Journal of Biophotonics最新文献

Three-photon fluorescence microscopy (3PFM) enables high-resolution volumetric imaging in deep tissues but is often hindered by motion artifacts in dynamic physiological environments. Existing solutions, including surgical fixation and conventional image registration algorithms, frequently fail under intense and nonuniform motions, particularly in low-texture or highly deformed regions. To overcome these problems, we propose StabiFormer, a transformer-based optical flow learning network designed for robust motion correction. Central to StabiFormer is the stable-dynamic feature extractor, which captures interlayer dynamics to facilitate accurate image registration. Our validation across cerebrovascular and intestinal 3PFM datasets demonstrates that StabiFormer achieves near-zero displacement error relative to ground truth in brain vasculature. Furthermore, it enables artifact-free 3D visualization of intestinal macrophages and vasculature at 300 μm depth, a physiologically relevant depth for studying intestinal immune microvasculature. These results establish a noninvasive computational solution for motion-artifact-free volumetric imaging, paving the way for quantitative investigations in previously inaccessible dynamic organ systems.

Functional near-infrared spectroscopy (fNIRS), as a noninvasive brain imaging modality, has shown great potential for autism spectrum disorder (ASD) identification combined with machine learning. In this work, we proposed an ASD identification method using edge-weight enhanced graph attention network (EWE-GAT) with multiple features in resting-state fNIRS signals measured from the bilateral temporal lobes on 22 typically developing (TD) children and 25 children with ASD. Seven features were selected for the EWE-GAT model, including five node features: the coupling between oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) fluctuations, sample entropy for HbO and Hb, and average resting-state functional connectivity (RSFC) for HbO and Hb of each channel, and two edge features: RSFC between each channel pair for both HbO and Hb. With the proposed method, high accurate classification was achieved with 97.92% accuracy, 100% sensitivity, 96.43% precision, and 98.08% F1 score, outperforming usually used traditional machine learning and convolutional neural network models.

Photoacoustic computed tomography (PACT) synergizes optical absorption contrast with ultrasonic resolution for noninvasive biomedical imaging yet faces limitations in signal-to-noise ratio (SNR), resolution, and contrast. This study introduces a coherent-excitation PACT system integrating interferometric optical excitation and Frequency-Compensated Filtered Back Projection (FC-FBP) reconstruction. The proposed method utilizes phase-locked dual-pulse interferometric excitation to amplify photoacoustic emissions; for the isolated chicken heart, the resolution is improved by 7.9% compared to the single-pulse protocol. The FC-FBP algorithm compensates for frequency-dependent acoustic attenuation via depth-adaptive Gaussian filtering, enhancing the projected signal in the target area while suppressing speckle artifacts. Through experimental validation, we confirm that the coherent-excitation scheme enables simultaneous optimization of optical fluence distribution and acoustic coherence; hence, it can be used to resolve previously indistinguishable hemoglobin oxygenation gradients in murine tumor models. This advancement establishes a high-sensitivity PACT framework, showing potential for real-time intraoperative imaging and dynamic metabolic monitoring in clinical applications.

Functional near-infrared spectroscopy (fNIRS) is an imaging technique that uses near-infrared light to monitor blood oxygen level changes in the cerebral cortex and noninvasively study brain function. This review provides an overview of the expanding applications and physical principles of fNIRS to enhance understanding of its imaging process and promote awareness of its broad applicability and potential. We systematically searched PubMed, Web of Science, and Google Scholar, analyzing studies on fNIRS applications in psychiatry, neurology, education, and multimodal imaging. We also introduce the imaging principles of continuous wave fNIRS, frequency domain fNIRS, time domain fNIRS, and diffuse optical tomography fNIRS (including high-density diffuse optical tomography). These studies have demonstrated the potential of fNIRS technology in measuring cerebral hemodynamics with high temporal and spatial resolution. The results of this review indicate that fNIRS is a versatile neuroimaging tool with great potential in research and clinical applications.

Multimodal imaging agents derived from plasmonic nanoparticles have great potential for medical diagnostics. To date, dual-mode Raman-fluorescence gold-based nanolabels have remained outside conformed in vivo studies. To overcome this lack, gold nanorods coated by a polymer shell with incorporated cyanine 7 and a bimodal optical response were prepared. The study of nanolabels imaging capabilities was performed after their intravenous administration to laboratory mice. Natural biodistribution of the labels was obtained from SERS spectra of tissue sections, fluorescence images of organs, and gold concentrations determined in the digested tissues by ICP-AES. The comparison of distribution diagrams obtained from three techniques revealed that fluorescence tomography gives the underestimated labels content in the spleen, arising as a result of emission self-quenching. The consistency of SERS and ICP-AES results opens up the opportunity to use SERS as a complementary technique to assess semi-quantitative gold nanoparticle distribution in the tissues.

Single-objective light sheet fluorescence microscopy (LSFM) excels in high-speed imaging of large samples, but its limited fluorescence collection angle would degrade system resolution due to the fluorescence loss at low magnifications. To address this issue, we introduce a new method using a coaxial modulation module. By combining two sets of cylindrical lenses with the camera's rolling shutter, we achieve 3D imaging with an optimized fluorescence collection angle in low-magnification mode. Compared to advanced single-objective LSFM, our method provides a practical solution for low-NA oblique plane microscopy (NA 0.5) and enhances resolution by ~30% along the light propagation axis. We validated the system's resolution with fluorescent microspheres, achieving sub-micron lateral and sub-cellular axial resolutions. Furthermore, we have demonstrated its feasibility using GFP-labeled neurons and Siha cervical cancer cell samples for cell counting.

Accurate, label-free, non-destructive discrimination between head and neck squamous cell carcinoma (HNSCC) and keratoacanthoma (KA) remains challenging due to their overlapping morphology. We introduce a real-time, end-to-end hyperspectral imaging (HSI) workflow applied to 80 formalin-fixed, paraffin-embedded sections, each sampled with five 50 × 50-pixel ROIs and captured across 400–1000 nm to produce 128-band reflectance cubes. After reflectance calibration, Savitzky–Golay smoothing, and first-derivative preprocessing, a compact one-dimensional convolutional neural network achieved 87% accuracy, 93% sensitivity, 77% specificity, and AUC = 0.85 on a held-out test set. Spectral difference analysis revealed key biomarkers at the hemoglobin Q-band (630 nm) and OH overtone (917.5 nm), corresponding to vascular and extracellular matrix variations. This resource-efficient photonic platform enables rapid, automated “optical biopsy” without exogenous stains, offering scalable adjunctive diagnostics and a clear pathway toward intraoperative tissue classification.

This study validated the feasibility of visible spectroscopy in rapidly detecting Urinary Microalbumin (UALB). Based on 127 clinical urine samples, spectra ranging from 400 to 750 nm were collected using a microspectrometer. The successive projections algorithm (SPA) was used to screen for nine wavelengths highly correlated with UALB, and the spectral index (SI) method was fused to construct an m-SPA-SI strategy. The m-SPA-SI was combined with a random forest (RF) model for discriminant analysis. The results showed that the m-SPA-SI₆-RF model for low-dimensional spectra exhibited the best performance, with a training accuracy of 100% and improved testing accuracy and sensitivity of 97.37% and 0.9333, respectively. The number of wavelengths was reduced from 457 to 9. Research has confirmed that the combination of low-dimensional spectroscopy and a wavelength selection algorithm can achieve efficient discrimination of UALB, providing a new method for portable screening of diseases such as chronic kidney disease.