Here, we report a passively Q-switched Tm laser at 1720 nm. The Q-switching behavior originates from a piece of Tm-doped fiber. Tm-doped fiber has a broad absorption spectrum covering 1.7 μm waveband, which can be used as a fiber-type saturable absorber for 1.7 μm pulsed lasers. In comparison with the typical Q-switching system in which the gain fiber has a different rare-earth doping from the fiber-type saturable absorber, the same rare-earth doping in the gain fiber and the saturable absorber cannot support the effective Q-switching operation. To initiate the pulsing operation in this Tm-Tm laser system, we introduce a mismatch of mode-field area between the gain fiber and the fiber-type saturable absorber. By using ∼50 cm Tm-doped fibers as the saturable absorber, the passive Q-switching 1720 nm laser is realized and produces 340 mW output power with a nanosecond pulse width (400 ns ∼422 ns). After investigating the Q-switching pulses, it is found that the evolution trend of the pulses with the pump power is not consistent with the typical passive Q-switching lasers. In order to understand this unusual Q-switching behavior, we establish a rate equation model that is coupled with the mismatch of mode-field area to give a comprehensive understanding. From the numerical simulation, a new Q-switching laser cavity consisting of dual laser resonance is proposed to guide how to realize a passive Q-switching 1.7 μm laser with a higher output power.

Computed tomography (CT) is essential for diagnosing and managing various diseases, with contrast-enhanced CT (CECT) offering higher contrast images following contrast agent injection. Nevertheless, the usage of contrast agents may cause side effects. Therefore, achieving high-contrast CT images without the need for contrast agent injection is highly desirable. The main contributions of this paper are as follows: 1) We designed a GAN-guided CNN-Transformer aggregation network called GCTANet for the CECT image synthesis task. We propose a CNN-Transformer Selective Fusion Module (CTSFM) to fully exploit the interaction between local and global information for CECT image synthesis. 2) We propose a two-stage training strategy. We first train a non-contrast CT (NCCT) image synthesis model to deal with the misalignment between NCCT and CECT images. Then we trained GCTANet to predict real CECT images using synthetic NCCT images. 3) A multi-scale Patch hybrid attention block (MSPHAB) was proposed to obtain enhanced feature representations. MSPHAB consists of spatial self-attention and channel self-attention in parallel. We also propose a spatial channel information interaction module (SCIM) to fully fuse the two kinds of self-attention information to obtain a strong representation ability. We evaluated GCTANet on two private datasets and one public dataset. On the neck dataset, the PSNR and SSIM achieved were 35.46 +/- 2.783 dB and 0.970 +/- 0.020 , respectively; on the abdominal dataset, 25.75 +/- 5.153 dB and 0.827 +/- 0.073 , respectively; and on the MRI-CT dataset, 29.61 +/- 1.789 dB and 0.917 +/- 0.032 , respectively. In particular, in the area around the heart, where obvious movements and disturbances were unavoidable due to the heartbeat and breathing, GCTANet still successfully synthesized high-contrast coronary arteries, demonstrating its potential for assisting in coronary artery disease diagnosis. The results demonstrate that GCTANet outperforms existing methods.

Spectral imaging technology has gained widespread application across diverse fields due to its ability to capture spatial and spectral information simultaneously. However, conventional spectral scanning methods using single-peak tunable filters face the challenges of low optical throughput. Inspired by Fellgett's advantage in Fourier-transform infrared spectroscopy, this paper proposes a tunable filter with multiple resonances to improve optical throughput. It is composed of a simple Fabry-Pérot cavity filled with liquid crystal. An artificial neural network is employed to match with the filter for spectrum reconstruction. Experimental results show a spectral resolution of 10 nm and a switching time of ≈23 ms between adjacent states. As a demonstration, biological specimens are spectrally imaged under different light conditions with good fidelity. The results suggest that the filter possesses over six times higher optical throughput than a commercial liquid crystal tunable filter (LCTF), leading to better spectrum accuracy for spectral imaging under low-light conditions. The compact and cost-effective design of this tunable filter enables seamless integration into imaging systems, presenting promising prospects for practical applications such as portable health management and food inspection in low-light conditions.

In the coded aperture snapshot spectral imaging (CASSI) system, the coded and compressed single-channel measurements need to be reconstructed into hyperspectral cubes. Existing discriminative models reconstruct the spectral cube by optimizing the mean squared error (MSE) between the ground truth and the predicted image, employing peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as metrics to gauge the quality of reconstruction. However, these indicators often possess significant limitations in mimicking human visual perception and in discerning the impact of image distortions on perceived visual quality. In this article, a new model named CASSIDiff is proposed to reconstruct CASSI measurements, achieving advanced results in perceptual loss-based evaluation metrics such as learned perceptual image patch similarity (LPIPS) and Fréchet inception distance (FID). The diffusion model, which enjoys high accuracy and reliability in generative tasks, is used for the first time for the hyperspectral reconstruction task. A feature fusion mechanism based on discrete wavelet transform (DWT) is used to weaken the noise interference effect in the conditional diffusion model. Considering the interspectra similarity and long-range dependencies of hyperspectral data, the spatial-spectral attention mechanism is also introduced. Experiments show that CASSIDiff not only outperforms most existing algorithms in simulation datasets but also shows robustness to real data published and collected in our home-built CASSI system. The code and models are publicly available at: https://github.com/YifanSi/CASSIDiff.

Phaeocystis proliferation is a primary instigator of algal blooms, commonly known as red tides, posing a significant threat to marine life and severely disrupting marine ecosystems. Currently, no effective method exists for estimating Phaeocystis density, underscoring an urgent need for preventative measures against Phaeocystis blooms. Given the challenges associated with the varying sizes and frequent overlapping of Phaeocystis colonies, we propose an innovative counting algorithm that leverages feature reconstruction and multispectral generator modules. Utilizing deep learning, our method achieves accurately real-time density estimation and prediction of Phaeocystis colonies. The algorithm operates in two stages: first, a multispectral reconstruction block is trained to function as a multispectral generator; second, spectral and spatial features are integrated to predict density and perform counting. Our approach surpasses existing algorithms in accuracy for Phaeocystis counting and demonstrates the utility of multispectral data in enhancing the neural network's ability to discern targets from their background.

To transform bound state-in-continuum (BIC)-related unidirectional radiation into BIC-related unidirectional lasing, a 1-D grating with a parity-time (PT)-symmetry configuration is proposed. Through non-Hermitian modulation, the BIC undergoes an asymmetric split in the –k and +k spaces. Abnormal phenomena have also been observed. The asymmetric BIC makes the grating either a gain cavity or a loss cavity depending on the incident direction of the plane wave. With plane wave incidence, the grating exhibits Fano resonance with energy conservation. However, for diverging light incident from a line source or a Gaussian source, the coupling of the gain cavity and loss cavity produces a new phenomenon, unidirectional and single-mode lasing with an interesting wavefront transformation from a diverging wave to a unidirectional plane wave. The physical mechanism is explained by the joint effect of the asymmetric PT-BICs and cavity resonance.

Early detection of Autism Spectrum Disorder (ASD), a neuro-developmental condition characterized by impairments in social communication, is critical for prompt intervention and care. Recent advancements in digital medicine have significantly enhanced the precision and efficiency of ASD diagnosis, management, and care coordination. These technologies offer considerable potential for optimizing treatment pathways and improving patient outcomes. However, challenges remain in sustaining long-term user engagement and effectively integrating these innovations into routine clinical practice for ASD management. In this study, we report the findings from a clinic-based, prospective investigation aimed at evaluating the diagnostic validity of ASD employing XGBoost algorithm. Of the 51 cases assessed, 24 were diagnosed with ASD, while 27 were identified as having developmental delay without autism. Functional near-infrared spectroscopy (fNIRS) was employed to measure resting-state hemo-dynamic fluctuations in the bilateral temporal lobes, revealing connectivity differences through computer vision and machine learning analysis. Agglomerative hierarchical clustering (AgHC) was employed to evaluate the active coupling between fluctuations of deoxygenated hemoglobin (HbR) and oxygenated hemoglobin (HbO) across specific channels, elucidating inter-channel relationships in neurovascular coupling. The algorithm, using a range of significant features, achieved robust diagnostic performance with area under receiver operating characteristic curve of 0.99 ± 1, demonstrating sensitivity of 98%, specificity of 95%, negative predictive value (NPV) of 98%, and positive predictive value (PPV) of 95%, indicating high diagnostic accuracy in distinguishing relevant cases. These findings underscore the potential of digital medicine to offer objective and scalable framework for the diagnosis of autism in real-world clinical environments.

The critical importance of early cancer detection in improving patient survival rates has driven substantial innovation in diagnostic methodologies. Optical detection technologies, renowned for their superior sensitivity, molecular specificity, and non-invasive or minimally invasive operational capabilities, have emerged as critical tools for tumor detection. This review systematically examines the use of optical techniques in tumor recognition and diagnosis, with particular emphasis on five technologies: Raman spectroscopy, fluorescence sensors, fiber-optic biosensors, photoacoustic imaging systems, and colorimetric detection platforms. The methods for the direct detection of tumor cells and tumor tissue are first reviewed. Then the contents of some indirect tumor detection methods (through, e.g., cell-derived components, peripheral microenvironment, and molecular biomarkers such as proteins or nucleic acids) are broaden. The review evaluates recent technological progress, identifies key challenges and clinical barriers, and aims to guide future research toward the development of next generation optical platforms for accurate and early cancer diagnosis.

Computed tomography imaging spectrometry (CTIS) is a snapshot hyperspectral imaging (HSI) technique capable of capturing projections of the target scene from multiple wavelengths in one single exposure. The CTIS inversion problem is usually very challenging, and solving it from a single snapshot measurement often requires time-consuming iterative algorithms. And most deep learning-based algorithms in computational imaging need the priori of many samples, which brings a heavy data collection burden. In this article, to reconstruct hyperspectral cubes from CTIS measurements in an efficient way, we introduce a new CITS framework named ASP-Model based on the angular spectrum propagation theory to model the forward CITS process and efficiently reconstruct hyperspectral. Specifically, our method acquires simulation data using angular spectrum propagation for training and reconstructs real data captured by our custom-built CTIS system during inference. This framework allows us to eliminate the need to acquire extensive real data for network training. Moreover, the proposed network can reconstruct 26 spectral channels from one single measurement and demonstrates state-of-the-art results over existing reconstruction algorithms both in simulation and experimental results. We also release a new dataset containing simulated and real CTIS data for public comparison. The code and dataset are publicly available at https://github.com/YifanSi/ASP_Model.

Hyperspectral data, renowned for its capacity to provide comprehensive spectral details, is widely applied in a range of low-level and high-level tasks in remote sensing and computer vision. In this paper, we introduce an algorithm that, for the first time, leverages snapshot spectral imaging technology to generate four-dimensional hyperspectral-spatial data, named CTISNeRF. This advancement is made possible through the use of a Computed Tomography Imaging Spectrometer (CTIS), a cutting-edge sensor technology capable of capturing high-resolution spectral and spatial information in a single snapshot. In addition, a cutting-edge 360-degree panoramic hyperspectral dataset has been created and made publicly available. Our approach utilizes data from the CTIS sensor and a zeroth-order feature-sharing mechanism to adeptly learn spectral and spatial characteristics from diverse scenes. This enables the rendering of high-fidelity spectral cubes for novel views, significantly enhancing the quality and detail of hyperspectral imaging. Extensive experimental outcomes demonstrate that CTISNeRF not only markedly reduces the expenses associated with data collection but also achieves superior image quality. It reaches state-of-the-art standards in metrics such as PSNR, SSIM, and LPIPS. Furthermore, CTISNeRF maintains a more stable generation capability even when the number of training samples is reduced, showcasing its robustness and efficiency. The associated dataset and our algorithm will be publicly accessible at the following repository: https://github.com/YifanSi/CTISNeRF.