planning.

ObjectiveDislocation, distortion, and honeycomb artifacts, common problems in confocal endomicroscopic images, disturb the pixel arrangement of images and obscure potential information, thus hindering further analysis of the target object in an image. Many studies have focused on solving one of these individual problems, such as correcting image dislocation or distortion, or simply studying methods to remove honeycomb artifacts. The methods of dislocation correction and distortion correction mainly include hardware correction or post-correction of images using specific algorithms. Hardware methods increase the complexity of the system, whereas software algorithms are relatively time consuming. Methods to remove honeycomb artifacts include interpolation, filtering, image mosaicing, and deep learning; however, related research usually does not consider the impact of image dislocation and distortion. The applicability of some of these methods may be reduced when multiple problems exist simultaneously. A simple combination of multiple methods in a specific order is a feasible solution; however, it may lead to low image reconstruction efficiency. Therefore, developing efficient integrated solutions for mitigating the effects of dislocations, distortions, and honeycomb artifacts in confocal microscopy images is crucial. In this study, we proposed a confocal endomicroscopy image reconstruction method that efficiently combines dislocation correction, distortion correction, and honeycomb artifact removal to overcome multiple problems observed in confocal endomicroscopic images and recover potential information in images.MethodsThe proposed method consists of calibration and reconstruction stages. In the calibration stage, the proposed method obtained the dislocation of pixels, reference coordinates of the fiber core center, and the relative transmittance of the fiber core from a uniform fluorescence image. In addition, distortion correction and Delaunay triangulation were performed on the fiber center coordinates during the calibration stage, and the barycentric coordinates of each pixel in the images were calculated. Calibration information was used to assist in the reconstruction of subsequent images. In the reconstruction stage, the entire dislocation of the image was corrected according to the dislocation determined in the calibration stage, and the actual gray value of the fiber core center was obtained by searching for the maximum gray value. The gray value of the fiber core center was further corrected by dividing it by the relative transmittance to eliminate the influence of the difference in the transmission characteristics of each fiber core. Then, the corrected gray values were individually mapped to Delaunay triangular grids, and the honeycomb artifacts were removed by barycentric interpolation to obtain the final reconstructed image. We studied the influence of dislocation, distortion, and honeycomb artifacts on the reconstructed image through simulation and quantitatively evaluated the reconstruction effect of our method. In practical experiments, we captured pictures of USAF-1951 resolution targets and reconstructed the images to verify the effectiveness of our method. In addition, we captured the images of plant leaves and animal fat, and reconstructed them to evaluate the effect of our method on biological samples.Results and DiscussionsThe simulation results show that dislocation and core transmittance differences (Fig. 5) lead to abnormal gray values in the reconstructed image. Different regions in an image have different degrees of distortion, which cannot be corrected by horizontal image scaling. Compared to other methods, our method effectively eliminates abnormal gray values, corrects distortion, removes honeycomb artifacts, and obtains the highest peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) (Table 1). The reconstruction results of the USAF-1951 resolution target in the experiments (Fig.8) show that the method presented in this study recovers the structural information of the resolution target. Compared to the simple combination of dislocation correction, distortion correction, and Gaussian filtering, the brightness and contrast of the reconstructed images obtained using our method are higher (see Fig.8 (e)), and the contrast-to-noise ratio (CNR) is improved by approximately 100% (Table 2). In the reconstructed image, the approximate structure contour of the second element of the eighth group can be recovered. However, the information in the reconstructed image cannot be identified by adopting a simple combination of dislocation correction, distortion correction, and Gaussian filtering. This result implies that our method can effectively protect and utilize the information contained in the original image during the reconstruction process, allowing the recovery of the potential structural features. The experimental results of the biological samples (Fig. 9) show that our method effectively reconstructs the structure of the biological samples, even though the original image contains dislocation, distortion, and honeycomb artifacts, allowing identification of the characteristic information of the biological samples in the image.ConclusionsWe presented a comprehensive method for confocal endomicroscopic image correction and reconstruction. This method efficiently combines dislocation correction, distortion correction, and honeycomb artifacts removal, and avoids repeated dislocation evaluation and distortion correction through one-time calibration. The experimental results show that the proposed method can effectively eliminate image distortion and honeycomb artifacts introduced by fiber bundles, thus improving the image quality and recovering the structural details of objects in images. Compared to Gaussian filtering combined with distortion correction, this method can utilize the information of the original image more effectively, and the reconstructed image has a higher contrast and less noise.

ObjectiveThis study utilizes a dual-wavelength photoacoustic microscopic imaging system to effectively separate Evans blue dye within tissues. We establish a localized blood-brain barrier (BBB) disruption model in mice and conduct in vivo imaging investigations. Our results indicate that, even with an intact skull, the system can clearly present cerebral vascular structures and accurately identify the areas of BBB damage, demonstrating its significant advantages in the study of cerebrovascular diseases. With its high resolution and sensitivity, photoacoustic microscopy holds great promise for applications in the mechanistic research, early diagnosis, and treatment monitoring of brain diseases, particularly those affecting the brain vasculature and the blood-brain barrier, positioning it as a potential novel diagnostic tool.MethodsA dual-wavelength (532 nm/610 nm) photoacoustic microscope is established, and a localized BBB disruption model in mice is induced using a hot water stimulation method. Initially, the photoacoustic microscope is utilized to observe the BBB leakage process in the mouse ear. Subsequently, imaging is conducted on both experimental and control groups exhibiting BBB disruption, followed by quantitative analysis.Results and DiscussionsPhantom experiments validate the feasibility and accuracy of the spectral separation algorithm, as evidenced by the results shown in Fig. 3. The permeability detection experiment of ear blood vessels confirms the system capability to detect Evans blue extravasation from the blood vessels (Fig. 4). Tests on BBB integrity show that, even with an intact skull, the system can clearly image brain vascular structures with distinct regional boundaries, demonstrating its effectiveness in brain vascular imaging and assessing BBB integrity (Fig. 5).ConclusionsWe successfully develop a dual-wavelength photoacoustic microscopy imaging system based on 532 nm and 610 nm, achieving high-resolution imaging of mouse brain microvascular structures while preserving the skull. By incorporating spectral separation algorithms, this study quantifies the changes in BBB integrity. Photoacoustic imaging (PAI) shows great potential for researching brain diseases, particularly those affecting blood vessels and BBB, and is anticipated to become a vital tool in the fundamental research of brain disorders.

ObjectiveIn optical coherence elastography (OCE) based on an elastic wave, the phase velocity of the elastic wave is closely related to the Young modulus of tissues. The elastic properties of tissues can be derived by calculating the phase velocities of elastic waves at different frequencies to obtain the phase velocity dispersion curves. A high-frequency elastic wave plays a significant role in tissue elasticity characterization. On one hand, the spatial resolution of elastography is influenced by the elastic wave wavelength, where a higher frequency enables a higher spatial resolution. On the other hand, the wave velocity dispersion over an extended frequency range enables the extraction of detailed mechanical properties including the depth-dependent variation of elasticity and internal stress. However, the conventional two-dimensional Fourier transform (2D-FT) methods exhibit limitations in processing high-frequency elastic waves, because they are susceptible to noise interference, leading to the reduced accuracy in phase velocity estimation and consequently affecting the precise characterization of tissue mechanical properties like elasticity. In this work, we propose a phase velocity estimation method combining the generalized Stockwell transform with the Radon transform (GST-RT). The derived phase velocity dispersion curve is used to calculate the Young modulus. The generalized Stockwell transform is applied to perform a time-frequency analysis on the spatiotemporal displacement data. Subsequently, the Radon transform is applied to identify the elastic waves within the time-frequency-transformed spatial data at each frequency and calculate their phase velocities, thereby enabling the determining of the Young modulus. The feasibility of this method is validated through simulation data, and the robustness is evaluated in the presence of additive Gaussian white noise conditions. Additionally, the experimental analysis of agar phantom is conducted to assess the performance of the proposed method in practical applications.MethodsThe flowchart of the GST-RT method is shown in Fig. 1. First, the two-dimensional spatiotemporal displacement data are obtained from the optical coherence tomography (OCT) data with a phase-sensitive detection algorithm. Subsequently, the generalized Stockwell transform is used to map the spatiotemporal displacement data into a three-dimensional time-frequency-spatial domain. The Radon transform then converts the time-spatial data from the frequency domain to the angle-normal distance space. To validate the method, the GST-RT and 2D-FT methods are applied to elasticity simulation data under noise-free and noisy conditions (signal-to-noise ratios of 35 dB, 30 dB, 25 dB) for phase velocity estimation, and the percentage errors in phase velocity and Young modulus for both methods are calculated. Additionally, the agar phantom elasticity measurement experiments are conducted to validate the capability of the GST-RT method for evaluating the elastic properties of real samples. The Young modulus values calculated by both the GST-RT and 2D-FT methods are compared with those obtained from the mechanical compression tests.Results and DiscussionsThe simulation reveals that both methods achieve the phase velocity percentage errors below 1% and the Young modulus errors under 2% in noise-free conditions. The GST-RT demonstrates a robust performance even with added Gaussian white noises, exhibiting the phase velocity errors consistently within 1% and the Young modulus errors below 2%. In contrast, the 2D-FT method displays significantly higher errors, reaching up to 9.62% for phase velocity and 12.35% for Young modulus under noisy conditions, thereby confirming the superior robustness of GST-RT. The agar phantom experimental results demonstrate that the percentage errors in Young modulus calculated using the GST-RT method are consistently controlled within 2%, outperforming those of the 2D-FT method which exhibits a maximum error of 6.16%. These findings align with the simulation results, further validating the reliability and accuracy of the GST-RT method in analyzing experimental data.ConclusionsThis study proposes a phase velocity estimation method integrating the generalized Stockwell transform and the Radon transform. The generalized Stockwell transform captures high-frequency information while mitigating noise interference in calculations, while the Radon transform enables precise identification of elastic waves, thereby improving the accuracy of phase velocity estimation and enhancing the reliability of tissue elasticity characterization. Simulations validate the feasibility of the method. Comparative analyses under noise-free and noisy conditions demonstrate that the GST-RT method keeps Young modulus percentage errors below 2% in both conditions, highlighting its robust noise resistance. Agar phantom experiments confirm the method practicality. Compared to the conventional 2D-FT method, the GST-RT method demonstrates enhanced robustness in elastic simulation data contaminated with Gaussian white noises, while also extending the bandwidth of phase velocity analysis in experimental results. This approach provides a reliable quantitative and analytical method for tissue elasticity characterization.

ObjectiveTumor organoids, as novel in vitro tumor models, hold significant value in tumor biology research and personalized drug sensitivity assessment. However, existing methods relying on manual seeding and destructive endpoint testing are limited by the lack of dynamic monitoring capabilities and the requirement for high sample homogeneity. This study aims to develop a non-destructive, dynamic analysis framework for tumor organoids based on 3D optical coherence tomography (OCT) and deep learning, enabling precise segmentation, morphological characterization, and growth analysis of organoids to assess drug responses efficiently.MethodsWe presented a label-free OCT-based framework that includes deep learning-driven segmentation, 3D morphometric quantification of individual organoids, and growth rate modeling of organoid clusters. To tackle 3D discontinuities in organoid segmentation, we introduced a novel parallel encoder architecture, ParaSAM2CNN, which integrates ResNet's deep feature extraction with SAM2's multiscale feature capture, enabling automated and precise segmentation (Dice coefficient: 0.8026). An adaptive surface roughness quantification algorithm was developed to enable longitudinal, high-throughput, multidimensional morphological characterization of organoids. Unsupervised clustering was applied to categorize organoid phenotypes, while principal component analysis (PCA) was employed to elucidate correlations among morphological parameters, growth dynamics, and drug response. A growth level model for organoid clusters was established and validated against traditional destructive ATP-based assays, showing high consistency (90.45%).Results and DiscussionsThe proposed framework demonstrates significant advantages in the non-destructive analysis of tumor organoids and drug response assessment. The ParaSAM2CNN model achieves superior segmentation performance compared to other state-of-the-art models, with improved precision and Jaccard index. The adaptive surface roughness algorithm provides detailed morphological characterization, capturing changes in organoid structure under drug treatment, such as the transition from cystic to solid phenotypes. The growth level model shows a high correlation with ATP test results, confirming its reliability in assessing organoid growth and drug sensitivity. This framework not only provides a non-invasive alternative to traditional endpoint testing but also offers a transformative potential for drug screening and personalized therapy optimization based on patient-derived tumor organoids.ConclusionsThis study presents a significant advancement in the analysis and application of tumor organoids for cancer research and treatment. By integrating OCT imaging with deep learning and machine learning techniques, we have developed a comprehensive and non-destructive evaluation framework that accurately assesses organoid growth and drug responses. This method has the potential to revolutionize traditional drug screening and sensitivity testing methods, providing a new technological platform for cancer research and personalized medicine. The high consistency with ATP testing highlights the model’s potential as a reliable and non-invasive tool for cancer treatment.

SignificanceParticle sorting plays a crucial role in various fields, including biomedicine and physical chemistry. Traditional sorting techniques, such as those based on acoustics or magnetism, are limited by factors such as low resolution, restricted throughput, and poor selectivity. Optical sorting, which utilizes optical forces or other related optical techniques, has emerged as a powerful alternative. Specifically, optical-force-based sorting exploits differences in the optical forces acting on particles or cells within a light field, which are driven by physical properties such as their shape, size, chirality, or polarizability. Compared to conventional techniques, optical sorting offers significant advantages, including high resolution, non-invasiveness, and broad applicability.The optical tweezers technique, which uses optical forces to manipulate micro- and nano-objects, was pioneered by Arthur Ashkin in the 1970s and 1980s. Since then, optical tweezers have become invaluable tools for capturing and manipulating microscopic particles, opening new avenues of research in biomedicine, physics, and chemistry. In 1997, Steven Chu, Claude Cohen-Tannoudji, and William D. Phillips were awarded the Nobel Prize in Physics for their work on atomic cooling using optical forces. In 2018, Ashkin was awarded half of the Nobel Prize in Physics for his groundbreaking contributions to the development of optical tweezers and their applications in biomedicine.Conventional optical sorting schemes rely on differences in the magnitude and direction of optical radiation and gradient forces acting on particles with various shapes, sizes, chiralities, or polarizabilities. However, these techniques are limited by directional constraints and degrees of freedom, which can compromise the sorting accuracy. In the past decade, two novel optical force mechanisms have been discovered: the optical pulling force (OPF) and optical lateral force (OLF). These forces offer additional degrees of freedom for sorting and have demonstrated significant potential for high-precision and chiral particle sorting. Each of the optical forces (radiation, gradient, pulling, and lateral) displays unique mechanical properties, enabling the manipulation and sorting of nanoscale particles.In addition to optical-force-based sorting, several optically related techniques, such as fiber optic tweezers, fluorescent labeling, and artificial intelligence, provide innovative approaches for sorting particles and cells. Fiber optic tweezers have transformed optical sorting into a cost-effective technology because dual or single optical fibers can be used to efficiently sort small particles or cells. Fluorescent labeling enables precise identification and automated tracking by targeting unique structures within particles or cells. Artificial intelligence facilitates the high-resolution processing and automated analysis of particle images. Current research in optical sorting focuses on developing novel technologies to enhance the efficiency and precision when sorting particles with distinct physical properties.Optical sorting technology plays an important role in many fields, such as material science and biomedical fields. In the biomedical field, it is increasingly used in areas such as genomics, drug discovery and development, proteomics, single-cell analysis, and clinical therapeutics. Optical diagnostics, which is based on the principle of optical sorting, has become one of the most important tools in biomedicine. Its high sensitivity to the physical properties of particles enables the precise detection of small changes in cell morphology and biochemistry, offering promising prospects for clinical applications and therapies.Progress This paper reviews the progress in optical sorting research and discusses the topic in the following orderoptical sorting based on optical forces, sorting using other optical technologies, and the applications of optical sorting technologies across various fields (Fig. 1). The review begins by introducing the theoretical foundations of optical sorting based on optical forces and providing an overview of the research progress in conventional optical forces for sorting applications (Figs. 2 and 3). Next, the mechanisms of novel optical forces such as OPFs and OLFs and their applications in sorting are discussed (Figs. 4 and 5). The review then explores other optical technologies used for sorting, including fiber optic tweezers, fluorescent labeling, and artificial intelligence. Finally, the paper highlights the value of optical sorting technologies in material science and biomedicine, and envisions the emergence of new optical sorting techniques and potential applications in the future.Conclusions and ProspectsOptical sorting technology based on the principle of optical tweezers has significantly advanced the manipulation and sorting of particles and cells, driven by the continuous development of optical tweezers technology.The core principle of optical sorting relies on the differences in the magnitudes and directions of the light forces acting on particles or cells with distinct physical properties. Both conventional and novel optical forces play crucial roles in precisely sorting target particles and cells. Furthermore, the integration of additional optical technologies has broadened the application scope and improved the practical efficiency of optical sorting. For example, fiber optic tweezers provide a high-precision, flexible, and cost-effective sorting method (Fig. 6); fluorescent labeling technology enhances the imaging clarity, photostability, and spectral resolution of particles or cells; and image processing combined with artificial intelligence enables the efficient identification and sorting of particles and cells (Fig. 7).With the ongoing technological advancements and increasing demands across various fields, optical sorting technology is poised to evolve further, driven by emerging innovations. The integration of artificial intelligence algorithms with optical sorting is expected to address current challenges, such as improving the efficiency of high-throughput sample processing and enabling real-time data analysis, thereby facilitating a more efficient and accurate sorting process.Looking ahead, the development of super-resolution microscopy and emergence of new optical materials are anticipated to bring significant breakthroughs to optical sorting technology. Super-resolution microscopy is expected to enhance image resolution, while new optical materials may exhibit unique interactions with different sorting objects. By optimizing these technologies, the applications of optical sorting in biomedicine, materials science, and nanotechnology are likely to expand, paving the way for it to have a more extensive impact in the future (Fig. 8).

ObjectiveAlzheimer’s disease (AD) represents the predominant form of dementia, constituting 60%?80% of all dementia cases. The aggregation and oligomerization of β-amyloid (Aβ) in the brain represent hallmark pathological features of AD. While cerebrospinal fluid (CSF) analysis remains the current gold standard requiring a lumbar puncture, plasma-based Aβ detection presents a less invasive and more accessible diagnostic approach. Early plasma-based Aβ detection enables timely intervention and potentially decelerates AD progression, rendering this research significant for clinical and scientific communities. Plasma Aβ level detection is essential for early diagnosis; however, the extremely low mass concentrations of Aβ40 (270?290 pg/mL) and Aβ42 (30?40 pg/mL) present significant detection challenges. Ultrasonic cavitation-based β-amyloid fibril proliferation technology enables protein fibril amplification using trace seeds, and when integrated with fluorescent probe detection technology, facilitates trace β-amyloid detection and fibril aggregation process observation. However, high-power ultrasonic processing and varying incubation periods induce sample temperature fluctuations between 25 ℃ and 40 ℃. Since fluorescent probe excitation and emission spectra demonstrate temperature sensitivity, these variations can affect fluorescence intensity measurements, potentially compromising the accuracy of β-amyloid concentration determination and diagnostic result reliability.MethodsWe selected Thioflavin T (ThT) as the fluorescent detection probe, an established fluorescent probe that selectively binds to Aβ aggregates to form ThT-amyloid fibril complexes. When excited by a 440 nm light source, this complex emits fluorescence at 480 nm, with the fluorescence signal intensity linearly correlating to the Aβ aggregate content in the sample. To address temperature fluctuation effects from high-power ultrasonic processing on fluorescence intensity detection, we examined the temperature sensitivity of excitation efficiency for light sources with varying spectral linewidths, ultimately selecting an optimally linewidth-configured excitation source. Given that ThT’s fluorescence emission spectrum exhibits a red shift with increasing sample temperature, and to eliminate non-fluorescent stray light interference, we investigated fluorescence spectral filtering techniques and examined temperature effects on detected fluorescence intensity using filters of varying bandwidths. To satisfy high fluorescence detection sensitivity and linearity requirements, we implemented single-photon detection technology. Considering the low Aβ sample concentrations and resultant weak fluorescence emissions, we analyzed system stray light sources and developed a combined spectral filtering-based stray light suppression technique to attenuate stray light below the single-photon detector’s equivalent noise power.Results and DiscussionsRegarding the impact of the excitation light source on excitation efficiency, we found that when the spectral linewidth of the excitation light source was 2 nm, the relative change in the excitation spectrum overlap integral decreased by approximately 67% compared to a light source with a commonly used 10 nm linewidth [Fig. 3(b)], indicating that the excitation light absorption efficiency is less sensitive to sample temperature changes. Regarding the effect of using filters with different bandwidths on the temperature sensitivity of detected fluorescence intensity, the study found that when using a fluorescence filter with a bandwidth of 40 nm (passband of 460 nm to 500 nm), the detected fluorescence intensity was least sensitive to sample temperature changes [Fig. 5(b)]. The combined spectral filtering-based stray light suppression technique ultimately controlled the background noise of the detection device to 7 photons (Fig. 17). Experimental measurements on plasma samples with different Aβ concentrations showed that the temperature sensitivity of the detected fluorescence intensity was better than 2.1% [Fig. 18(b)]. Further experiments, by adjusting the output power of the light source to simulate the fluorescence intensity of plasma samples with different Aβ concentrations, demonstrated that within a dynamic range of 957358, the linear fitting coefficient of determination (R2) between the count value and the detected fluorescence intensity reached 0.9996 [Fig. 20(b)].ConclusionsThe developed fluorescence detection device demonstrated robust performance in experimental measurements of plasma samples with varying Aβ concentrations. The measured fluorescence intensity count values exhibited strong linear correlation with Aβ concentration, achieving a linear fitting coefficient of determination of 0.9996. The relative standard deviation across 50 measurements of identical concentration samples remained below 1.3%, indicating excellent measurement stability. Additionally, utilizing a light source with adjustable output power to simulate plasma sample fluorescence intensity at different Aβ concentrations revealed the relationship between count values and fluorescence emission powers. The experimental results demonstrated a linear counting dynamic range of 957358, maintaining a linear fitting coefficient of determination of 0.9996 throughout the entire range. These findings confirm that the developed Aβ concentration fluorescence detection technology achieves high-sensitivity weak fluorescence signal detection, satisfying the requirements for low concentration Aβ detection in plasma samples and advancing early Alzheimer’s disease diagnosis capabilities.

ObjectiveNon-invasive, timely and accurate detection is of great significance for the early diagnosis and treatment of tumors. Raman spectroscopy has shown good application prospects in the field of tumor detection due to its advantages of non-destructive acquisition, high sensitivity and rapid detection. Several studies have given the principles of Raman spectroscopy for detecting cancerous tissues, however, this technique still has the limitations of weak signals and insufficient penetration depth and faces the challenge of improving its ability in detecting subcutaneous deep tumor signals for clinical applications. Spatially offset Raman spectroscopy (SORS) is a deep-penetration Raman spectroscopy, which reduces the signal interference in the surface layer in order to effectively obtain the spectral information of deep samples by physically shifting the spectral acquisition point with a certain distance laterally relative to the excitation point. However, most of the studies indicate that the detection depth of this technique in biological tissues is limited to 2 mm, far from being sufficient for the detection of deep-seated tumors, and the quantitative relationship between the detection depth and the spatial offset distance (Δd) lacks the support of experimental data. In this study, we conduct experiments based on a self-developed fiber optic Raman probe, and investigate the maximum detection depth of the fiber optic Raman probe detection technique by respectively using two acquisition modes of transmission and reflection. Based on the both modes, the SORS is introduced, and the quantitative relationship between Δd and the optimal detection depth is established. This study provides an experimental data support for the application of SORS in clinical tumor diagnosis, and provides a technical reference for the further optimization of Raman spectroscopy.MethodsRaman spectra of pork adipose tissue (PAT) are collected under various experimental conditions based on a self-developed fiber optic Raman probe. First, the experimental optical paths of transmission and reflection modes are constructed (Fig. 2), then the PAT is cut into 3 mm-thick slices, and the samples are stacked layer by layer for spectral acquisition in both transmission and reflection modes until the maximum detection depth is obtained. Then, after obtaining the detection depths in the both Raman spectral acquisition modes, the SORS is introduced, and the experiments are carried out under different sample thicknesses with the Δd increment of 1 mm and the offset ranging from 1 mm to 6 mm, to obtain the Raman spectral data with different Δd in the both modes. Finally, the experimental spectral results are pre-processed and analyzed to summarize the experimental results.Results and DiscussionsThe spectral data show a negative correlation between feature band Raman intensity and tissue thickness. In the transmission and reflection acquisition modes, the maximum acquisition depths of about 30 mm and 6 mm can be achieved, respectively (Figs. 3 and 4). On this basis, the SORS experiment is performed, and the Raman intensity shows a certain attenuation trend with increasing Δd at 3 mm and 6 mm sample thicknesses in the transmission mode (Fig. 5), which indicates that the photons in this thickness range are less likely to diffuse laterally and more likely to penetrate the sample along a straight line. This trend is especially obvious at 3 mm, and the Raman scattering intensity is more uniform with increasing Δd at sample thicknesses from 9 mm to 30 mm, which discloses that the best results are obtained in the transmission mode with no offset acquisition. In the reflection mode, a layered model with PAT in the surface layer and polytetrafluoroethylene (PTFE) in the deep layer is used for the spectral acquisition, and both signals attenuates with increasing Δd (Fig. 6), but the PTFE signal shows a tendency of enhancing and then weakening relative to the PAT signal with increasing Δd (Fig. 7). The relatively strongest signal of PTFE is obtained from the samples with thicknesses of 3 mm and 6 mm, corresponding to Δd of 4 mm and 5 mm, respectively. This indicates that the SORS under the reflection mode can effectively avoid surface signal interference and acquire deep tissue signals, and the optimal Δd is positively correlated with sample thickness.ConclusionsThe potential of Raman spectroscopy based on a novel fiber optic probe for deep tissue detection is systematically investigated. On this basis, the application of SORS under the transmissive mode indicates that the transmissive mode does not possess the ability to acquire tissue information layer by layer although it has a deeper tissue penetration effect. The reflection mode SORS achieves a deeper acquisition depth (6 mm) than previous studies, validating the Monte Carlo simulation based prediction by Mosca et al., and a quantitative relationship between the detection depth and the optimal Δd is established. It confirms the ability of the SORS under the reflective mode to effectively acquire deep/stratified sample signals. This study not only demonstrates the superior performance of the self-developed fiber optic Raman probe in Raman spectroscopy, but also provides an important experimental basis and a technical reference for the further optimization of the SORS, which is of great significance in promoting the application of Raman spectroscopy in clinical diagnosis.

ObjectiveIn recent years, Raman distributed fiber sensors (RDFS) has been applied in various fields. Inadequate spatial resolution can lead to significant errors in measurements. Some application scenarios, such as pipeline leakage monitoring and power grid safety monitoring, require high spatial resolution in the millimeter range. Therefore, enhancing the spatial resolution of RDFS is crucial. Various schemes have been proposed by researchers to optimize the spatial resolution of RDFS. In this paper, a differential pulse-pair detection scheme for RDFS is proposed. This scheme avoids the problem that it is difficult to balance the spatial resolution and the sensing distance of conventional RDFS. In addition, this scheme can also synchronously achieve the measurement of fiber loss anomalies, and achieve the dual-parametric simultaneous measurement of fiber loss points and temperature.MethodsThe experimental setup (Fig. 2) includes a pulsed laser, an erbium-doped fiber amplifier (EDFA), a wavelength division multiplexer (WDM), an avalanche photodetector (APD), a high-speed data acquisition card (DAQ), an arbitrary waveform generator (AWG), and a multimode fiber (MMF). The pulsed laser produces pulses with a central wavelength of 1550 nm. These pulses pass through an EDFA for power amplification before entering the WDM. The Raman scattering signal produced in the sensing fiber is first separated by the WDM and then fed into the APD. The received Raman scattering signal is converted into an electrical signal by the APD. The APD bandwidth is 200 MHz. This signal is subsequently collected by the DAQ and finally input into the computer for demodulation. The AWG produces periodic pulsed electrical signals to synchronize the operating time of the pulsed laser with that of the DAQ. The fbier under test (FUT) is set at the end of the multimode fiber.Results and DiscussionsLoss point detection experiments depict that the dual-pulse differential detection scheme improves the accuracy of identifying two loss points compared to the single pulse scheme. Two sharp peaks are clearly observed. The conventional scheme fails to identify two loss points, in contrast to the dual-pulse differential detection scheme. Temperature detection experiments depict that in a case of a short FUT, the superiority of the dual-pulse differential detection scheme in terms of temperature measurement accuracy becomes evident as the FUT temperature increases. The temperature error of the dual-pulse differential detection scheme is less than 0.6 ℃. The experiments depict that the dual-pulse differential detection scheme improves the temperature measurement accuracy. Temperature resolution experiments depict that with the increase of sensing distance, the Raman scattering signal is gradually weakened, and the signal to noise ratio (SNR) deteriorates. As the sensing distance continues to increase, the standard deviation of temperature deteriorates sharply.In the dual-pulse differential detection scheme, the differential equivalent pulse width (obtained by subtracting input pulses with different widths) is a crucial factor that affects the system SNR and spatial resolution. The longer pulse leads to an unstable amplification power of the EDFA, making it difficult to maintain a constant peak power for pulses with differential pulse widths, which in turn affects the final demodulation results. Figure 8 depicts the relationship between temperature resolution and equivalent pulse width at varying sensing distances. As the equivalent pulse width increases, the temperature accuracy increases. An increase in the equivalent pulse width does not result in an infinite increase in temperature accuracy. The wider the equivalent pulse width, the less obvious the optimization of temperature measurement accuracy. The experimental results (Fig. 9) depict a gradual decrease in the standard deviation of temperature over the same sensing distance with the increase of equivalent pulse width.ConclusionsThis paper proposes a differential dual-pulse demodulation method based on RDFS, aiming to address the technical challenge of limited spatial resolution in traditional kilometer-range RDFS systems. The proposed scheme enables simultaneous measurement of temperature and loss parameters through differential pulse pair demodulation. Experimental results demonstrate that the sensing system achieves a spatial resolution of 0.56 m under a sensing distance of 5.6 km, with a temperature standard deviation of less than 1 ℃. Furthermore, it accurately identifies two loss event points spaced 0.4 m under a sensing distance of 8.0 km. Additionally, this scheme achieves synchronous enhancement of dual-parameter sensing capability and spatial resolution without modifying the system architecture. Its primary advantages lie in its simplicity in design and low hardware cost, demonstrating significant application value in engineering scenarios such as pipeline micro-leakage monitoring and power grid security early warning.

ObjectiveSpectral imaging technology integrates spectral information with spatial data to achieve a multidimensional representation of target objects and is widely utilized in various fields, including agricultural monitoring, biomedical diagnosis, industrial inspection, and remote sensing. The snapshot spectral cameras, as innovative form of spectral imaging technology, overcome the limitations of traditional spectral cameras that require point-by-point or line-by-line scanning, enabling the acquisition of multi-channel spectral data in a single exposure. They have advantages such as compact size, low cost, and strong real-time capabilities. However, the insufficient transmittance of their core component—the multispectral filter array—significantly constrains their imaging performance, severely impacting the quality of spectral imaging. Therefore, it is essential to explore real-time spectral image enhancement methods for snapshot spectral cameras to mitigate the effects of transmittance on imaging quality.MethodsThis study presented a real-time spectral image enhancement method for snapshot spectral cameras, rooted in the principles of Retinex theory. Initially, an imaging model tailored for snapshot spectral cameras was formulated based on the Retinex theory. Subsequently, the homomorphic filtering technique was optimized in accordance with this model. Finally, a real-time spectral image enhancement method was developed that integrates the advantages of both the model and the improved homomorphic filtering approach. To evaluate the overall performance of the proposed method, comparative experiments were conducted using power-law (gamma) transformation enhancement, logarithmic transformation enhancement, and the proposed method across two different snapshot multispectral cameras operating in distinct imaging spectral bands.Results and DiscussionsThe spectral image processing results obtained with the proposed method demonstrate the highest brightness. This method not only effectively enhances the overall brightness and contrast of the images but also preserves edge details and texture features, resulting in superior visual quality (Fig. 4 and Fig. 6). The proposed method achieves the best performance across four evaluation metrics, significantly increasing image entropy. Specifically, it enhances the average gradient, image standard deviation, and spatial frequency by approximately six times compared to the original images (Fig. 5 and Fig. 7). The average processing time for the proposed method is only 47.1% of that for the power-law transformation enhancement method, at just 15.6 ms, enabling real-time processing of spectral images from snapshot spectral cameras with a frame rate of up to 64 frame/s (Table 3). The results produced by the proposed method not only exhibit high image quality and rich detail but also significantly surpass those of other methods and the original images across various metrics. Moreover, this method features a short processing time and demonstrates excellent robustness, making it well-suited for snapshot spectral cameras operating across different imaging spectral bands.ConclusionsThis study introduces a real-time spectral image enhancement method for snapshot spectral cameras. Initially, an imaging model tailored for snapshot spectral cameras was developed based on Retinex theory. Subsequently, the homomorphic filtering method was improved in alignment with the imaging model. Finally, a real-time enhancement technique for snapshot spectral cameras was designed based on these advancements. Experimental results indicate that the method proposed in this study not only achieves optimal visual effects but also yields the best quantitative outcomes. It effectively preserves spectral information, demonstrates high spectral fidelity, requires short processing times, and exhibits robust performance, thereby enabling real-time spectral image enhancement for snapshot multispectral cameras. Furthermore, this method provides a universal imaging physical model and an efficient processing approach for snapshot spectral cameras utilizing MSFA (multispectral filter array), establishing a foundation for the broader adoption of snapshot spectral cameras in diverse operational environments and application domains in the future.

ObjectiveThis study addresses the challenge of generating high-quality, visible-to-blue supercontinuum (SC) spectra using GHz-repetition-rate femtosecond laser frequency combs. While GHz combs offer advantages in mode spacing and system integration, especially for astronomical spectrograph calibration and precision metrology, their low single-pulse energy limits efficient spectral broadening. To overcome this, we develop and experimentally demonstrate a pre-chirped, dual-stage nonlinear fiber amplification scheme, achieving SC generation from 440 nm to 1500 nm at nJ-level pulse energies. The results provide a stable, broadband light source for advanced GHz frequency comb applications and pave the way for future extension into violet and ultraviolet (UV) regimes.MethodsThe experiment employs a 1 GHz mode-locked fiber laser as the seed source, producing 54 fs pulses with a 33 nm bandwidth. A portion of the output is amplified using a two-stage nonlinear fiber amplifier. The first stage, based on a single-mode polarization-maintaining (PM) fiber, boosts the power from 22 mW to 146 mW. The second stage, using a double-cladding PM fiber, further increases the output to 1.93 W. Pre-chirp control and precise dispersion management enable pulse compression to 110 fs and enhance nonlinear interactions (Fig. 4 and Table 1). The compressed pulses are injected into a tapered photonic crystal fiber (PCF), engineered to shift the zero-dispersion wavelength and support efficient supercontinuum (SC) generation extending into the blue. The resulting SC spans 440?1500 nm. Spectral characteristics are measured using optical spectrum analyzers (Fig. 5), and the SC stability is assessed through long-term spectral recording and power monitoring over 6 h (Fig. 6).Results and DiscussionsThe experiment successfully generates a broadband SC spectrum covering 440 nm to 1500 nm with an average power of 1.5 W and a pulse energy of 1.5 nJ. The compressed pulses exhibit a 3 dB bandwidth of 28.8 nm and a pulse duration of 110 fs (Fig. 4). The SC spectrum shows a uniform power distribution across the visible range, with the blue region (440?550 nm) achieving over 20 mW power (Fig. 5). Stability measurements over 6 h demonstrate minimal power fluctuation (<1 mW) and consistent spectral profiles (Fig. 6). Notably, increasing the pump power to 11 W extends the spectrum to 420 nm but introduces a dip in the 570?700 nm range due to soliton self-frequency shift (SSFS) effects. These results highlight the effectiveness of the proposed amplification and spectral broadening strategies in achieving high-quality SC generation under low-energy conditions.SC spanning 440 nm to 1500 nm is achieved using a GHz repetition-rate system at pulse energies of only 1.5 nJ and pulse durations of 110 fs after compression. The visible portion (>50 mW), including 20 mW in the 440?550 nm blue region, exhibits spectral flatness with intensity variation within 20 dB [Fig. 5(a)]. The system second-order dispersion (SOD) is compensated to near zero, while the third-order dispersion (TOD) is self-compensated through nonlinear phase accumulation during amplification. As a result, the compressed pulses exhibit high peak powers, minimal sidelobes, and clean temporal profiles [Table 1 and Fig. 4(b)]. As pump power increases, the appearance of spectral dips (e.g., 570?700 nm) is attributed to SSFS, which red-shifts the pulse center wavelength and disrupts the phase-matching conditions for dispersive wave generation in this band. This mechanism highlights the importance of managing soliton dynamics and dispersion design for SC uniformity [Fig. 5(b)]. The visible (440?900 nm) SC output power remains stable within <1 mW fluctuation over 6 h continuous operation at 9 W pump power. Spectral shape and peak positions show no significant drift, demonstrating the system thermal and mechanical stability for metrology applications (Fig. 6).ConclusionsThis work presents an effective strategy for generating broadband supercontinuum light from a GHz-repetition-rate femtosecond fiber laser using pre-chirped, dual-stage nonlinear amplification. Despite the low pulse energy (~1.5 nJ), the system delivers 110 fs pulses and achieves spectral broadening from 440 nm to 1500 nm, with ~20 mW power in the blue region. The resulted spectrum is flat, stable, and well-suited for high-precision applications such as astronomical spectrograph calibration. These results confirm the viability of nonlinear amplification and dispersion control at high repetition rates and establish a scalable approach for extending GHz frequency combs into the violet and ultraviolet. This work provides a solid technical foundation for advancing GHz comb systems in precision metrology and broadband spectroscopy.

ObjectivePulse wave detection plays a pivotal role in the diagnosis, treatment, and clinical management of cardiovascular diseases, as well as in traditional Chinese medicine. Pulse wave signals are intrinsically linked to key physiological parameters, including blood oxygen saturation, blood pressure, and heart rate. They enable indirect yet reliable evaluation of critical cardiovascular indicators, such as cardiac output and endocardial viability ratio. Pulse signals are of diagnostic value owing to their ability to encapsulate vital information for identifying cardiovascular and other systemic diseases, which makes the acquisition of a complete and accurate pulse wave profile essential for clinical assessment. As the global population ages, chronic diseases become more prevalent, and suboptimal health conditions increase, the demand for convenient and comfortable daily monitoring solutions has grown significantly. This trend has driven an increasing need for noninvasive, noncontact monitoring technologies that provide an acceptable user experience while delivering precise physiological insights. Additionally, the magnitude of random movements of the human wrist is significantly smaller than those of the human face, upon which the traditional imaging photoplethysmography (IPPG) method is based. Consequently, the signal source is set on the human wrist to facilitate better observation, minimize interference, and achieve more accurate and timely results. Under normal circumstances, radial artery pulsation induces small, visually perceptible vibrations, which can be analyzed using conventional imaging and signal processing techniques.MethodUnder light-emitting diode (LED) illumination, a flexible Y-shaped optical fiber functions as both the probe and transmission medium. By integrating photoelectric conversion and signal processing technologies, a richly detailed complete pulse wave is successfully captured, including tidal waves and dicrotic waves. The specific process is as follows: First, a flexible Y-shaped optical fiber is used as both the probe and transmission medium to capture the variations in reflected light intensity at the radial artery. These variations are recorded using an oscilloscope to obtain the raw signal. The raw signal then undergoes normalization and band-pass filtering in the 0.6?5 Hz range to isolate the relevant frequency components. Finally, the signal is decomposed and reconstructed using the ensemble empirical mode decomposition (EEMD) technique. Because the heart rate and its second and third harmonic frequencies are dominant in the pulse wave, the first intrinsic mode function (IMF) obtained through decomposition represents the high-frequency components, including the second and third harmonics, as well as the characteristic tidal and dicrotic wave information of the pulse wave. The second intrinsic mode function corresponds to the mid-frequency components derived from the decomposition and represents the frequency of cardiac activity. The remaining intrinsic mode functions primarily account for low-frequency noise caused by respiration and environmental interference factors during pulse acquisition. Therefore, only the first two intrinsic mode functions are selected for reconstruction and a richly detailed complete pulse wave is obtained that included tidal and dicrotic waves.Results and DiscussionsThe proposed noncontact optical fiber probe method can acquire complete pulse wave signals with well-preserved characteristic points (Fig. 8). This study includes two designed experiments. The first experiment includes 25 participants; their radial artery pulse signals are measured and the acquired waveforms are evaluated using the characteristic cycle ratio as an assessment metric. In the second experiment, the pulse waveforms obtained through the proposed method are synchronously compared with those measured by a clinically used wrist-mounted hemodynamic analyzer (TL-400) to verify the accuracy and effectiveness of the method. The results of the first experiment show that in signal groups 1?22, the primary waves of the participant pulse waves are fully captured during the measurement period, with the completeness of the measured tidal waves and dicrotic waves reaching 86% and 87%, respectively (Table 1). The results of the second experiment show that the characteristic details, vibration cycles, trends, and shapes of the pulse waveforms obtained using the proposed method are fundamentally consistent with those measured by the TL-400 (Fig. 10). These results indicate that the proposed method for extracting radial artery pulse waves is effective, accurate, and can preserve most of the detailed features in the pulse wave signals.ConclusionsThis study proposes a noncontact method for acquiring radial artery pulse waves using a flexible Y-shaped optical fiber probe. The method uses an LED light source for illumination, with the flexible Y-shaped optical fiber serving as both the probe and light transmission medium. Combined with photoelectric conversion and signal processing techniques, the initial signal is obtained through outlier handling and band-pass filtering. Subsequently, the pulse wave signal is successfully decomposed and reconstructed using EEMD. The analysis and experimental results demonstrate that this method obtains robust and reliable pulse wave signals that can be further applied to the measurement of heart rate, heart rate variability, blood oxygen levels, and even blood pressure.

ObjectiveTemperature fluctuations substantially influence the accuracy of optical property measurements in non-invasive physiological monitoring, particularly in the field of non-invasive blood glucose monitoring. Accurate measurement of tissue optical properties is crucial for reliable physiological parameter assessment. However, existing studies on the impact of temperature on tissue optical properties are mostly limited to ex vivo tissues or simulated samples, which fail to fully account for the complexity and dynamic nature of in vivo biological tissues. Moreover, the interplay between temperature changes and tissue optical parameters, such as the reduced scattering coefficient and total hemoglobin concentration, remains poorly understood. This study aims to address these gaps by investigating the effects of temperature changes on the reduced scattering coefficient and total hemoglobin concentration of human skin, using spatial frequency domain imaging (SFDI) combined with a two-layer skin model. Additionally, this study validates the effectiveness of temperature correction in reducing noise signals through glucose tolerance experiments. The findings are expected to enhance the understanding of the role of temperature in tissue optical properties and improve the accuracy and reliability of non-invasive physiological monitoring techniques, particularly in the context of non-invasive blood glucose monitoring.MethodsIn this study, SFDI is employed to investigate the impact of temperature changes on the reduced scattering coefficient and total hemoglobin concentration of human skin. The SFDI technique, known for its wide field-of-view and non-contact nature, allows for the quantitative inversion of these optical parameters during natural skin temperature changes. A two-layer skin model is used to account for the different optical properties of the epidermis and dermis layers. The experiment is conducted on four volunteers whose skin temperatures are artificially manipulated by heating and cooling. For the heating process, a hot water bag is used to increase the skin temperature to 41 ℃, followed by natural cooling on a platform for 5 min. For the cooling process, the skin is exposed to air conditioning at 16 ℃ for a minute and a half, followed by natural warming for 5 min. The skin temperature is monitored by attaching temperature sensors to the hand. The experimental setup includes a charge-coupled device (CCD) image sensor and a digital micromirror device as the projection source. Three light-emitting diode (LED) sources with wavelengths of 623, 540, and 460 nm are used, and the spatial modulation frequency is set at 0.2 mm-1 with camera exposure time of 40 ms. To validate the effectiveness of temperature correction, glucose tolerance experiments are conducted on the four subjects. Each subject undergoes two experiments: one with the ingestion of a glucose solution and the other with pure water as a control. The skin temperature, reduced scattering coefficient, and blood glucose levels are continuously monitored during these experiments. The data are collected every 30 s to minimize measurement errors due to hand movement.Results and DiscussionsThe study reveals a positive correlation between skin temperature and the reduced scattering coefficient, with an increase of 0.5%?1% per 1 ℃ temperature change. Conversely, the total hemoglobin concentration exhibits a negative correlation with temperature. During the natural cooling process, the reduced scattering coefficient and total hemoglobin concentration exhibit distinct trends, with the former increasing and the latter decreasing with a decrease in temperature (Fig. 3). In the natural warming process, both parameters demonstrate consistent changes, with the reduced scattering coefficient increasing and total hemoglobin concentration decreasing with an increase in temperature (Fig. 4). Notably, blood oxygen saturation exhibit individual differences, particularly during the cooling process, with some subjects showing an increase whereas others showing a decrease followed by an increase. However, during the warming process, blood oxygen saturation generally decreases, except for one subject where no significant change is observed.Temperature correction is found to be effective in reducing background noise and enhancing the accuracy of non-invasive glucose monitoring. After temperature correction, the reduced scattering coefficient exhibits a clear negative correlation with blood glucose concentration, which aligns with previous reports (Fig. 5). The corrected reduced scattering coefficient curves are smoother, indicating that temperature correction can effectively suppress physiological noise (Fig. 6). These findings highlight the importance of temperature correction in improving the reliability of physiological parameter measurements. This study demonstrates that temperature significantly affects the optical properties of the skin, particularly the reduced scattering coefficient and total hemoglobin concentration. The results provide valuable insights for future research on non-invasive physiological monitoring and suggest further exploration of the interrelationship between tissue optical parameters and physiological conditions.ConclusionsThe present study conclusively establishes that temperature exerts a substantial influence on the optical properties of human skin, specifically the reduced scattering coefficient and total hemoglobin concentration. These findings underscore the critical necessity of incorporating temperature correction mechanisms to increase the precision and reliability of non-invasive physiological monitoring techniques, particularly in the context of non-invasive blood glucose monitoring. The results obtained offer significant and valuable insights for advancing future research endeavors aimed at elucidating the intricate interplay between tissue optical parameters and physiological conditions. Future work should focus on further exploring the complex relationships among the reduced scattering coefficient, total hemoglobin concentration, and other relevant physiological parameters to deepen our understanding of these interactions.

ObjectiveThe persistence of bacterial infection is the main cause of impeding rapid wound healing, which not only brings physical pain and economic burden to patients, but also exacerbates the hazards of antibiotic abuse, and even potentially leading to infectious complications that endanger patients lives. The diagnosis made by clinicians, based on the subjective judgement of patients clinical symptoms and signs, lacks sufficient accuracy. Sampling and culture methods are time-consuming, complicated, cumbersome, and low sensitivity. Therefore, developing a rapid and accurate method to identify wound pathogens is of great significance for the clinical diagnosis of bacterial infections.MethodsCommon pathogenic agents (E. coli and S. aureus) are used in the research. The scattering interferences and noise effects in bacterial spectral data are removed by a customized method that composes of Hough transform line detection and adaptive median filtering. In order to avoid the interference of the concentration factor, the least square method is used to fit the maximum and average values of each row or column with the sample solution concentration, and the goodness of fit (R2) is calculated. The maximum of R2 is selected as the normalization standard for normalization of the data. Finally, for each bacterial sample set, a 10×10 sliding window is used to traverse the spectral data to divide the dataset into fixed-size regions. And then the data in each region are analyzed independently using the support vector machine (SVM) models, as well as using the K-fold cross-validation method to select the subset of features with high contribution to classification as feature regions. The classification effect of the SVM model is compared with those of the Random Forest (RF), K Nearest Neighbors (KNN) and Multi-Layer Perceptron (MLP) models to highlight the superiority of this research method. In order to further verify the effectiveness and practicability of the model and the feature regions, the model is evaluated through the spectral data of the mixed bacteria and their background solution containing human skin tissue components. Furthermore, Gaussian noise with different sizes is added to the bacterial spectra containing the simulated background in order to generate simulated data for classification and identification and to ensure higher accuracy in more complex or diverse wound environments.Results and DiscussionsThe results of data pre-processing (Fig. 5) show that the interference of the first-order Rayleigh scattering in the 3D fluorescence spectra can be effectively eliminated by this method, which makes the effective information in the spectra highlighted. The maximum value in column 19 is selected as the normalized standard value, and 5-fold cross-validation is used to determine the optimal parameter combination. The extracted region data are input into the SVM model for a classification study, and the two fixed windows at the excitation bands of 250?280 nm (corresponding to the emission band of 345-365 nm) and the excitation band of 385?415 nm (corresponding to the emission band of 600?635 nm) are finally selected as spectral data in the bacterial feature regions. Compared with the ordinary normalization and principal component analysis (PCA) methods (Table 4), the classification accuracies of the SVM model in the single-strain training and test sets achieve 98.4% and 96.3% respectively, and the recognition accuracy of the mixed strains is 93.5%, which is about 16% higher than that of the traditional PCA method. All comparative results of relevant parameters, i.e. confusion matrices (Fig. 8), ROCs (Fig. 9), accuracies, F1-scores, indicate that SVM performs optimally among the four models. Under the complex wound environment that simulates human tissue interference experimentally, the accuracies for single strains and mixed strains are 91.7% and 94.4% respectively, and that for 2200 cases of theoretical spectral classification is 92.3%.ConclusionsIn this study, 3D fluorescence spectroscopy is combined with SVM for the rapid detection of wound pathogenic bacteria, and the spectral feature regions are extracted by the sliding window method, which effectively improves the feature extraction efficiency and the classification accuracy. The classification accuracies of single pathogenic bacteria in the training and the test sets reach 98.4% and 96.3% respectively, and the classification accuracy of mixed bacteria is 93.5%. In addition, the classification accuracies of 91.7% and 94.4% for single and mixed pathogens in the simulated human skin background further validate the applicability of the method in complex environments. The classification accuracy of the proposed model is about 16% higher than that of the traditional PCA method. This total experimental time required to complete the classification process for the 88-case bacterial spectral dataset in a single run is less than 2 s (including the full process of data preprocessing and model prediction). Meanwhile, the sliding window method proposed in this study makes the spectra be collected only for feature regions, and it takes only 6 min, which greatly shortens the time of data acquisition compared with that of scanning the whole spectrum (about 1 h). Therefore, the sliding window method combined with the support vector machine model proposed in this study is expected to provide a new way to achieve rapid identification and detection of wound bacteria.

ObjectiveParkinson’s disease (PD) is a common progressive neurodegenerative disease whose incidence increases with age. An increasing number of studies have identified depression in PD (DPD) as one of the most common non-motor symptoms, characterized by decreased interest, sleep disturbances, and even suicidal tendencies that severely impact patient quality of life. Although the drugs used clinically to treat DPD can alleviate symptoms to some extent, research on the long-term safety and efficacy of these medications remains limited. Recently, 40 Hz light flicker stimulation has gained attention for its non-invasive, safe, effective, well-tolerated nature, and high patient compliance. Therefore, this study investigates the effects of 40 Hz light flicker stimulation on inflammatory responses, microglial polarization, and pyroptosis in PD mice with depressive-like behaviors, and explores the mechanisms underlying its effects on these behavioral and pathological changes, providing a clinical basis for this treatment in DPD.MethodsEight-month-old C57BL/6 mice were randomly divided into Control, PD, and 40 Hz+PD groups, with eight mice in each group. The 40 Hz+PD group received 40 Hz light flicker stimulation intervention for four weeks. After the intervention, the visual function of the mice was assessed using dark and light box tests. After grouping, a subacute PD model was established in the PD and PD+40 Hz groups via intraperitoneal injection of MPTP. The Control group received no MPTP injection. The motor abilities of the mice were evaluated using rotarod, wire hanging, and open field tests. Tail suspension and forced swimming tests were used to detect depression-like behavior in mice. Immunofluorescence was used to measure the colocalization level of Iba-1, CD206, and iNOS in the prefrontal cortex. ELISA was used to detect the content of IL-18 and IL-1β in the serum. RT-PCR and Western Blot were used to detect the mRNA and protein expression levels of synaptic plasticity-related proteins, microglial markers, pyroptosis-related proteins, and components of the Sirt1/PGC-1α signaling pathway. NAD+, ATP, and LDH contents in the cortex were measured using kits.Results and DiscussionsThe results show that 40 Hz light flicker stimulation does not affect the visual function of C57BL/6 mice. Compared with the Control group, the PD group shows motor dysfunction and a prolonged immobility time in the forced swimming and tail suspension tests (Fig. 1), suggesting that PD mice exhibit depressive-like behaviors. Simultaneously, the expression levels of BDNF and PSD95 decrease (Fig. 2). The expression levels of CD206 and Arg1 and the levels of Iba-1 and CD206 colocalization significantly decrease (Fig. 3), but the expression levels of iNOS, NLRP3, Cleaved-Caspase1, GSDMD-N, IL-18, IL-1β, the levels of Iba-1 and iNOS colocalization, and IL-18 and IL-1β contents significantly increase (Fig. 4). Furthermore, the levels of NAD+, ATP, Sirt1, and PGC-1α significantly decrease (Fig. 5), suggesting that the NAD+/Sirt1/PGC-1α signaling pathway is inhibited in PD mice. After the four week 40 Hz light flicker intervention, motor dysfunction in PD mice is alleviated, and the immobility time during the forced swimming and tail suspension tests is significantly shortened (Fig. 1). The expression levels of PSD95 and BDNF show increasing trends (Fig. 2), suggesting their role in alleviating depressive-like behaviors in PD mice. The expressions of the M2-type microglial cell marker CD206 and Arg1 increase (Fig. 3) and the expression of cell pyroptosis decreases (Fig. 4), reducing the occurrence of inflammatory reactions. In addition, the expressions of NAD+, ATP, Sirt1, and PGC-1α increase (Fig. 5).ConclusionsThis study investigates the effects of 40 Hz light flicker stimulation therapy on depression-like behaviors in PD mice by establishing an MPTP-induced PD model. The 40 Hz light flicker stimulation therapy activates the NAD+/Sirt1/PGC-1α signaling pathway in the cortex of PD mice, promoting the polarization of microglia towards the M2 phenotype. This alleviates the vicious cycle of microglial polarization imbalance and pyroptosis, alleviates neuroinflammation, and ultimately relieves depressive-like behaviors in PD mice.

ObjectiveWhen studying the efficacy of laser surgery for vascular skin diseases using the rat dorsal skinfold window model, conventional approaches typically need removing the superficial skin tissue to directly irradiate exposed blood vessels. Such a methodology neglects critical factors including skin absorption, scattering, and thermal transfer, thus severely underestimating the required laser parameters for clinical treatments. A novel transdermal irradiation method is proposed to solve this issue by maintaining the intact skin layer during laser exposure, thus providing a research method more in line with the clinical scenario. The use of dual-modal imaging combining laser speckle contrast imaging (LSCI) with infrared thermography can precisely characterize and compare vascular thermal effects induced by direct laser irradiation and transdermal laser irradiation.MethodsA dual-modal imaging system combining LSCI with infrared thermography is developed to monitor blood flow dynamics and temperature changes simultaneously during therapeutic laser irradiation. The dorsal skinfold window model is prepared in Sprague-Dawley (SD) rats, facilitating visualization and monitoring of microvascular structures. Two irradiation modes are investigated: the direct irradiation of vessels exposed by skin removal and the transdermal irradiation of vessels beneath an intact skin layer (average depth of ~1.3 mm). The laser used is a long-pulsed Nd∶YAG (1064 nm) system, and the experimental parameters include energy densities of 7.88 J·cm-2 and 11.04 J·cm-2, pulse durations of 1 ms and 5 ms, frequency of 1 Hz, and pulse numbers of 4 and 10. To improve image quality and temporal resolution, a lightweight deep-learning denoising algorithm, LDSCI-GAN, is applied to raw speckle images, significantly enhancing the detection of rapid vascular changes.Results and DiscussionsThe LDSCI-GAN deep-learning denoising method enhances the quality of temporal laser speckle blood flow images by using only 5 frame raw speckle images, improving peak signal-to-noise ratio (PSNR),mean structural similarity index (MSSIM), and correlation coefficient R from 14.6±6.1, 0.25±0.18, and 0.38±0.09 to 33.9±3.1, 0.97±0.03, and 0.99±0.01, with corresponding increases of 132.2%, 288.0%, and 160.5%, respectively (Fig. 3 and Fig. 4). The denoised image quality matches the quality of processed 50 frame raw speckle images using MD-ABM3D, enabling a tenfold improvement in temporal resolution and allowing reliable visualization of transient blood flow changes. Under identical laser parameters (energy density of 7.88 J·cm-2, pulse duration of 5 ms, and pulse number of 4), the direct irradiation induces a complete vessel closure with a peak temperature of 50.6 ℃, whereas the transdermal irradiation results in only a mild blood flow reduction with a peak temperature of 37.6 ℃ (Fig. 5). This discrepancy is due to substantial optical attenuation and thermal diffusion within the skin, which causes the direct irradiation model to underestimate the energy threshold required for deep vessel treatment. In other words, the transdermal irradiation better replicates clinical conditions and provides a more realistic in vivo model for laser therapy evaluation. Increasing the pulse duration from 1 ms to 5 ms at 9.27 J·cm-2 (5 pulses) enables clear vessel contraction (Fig. 6). Raising the energy density from 9.27 J·cm-2 to 11.04 J·cm-2 (pulse duration of 1 ms, 5 pulses) significantly enhances the thermal response, with a peak temperature difference between direct and transdermal modes reaching 27.3 ℃ (Fig. 7). Increasing the pulse number from 4 to 10 at 7.88 J·cm-2 (pulse duration of 5 ms) elevates the vessel temperature from 42.5 ℃ to 57.6 ℃ and achieves a complete occlusion under transdermal conditions (Fig. 8). These findings confirm that optimizing laser parameters is essential for effective treatment of deep vessels and demonstrate the value of transdermal models in guiding clinically relevant laser therapy strategies.ConclusionsTo simulate real clinical scenarios, a novel transdermal irradiation method is proposed to overcome the inaccurate estimation of laser-tissue interactions caused by the removal of the upper skin layers. Using a dual-modal imaging system that combines LSCI with infrared thermography, the thermal effects by new and traditional methods are compared. Experimental comparisons reveal that direct irradiation significantly underestimates the energy required for effective vessel treatment due to the absence of skin-mediated scattering and heat diffusion. When the upper skin layers are preserved, higher energy density, larger pulse duration, and larger pulse number are necessary to reach an ideal thermal response. In conclusion, this work emphasizes the necessity of transdermal irradiation models in experimental dermatologic laser studies and provides a powerful imaging framework to guide the development of safer and more effective laser therapies.

ObjectiveCardiovascular diseases remain a leading global health burden, with catheter ablation playing a pivotal role in treating arrhythmias and other cardiac pathologies. Conventional 2 μm quasi-continuous wave (QCW) lasers, while widely adopted in clinical practice, face critical limitations rooted in their thermal ablation mechanisms. The prolonged pulse durations (μs?ms scale) and low peak power of these systems induce significant heat diffusion, resulting in uncontrollable thermal damage zones, tissue carbonization, and collateral damage to adjacent structures. These drawbacks not only compromise procedural precision but also elevate risks of post-operative complications such as perforation, thrombogenesis, and delayed healing—factors that severely constrain their applicability in high-risk interventions like atrial fibrillation ablation. The emergence of ultrafast lasers presents a transformative opportunity to overcome these barriers. Femtosecond-pulsed lasers, characterized by ultrashort durations (10-15 s) and gigawatt-level peak intensities, enable non-equilibrium interactions with biological tissues through photochemical and plasma-mediated processes. However, existing ultrafast systems predominantly operate at near-infrared wavelengths where water absorption is suboptimal, limiting their efficiency in hydrated myocardial tissues. Crucially, no prior studies have systematically explored 2 μm femtosecond lasers—a spectral window synergizing strong water absorption with hemoglobin transparency—for cardiac ablation. This knowledge gap hinders the development of wavelength-optimized, thermally confined ablation technologies tailored for cardiovascular applications. To address these unmet needs, this study pioneers the development and biomedical validation of a 2 μm thulium-doped femtosecond fiber laser system. By integrating Raman soliton self-frequency shifting and chirped-pulse amplification (CPA), we achieve a high peak power, all-fiber femtosecond source at 2 μm specifically engineered for myocardial ablation.MethodsThe experiment was carried out based on a 2 μm broadband femtosecond seed source generated by Raman soliton frequency shift, as shown in Fig. 1. The system mainly includes a Raman soliton seed source, a fiber stretcher, a three-stage thulium doped fiber amplifier, and a grating compressor. Using chirped pulse amplification technology, the system finally achieves a 2 μm femtosecond laser output with high peak power and ultrashort pulse width. The wavelength was 1978 nm, the repetition rate was 32.7 MHz, the pulse width was 480 fs, the average power was 12 W, the pulse energy was 0.4 μJ, and the peak power was 0.8 MW. In the experiment, fresh pig myocardial tissue samples were used as the experimental model. Before laser ablation, the myocardial tissue was cut into small pieces of 10 mm×5 mm×5 mm using a surgical knife, ensuring a smooth surface as much as possible to avoid changes in the spot due to focusing problems, which could affect the irradiation density. Based on the results of previous single factor experiments, key influencing parameters in 2 μm ultrafast laser ablation were selected in this experiment: laser power of 1.0?3.0 W, scanning speed of 1?2.5 mm/s, single line scanning path, and scanning path length of 5 mm. The ablation depth, ablation area, and thermal damage zone were quantitatively analyzed.Results and DiscussionsThe 2 μm femtosecond laser shows almost no photothermal ablation effect or carbonization phenomenon during myocardial tissue ablation, which is mainly due to its ultrashort pulse characteristics. Scanning electron microscopy shows that the ablation area presents a strip-shaped structure consistent with the laser scanning trajectory, proving that the laser achieves precise positioning in the tissue. Meanwhile, the results of energy spectrum and XPS (X-ray photoelectron spectroscopy) analysis indicate that there is almost no significant difference in the content of oxygen, carbon, nitrogen and other elements between the ablated and non-ablated areas, further indicating that no significant tissue carbonization or chemical composition change occurs during the ablation process. The Fourier transform infrared spectroscopy detection results confirm that femtosecond laser mainly breaks down chemical bonds in myocardial tissue, causing protein breakdown, rather than relying on thermal ablation mechanism. The interaction mechanism between femtosecond laser and biological tissue is very complex, and further research is needed to reveal its underlying processes.ConclusionsA femtosecond erbium-doped fiber laser was used to pump a highly nonlinear fiber, and a femtosecond broadband Raman soliton seed source operating near 2 μm was generated through fiber nonlinear frequency conversion method. Further, a femtosecond laser output with a center wavelength of 1978 nm, an average power of 12 W, a pulse width of 480 fs, a pulse energy of 0.4 μJ, and a peak pulse power of 0.8 MW was obtained through CPA technology. In vitro myocardial ablation studies have shown that 2 μm femtosecond laser has almost no thermal ablation effect or carbonization phenomenon during myocardial ablation. The 2 μm femtosecond laser mainly breaks down chemical bonds in myocardial tissue, causing protein breakdown, rather than relying on thermal ablation mechanism. Its excellent ablation ability is related to its high peak power and short pulse width characteristics. However, the interaction mechanism between femtosecond laser and biological tissues is very complex and further research is needed to reveal its underlying processes. In summary, these preliminary research results indicate that 2 μm ultrashort pulse laser has broad application prospects in the field of myocardial ablation and atrial fibrillation treatment.

ObjectiveAccurate short-term rainfall prediction plays a vital role in urban flood warning and emergency management. Traditional methods face significant challenges in capturing small-scale sudden rainfall events due to the insufficient vertical resolution of conventional meteorological radars and the complex physical assumptions of numerical models. Temperature and humidity lidar technology offers high spatiotemporal resolution data, but its non-stationary spatiotemporal characteristics pose challenges for feature extraction and model robustness. This study aims to develop a novel deep learning model, MixLinear, to address these limitations and improve the accuracy of short-term rainfall prediction using lidar data.MethodsIn this study, the temperature and humidity Raman lidar system with a 355 nm wavelength laser was utilized, featuring a vertical resolution of 7.5 m and a time resolution of 10 min, and the original detection range of 0?20 km was narrowed to 0?4 km to reduce noise and focus on high-reliability near-surface data for data acquisition and preprocessing. For feature extraction, the model integrated lidar extinction coefficient, temperature, and relative humidity data, and a decomposition algorithm was employed to separate the input signal into trend and residual terms for modeling long-term rainfall trends and local abrupt fluctuations. In terms of model architecture, MixLinear was equipped with a dual-branch “linear-nonlinear-linear” lightweight network and used patch-based time windows to enhance the capture of long-term dependencies, addressing the memory decay issue in long time series.Results and DiscussionsExperiments based on the Anqing City lidar dataset (from 2023 to 2024) demonstrate that MixLinear achieves a prediction accuracy of 0.857, outperforming benchmark models like Dlinear (0.826), Autoformer (0.710), and TiDE (0.821), while maintaining an accuracy of 0.783 in the 24th time window to exhibit strong long-term prediction stability. Ablation experiments reveal that removing the temporal decomposition or patch module reduces accuracy to 0.818 or 0.813, respectively, underscoring the significance of these components, and that using multi-parameter inputs (extinction coefficient, temperature, relative humidity) significantly enhances accuracy compared to single-parameter inputs. The model demonstrates excellent rainfall feature reconstruction, with the first time window accuracy reaching 0.983 (Fig. 6). Additionally, its mean squared error (MSE) and mean absolute error (MAE) are 0.0037 and 0.0298, respectively, which are lower than those of all benchmark models (Table 2).ConclusionsThe MixLinear model effectively integrates three-dimensional profile information from temperature and humidity lidar, using a trend-residual decomposition and dual-branch network to enhance prediction accuracy for small-scale rainfall events. Its accuracy of 0.857 surpasses that of existing models, providing a new technical approach for urban refined meteorological services. Future work will focus on optimizing the model for real-time applications in meteorological early warning and urban flood control.

ObjectiveEnergy derived from inertial confinement fusion (ICF) is clean and sustainable; however, achieving controlled nuclear fusion remains a major challenge. In laser-driven fusion experiments, X-ray radiation fields are generated within the hohlraum. To investigate the propagation characteristics of X-rays, it is essential to detect spatially resolved X-ray spectra and diagnose radiation from doped materials. This approach enables the analysis of X-ray transport paths, energy loss profiles, and the extraction of key parameters such as electron temperature and density. Crystal-based X-ray diffraction spectroscopy, a fundamental plasma diagnostic technique, plays a vital role in ICF research.Current crystal-based diagnostic tools exhibit limitations: flat crystals lack focusing capabilities; cylindrical crystals deviate from the Rowland circle geometry; conical crystals suffer from defocusing effects; and spherical and toroidal crystals are primarily designed for two-dimensional imaging. To meet the demands for high-resolution, high-throughput spectroscopy with minimal source broadening, this study proposes a novel X-ray spectrometer utilizing a sinusoidal exponential-type crystal. By incorporating a curved crystal geometry with tunable sagittal and meridional radii while preserving the Rowland circle configuration, the developed spectrometer achieves superior spectral resolution for X-ray diagnostics.MethodsThe sinusoidal exponential-type crystal designed in this study adheres to the Rowland circle configuration, which effectively mitigates spectral resolution degradation caused by source size broadening. The curved crystal is engineered to associate each diffraction position with a distinct energy point, ensuring that all energy points within the spectral range are focused optimally. This configuration ensures that all energy points across the diagnostic spectrum maintain a high spectral resolution. The crystal structure features independently tunable curvature radii in the sagittal and meridional planes. Incident and reflected X-rays tangentially converge at imaging points along variable-radius circles. Crucially, these focal points correspond to a common reflection path. By positioning the detector at this optimized location, optical aberrations are minimized, and spectral broadening induced by source size extension is significantly suppressed. This dual-curvature design thereby accomplishes the objective of high-resolution X-ray spectroscopy.Results and DiscussionsThe designed sinusoidal exponential-type crystal spectrometer system operates within a spectral energy range of 7.8?8.2 keV, corresponding to X-ray wavelengths of 0.1512 nm to 0.1590 nm. An α-quartz (202ˉ3) crystal with a lattice constant of 0.2749 nm is employed as the diffraction element, enabling detection across a Bragg angle range of 33.3° to 35.3°. The central arc length of the crystal spectrometer is 28.88 mm, with a physical dimension of 35 mm×30 mm to account for edge effects. With a detector resolution of 110 μm, the theoretical spectral resolving power of the system is calculated as 15170.In the X-ray spectral detection system design, the distance from the X-ray source to the crystal center is set to 300 mm, and the crystal-to-detector distance is optimized to 795.76 mm for ideal focusing. The diffraction focusing capability and spectral resolution of the sinusoidal exponential-type crystal are numerically validated using XCD simulation software. Five monoenergetic Gaussian-distributed sources spanning 7.8?8.2 keV are simulated, yielding well-focused spot images for all energy points (Fig. 7), thus demonstrating excellent focusing performance across the 7800?8200 eV range. The influence of source size on focusing is analyzed with the detector positioned at the optimal location (Fig. 8). Results indicate that increased source dimensions induce horizontal spot broadening. Furthermore, the combined effects of source size and detector misalignment on spectral resolution are quantified (Fig. 9). The findings reveal no significant resolution loss when transitioning from an ideal point source to a source with a radius of 0.5 mm. In the simulation experiment, the spectral resolution of the system is calculated to be approximately 11000.Experimental validation is conducted using a copper-target X-ray tube setup, incorporating the sinusoidal exponential-type crystal and a complementary metal-oxide-semiconductor transistor (CMOS) detector. Experimental results align with simulations: distinct focal spots corresponding to Cu Kα1 and Kα2 emission lines are observed (Fig. 11), confirming superior photon throughput. The practical spectral resolving power is measured as 2810 (Fig. 12). Discrepancies between theoretical and experimental resolutions are attributed to limitations in crystal surface figure accuracy and fabrication tolerances, highlighting the need for advanced high-precision crystal machining techniques in future studies.ConclusionsThis work is based on the Rowland circle geometry and introduces a sinusoidal exponential-type crystal with variable radii in the sagittal and meridional planes. The designed spectrometer achieves high photon throughput and high spectral resolving power simultaneously. Theoretical simulations predict its resolving capability, subsequently validated through systematic X-ray diffraction experiments. Experimental results demonstrate that the spectrometer exhibits exceptional focusing performance within a defined spectral range, effectively concentrating X-rays into distinct bright spots. The sinusoidal exponential-type crystal significantly suppresses spectral resolution degradation caused by source size broadening, achieving a practical resolving power of 2800 and demonstrating high-resolution spectroscopic characteristics.