This study develops and substantiates a polarization-interferometric Jones-matrix approach for revealing hidden multifractal optical anisotropy in dehydrated biological soft-matter films. The method integrates Mach-Zehnder interferometry, digital holographic reconstruction of complex amplitudes, phase-selective speckle-field analysis, and statistical-multifractal processing of Jones-matrix images. Unlike intensity-based Stokes polarimetry, it provides amplitude and phase information and enables selective analysis of the coherent ballistic or weakly scattered polarization-preserving field component. Analytical relationships between real and imaginary Jones-matrix components and linear/circular birefringence mechanisms establish their interpretation as markers of spatial heterogeneity, orientational consistency, and phase organization of supramolecular polycrystalline networks. Using bile films, phase-selective mapping revealed distinct anisotropy manifestations in dendritic and spherulitic structures. Higher-order statistical moments and multifractal spectra described coordinate- and scale-dependent transformations. Primary validation on bile, whole blood, and dental canal irrigation films showed comparable integral-field accuracy but higher Jones-matrix informativeness after minimally scattered component isolation, supporting label-free biomedical diagnostics requiring further clinical validation.

Fluorescence microscopy is essential for visualizing cellular and tissue structures. When combined with a hyperspectral modality, it enables the acquisition of emission spectra that provide insight into the composition of fluorophores. These spectra can be used for fluorophore-based image segmentation; however, accurate spectral separation becomes challenging when fluorophore emission spectra overlap and mix within individual pixels. We present a spatially-corrected density peak clustering (SC-DPC) method that combines phasor transformation with histogram-based density-peak detection and additional spatial correction to achieve accurate and fast clustering. SC-DPC effectively handles large datasets and clusters that are not separable linearly in phasor space while maintaining high clustering accuracy and computational efficiency. Results with synthetic data and experimental multiphoton microscopic data from the mouse retina demonstrate that SC-DPC provides a robust, high-performance approach for hyperspectral fluorescence data segmentation.

In this study, the difference in behavior of the raw photoplethysmography (PPG) signal and the laser Doppler flowmetry (LDF) signal reflecting hemodynamics in the skin of upper extremities at brachial arterial occlusion is recorded and discussed. It is shown that at various locations on the hand, the raw PPG signal provides richer and previously little-known information on the redistribution of blood between the arterial, microcirculatory, and venous beds, while the conventional blood flow (BF) parameter recorded by LDF technique is less sensitive to it and largely does not capture this redistribution.

This study utilizes an integrated laser speckle (LSCI) and intrinsic optical signal imaging (IOSI) system to evaluate microvascular dynamics in murine pressure ulcer (PU) models. We compared a single prolonged compression (6 h) against repeated compressions (1.5 h/day for 7 days) using an enhanced spatiotemporal gradient analysis of LSCI data. In the repeated compression model, gradient analysis revealed a progressive perfusion decline by Day 4. By Day 7, perfusion gradients indicated near-complete flow loss, corresponding to severe hypoxia detected by IOSI, signifying sustained microvascular impairment. Conversely, the single compression model exhibited reversible alterations; LSCI gradients showed initial disruption followed by gradual recovery, with perfusion nearing baseline by Day 7. The LSCI-IOSI platform, emphasizing advanced gradient processing, systematically delineates microcirculatory compromise. This approach highlights the critical role of repeated ischemia-reperfusion in driving persistent dysfunction and demonstrates the utility of quantitative optical imaging for early detection and targeted intervention in PU progression.

Elastography is widely used to estimate tissue mechanical properties based on wave propagation characteristics. However, a major limitation is that elastography maps often fail to accurately delineate internal boundaries within heterogeneous tissues. To address this issue, we propose a novel deep learning-based framework, termed Boundary Detection Network (BDNet). Depth-resolved phase images capturing wave propagation are acquired using Optical Coherence Elastography (OCE). The proposed method is first evaluated on agar phantoms. Experimental results show that all evaluated models achieve comparable performance on single-boundary samples, with a mean absolute error (MAE) of 2 pixels. For dual-boundary phantoms, BDNet achieves an overall MAE of 4.33 pixels. Furthermore, on a human acne dataset, the proposed method provides the best performance, with an MAE of 19.11 pixels. The proposed BDNet demonstrates high accuracy and computational efficiency, providing a promising tool for assisting abnormal tissue morphology identification in clinical elastography applications.

Evaluating the mechanical properties of atherosclerotic plaques is crucial for precisely identifying rupture-prone lesions, as different plaque constituents dictate local biomechanical instability and directly inform targeted therapeutic intervention. In this study, we further achieved quantitative photoacoustic elasticity imaging (PAEI) and photoacoustic viscosity imaging (PAVI) by analyzing the rise time of the thermoelastic displacement and by measuring the phase delay of the photoacoustic signal relative to the laser pulse, respectively. Experiments on mice aortas have shown that PAVI is more sensitive to the changes in lipid core area during lipid plaque stage, whereas PAEI is more effective at detecting changes in fibrous cap thickness during fibrous plaque stage. Furthermore, PAEI and PAVI can effectively distinguish lipid plaques and fibrous plaques from normal vascular tissue. All results indicate that the PAEI-PAVI imaging system enables label-free, high-resolution visualization of the component-based biomechanical properties of atherosclerotic plaques, facilitating early detection and rupture risk assessment.

Optical coherence tomography (OCT) enables non-invasive volumetric retinal imaging. Conventional tabletop and handheld systems rely on skilled operators and patient cooperation. Mobile robot-assisted OCT (RAOCT) systems can address these challenges, but many depend on complex visual servo modules for low-latency eye tracking. We present an intensity variance-guided RAOCT system integrated on a wheeled mobile platform. The system uses one depth camera for coarse eye localization and a single pupil camera for real-time tracking. By calibrating a lookup table between image variance and distance within the near-eye region, the pupil camera provides indirect depth estimation for robotic servoing. A motorized reference arm, an electrically tunable lens, and an automated polarization controller further enable image-quality optimization. Experiments demonstrated 103.50 μm axial and 20.46 μm lateral tracking accuracy. The pupil-camera response time was 10.53 ms. Automated retinal OCT imaging was achieved, demonstrating the system's potential for point-of-care diagnostics in resource-limited environments.

Diabetic liver injury (DLI) is a major complication of diabetes mellitus (DM), disrupting hepatic glycogen and lipid metabolism. This study adopted photoacoustic imaging (PAI) to detect hepatic blood oxygen saturation, hemoglobin content, lipid deposition, and liver function reserve (LFR) in type 1 diabetic rodent models. Compared with the control group, the T1DM group presented significantly higher average hepatic oxygen saturation (p = 0.014), lower total hemoglobin (p < 0.001), and decreased lipid photoacoustic signal intensity (p = 0.045). No obvious difference was found in LFR between the two groups. In summary, PAI holds promising potential for reflection of altered hepatic hemodynamics and lipid in early STZ-induced type 1 diabetic liver injury.

Acne is a common inflammatory skin condition and frequently leads to long-term adverse clinical outcomes including scarring and pigmentation, negatively impacting Health Related Quality of Life (HRQoL). Monitoring of acne progression can improve understanding of lesion evolution, support evaluation of treatment response and provide potential biomarkers for future investigation of acne sequelae. This study presents a multi-functional optical coherence tomography (OCT) protocol integrating structural imaging, OCT-based angiography (OCTA), and optical coherence elastography (OCE) for longitudinal in vivo monitoring of acne. Thirteen lesions were evaluated across eight time points over 18 days. Quantitative parameters, including Young's modulus and vascular metrics, captured biomechanical and microvascular changes during acne evolution. The system enables continuous multimodal assessment, showing that stiffness and vascular alterations reflect lesion progression. This pilot longitudinal study establishes a quantitative foundation for dermatological research and suggests that multimodal OCT monitoring may provide biomarkers for assessing lesion progression and long-term clinical outcomes.

Hyperspectral imaging combined with plasmonic nanoparticles offers powerful opportunities for nanoscale biological diagnostics, yet conventional push-broom systems are limited by slow acquisition and reduced spatial resolution. We report a wide-field hyperspectral imaging platform integrating a custom linear variable tunable filter (LVTF) with reflected light microscopy (RLM) for multiplexed plasmonic nanoparticle (NP) imaging. While LVTF-based systems have been used in industrial contexts, their adaptation to RLM microscopy enables high-resolution, high-contrast spectral imaging of NPs on biological samples. The system acquires data across 380-720 nm with 7-20 nm spectral resolution while preserving spatial detail. We demonstrate imaging of NPs bound to cancer cell membranes and classification of four distinct NP types. A trained support vector machine achieves an overall F-score of ~97%, significantly outperforming RGB-based approaches. Compared to push-broom systems, this platform provides faster acquisition, improved spatial resolution, and a compact, cost-effective solution for multiplex NP detection in bioimaging and immunoplasmonic applications.

Subtle differences between cancerous and normal regions in unstained tissue sections limit the performance of conventional diagnostic methods. Mueller matrix polarimetry provides comprehensive information on tissue polarization responses; however, the intrinsic coupling of optical effects in the original matrix elements complicates direct histological interpretation. In this study, a pixel-level polarization dataset from clinical lung cancer and basal cell carcinoma sections is established to systematically compare low-dimensional physical decomposition and high-dimensional combination mapping for cancer-region identification. Conventional decomposition approaches exhibit limited discriminative capability and significant class overlap in complex tissues. To address this limitation, a vectorial metric norm spectrum incorporating multi-order features is developed, enabling enhanced representation of polarization characteristics. The proposed high-dimensional mapping framework achieves accurate differentiation between cancerous and normal regions and demonstrates strong performance in cross-validated evaluations. This work establishes a progressive strategy from physical decomposition to high-dimensional representation for optical-assisted pathological analysis.

The skin performs essential functions between the internal and external environments of the human body, such as protection against microorganisms, substances and radiation, maintenance of body temperature, prevention of excessive water loss, and production of vitamin D. However, many of these functions are reduced with aging and can be accentuated in photoaged skin. Photodynamic therapy (PDT) is a noninvasive technique used in the treatment of cancer, microbial infections, precancerous changes and for cosmetic purposes. Research on topical PDT suggests antibacterial, anti-inflammatory and immunomodulatory effects on keratinocytes, fibroblasts, sebaceous glands and hair follicles. Therefore, this pilot study evaluated PDT on the face using 5-aminolevulinic acid (ALA) and methyl 5-aminolevulinate (MAL), combined with amber LED and laser irradiation in women aged 40-55 years. PDT was effective in rejuvenating photoaged skin, reducing fine lines, smoothing wrinkles, and improving skin softness, firmness and sagging, with minimal side effects.