Biomedical optics express最新文献

Birefringence of collagen fibers influences second-harmonic generation (SHG) polarimetric measurements by introducing phase retardation for the incident fundamental beam and the SHG signal. The effect of birefringence on double Stokes polarimetry (DSP) parameters is investigated by imaging unstained rat tail tendon sections of varying thicknesses. In thin sections (≈10 μm thick), birefringence effects can be neglected; however, parameters combining linear and circular polarization states are affected, indicating a presence of complex-valued achiral susceptibility ratio due to fiber chirality. In thicker samples (≈80 μm thick), birefringence significantly affects DSP polarimetric and ultrastructural parameters, although approximate in-plane fiber orientation can still be retrieved. DSP polarimetric parameters enable rapid collagen characterization and are suited for large-area whole-slide investigations in histopathology.

Traditional cochleostomy relies on mechanical drilling, which carries inherent risks of acoustic and thermal trauma. In this study, we performed a quantitative analysis of phosphoric acid gel (PAG)-mediated chemical cochleostomy in ex vivo mouse cochleae using a 1.06-µm swept-source optical coherence tomography (SS-OCT) system. The imaging results demonstrated that 1.06-µm OCT provides high contrast for visualizing internal anatomic features, such as the organ of Corti, and changes in cochlear architecture during the chemical etching process. Quantitative evaluation revealed a reduction in bony wall thickness and enhanced visibility of intracochlear conduits post-PAG-application. Notably, the PAG-mediated thinning mitigates signal attenuation by reducing scattering from the dense bony wall, thereby improving OCT imaging contrast of internal structures. Real-time M-mode OCT imaging successfully captured dynamic reaction kinetics, providing a reliable feedback mechanism for identifying the etching endpoint. These findings suggest that 1.06-µm SS-OCT is a feasible guidance tool for precise, hearing-preservation cochlear interventions.

Malignant melanoma tends to metastasize early, and its prognosis is extremely poor and life-threatening in advanced stages. Tumor size and lesion thickness are the most common imaging parameters used to detect melanoma. While changes in mechanical properties during the disease process are closely related to the tumor's basic composition and cellular activity, and are directly linked to its invasiveness and metastatic potential. Here, we present a dual-modality imaging system that combines photoacoustic absorption imaging (PAI) and photoacoustic viscoelasticity imaging (PAVEI) for the staging biopsies of melanoma. The tumor size and thickness can be accurately evaluated by PAI, and PAVEI helps to characterize the tumor viscoelasticity through the phase delay of photoacoustic signals. Experimental results demonstrate that the PAI-PAVEI system can comprehensively characterize the absorption characteristics and mechanical properties of tumor lesions, thereby providing essential reference for the early diagnosis, staging assessment, and efficacy prediction of melanoma.

A deeper understanding of brain function requires resolving the intricate networks formed by neurons at the microscopic scale. Imaging the connecting nerve fibers remains a significant challenge, particularly due to the difficulty of resolving crossing fibers using conventional optical imaging techniques. Computational Scattered Light Imaging (ComSLI) addresses this by using obliquely incident light to reconstruct the in-plane orientations of nerve fibers based on their scattering profiles, enabling the resolution of fiber crossings. One approach is to use an LED display as a light source, which allows for illuminating the sample by arbitrary patterns and measuring full scattering patterns as well as angular scattering profiles. However, when using an LED display instead of a high-intensity LED spot, ComSLI is limited by a low signal and acquisition times of several seconds per image. To overcome these limitations, this work introduces Hadamard basis sampling for the angular illumination patterns, allowing an increase in illumination intensity and a corresponding reduction in measurement time. Compared to standard sampling approaches, this method yields significantly sharper defined scattering peaks, resulting in enhanced angular resolution of the scattering profiles. The Hadamard-based illumination enhances the reconstruction of cortical fiber organization, overcoming a key limitation in challenging ComSLI applications and neuroscience.

Remote photoplethysmography (rPPG) extracts the cardiac pulse from facial video by isolating chromatic variation due to wavelength-dependent hemoglobin absorption. We report that this signal model is incomplete. A second cardiac-frequency signal is present in skin video: an isochromatic pulsation along the skin's mean reflectance direction, arising from ballistocardiographic modulation of skin surface geometry and changes in the effective scattering cross-section of the dermal vascular bed. This isochromatic component is conflated with the chromatic signal by every rPPG method we evaluated, including chrominance-based methods (POS, CHROM) and other projection or decomposition methods (OMIT, LGI, PBV, ICA). The two signals share the cardiac frequency but differ in phase and signal fidelity, and their relative directions vary with skin tone, lighting, and recording conditions. To assess whether the isochromatic component contributes significant error to rPPG, we constructed chrominance Phase-Aware Cardiac Eigenprojection (cPACE), a method that removes the isochromatic direction via a subject-adaptive projection orthogonal to the measured skin reflectance before extracting the cardiac signal through eigen-vector projection. Across UBFC-Phys (rest and speech) and the CMU-rPPG India and Sierra Leone cohorts, cross-ROI phase-locking values of the recovered signal are systematically higher under cPACE than under any other method evaluated, indicating cleaner waveform recovery. The resulting heart rate estimates achieve mean absolute error of 1.7, 3.7, 1.7, and 1.1 BPM, respectively, on these cohorts, compared to 2.8, 9.5, 16.8, and 17.8 BPM for POS with homodyne envelope correction applied for parity. Together, these results show that the isochromatic component is a major source of error in existing rPPG methods, and future methods should explicitly account for it.

Accurate assessment of knee osteoarthritis (KOA) is limited by the current arthroscopy's reliance on subjective visual inspection. We propose a 3D spatio-spectral automatic grading framework combining deep learning with chromatic differential confocal matrix-based 3D topography and hyperspectral imaging (CDCM-THI). Our digital micromirror device-based system simultaneously captures high-resolution 3D micro-topography and hyperspectral cartilage signatures (lateral: 3.91 µm, axial: 1.96 µm, spectral: ∼15 nm). To analyze this multi-modal data, we developed an improved ResNet50 late fusion network optimized with a consistency-enhanced loss function. Evaluated on ex vivo clinical samples, our framework achieved an average cross-validation accuracy of 94.83%, significantly outperforming single-modal baselines. This non-destructive approach bridges the gap between topographical assessment and broadband optical analysis, providing a powerful quantitative tool for future deep learning-assisted arthroscopic integration.

Accurate knowledge of tissue absorption (μ _a ) and reduced scattering (μ_s '), parameters is required to plan and monitor laparoscopic chemophototherapy (CPT) in ovarian cancer, including light dosimetry and quantitative fluorescence mapping of porphyrin-phospholipid (PoP) photobleaching and light-triggered doxorubicin (Dox) release. We implemented a depth-sensitive, multi-frequency laparoscopic spatial frequency domain imaging (SFDI) framework to improve optical-property estimation in layered tissue. A DMD-based laparoscope imaged two-layer phantoms with controlled optical contrasts and superficial thicknesses. Spatial-frequency subsets associated with different penetration depths were independently fit to recover μ _a and μ_s ', and compared with a two-layer diffusion model. Recovered μ_s ' values remained bounded by the known layer references and shifted monotonically toward the superficial value as spatial frequency and top-layer thickness increased, approaching a single-layer response at high frequency/thick layers. Quantitative model comparison showed δ-P1 variants outperformed the standard diffusion approximation, reducing RMSPE between modeled and measured μ_s ' to 0.8-6.5% (silicone/silicone) and 1.6-8.3% (silicone/intralipid), whereas SDA errors reached ∼13.8% and 21.1%, respectively. This approach demonstrates multi-frequency laparoscopic SFDI as a practical initial step for depth-sensitive fluorescence correction for individualized CPT treatment planning and monitoring.

This study presents classification models trained to diagnose and grade prostate cancer using fresh prostate biopsies. We compare the performance of classification models with optimised sensitivity and specificity (standard models) with application-specific models designed to maximise sensitivity and negative predictive value (NPV). Standard models achieve 80% sensitivity and 81% specificity. Application-specific models, calibrated to 90% sensitivity and 95% NPV, are intended to provide clinicians with a tool they can use with confidence to support intraoperative decisions, specifically to improve tissue retention during biopsy procedures and to ensure clear surgical margins. To this end, we introduce a 5-layer algorithm that combines 5 application-specific models chosen for overall best performance. This algorithm can reduce the number of biopsy samples required for diagnosis by 47% while maintaining 90% sensitivity, 95% NPV, and 62% specificity. All models are independently validated using two large patient cohorts. These results support the targeted use of Raman spectroscopy for real-time tissue analysis in diagnostic and intraoperative settings. The technology's clinical value as a decision-support tool aligns with the shared goal of pathologists and urologists to reduce the number of prostate biopsy cores while maintaining high sensitivity for clinically significant cancer. Prior studies have improved biopsy efficiency, but their performance has been variable, and concerns remain regarding underdetection of significant disease, revealing the need for approaches that improve biopsy efficiency without increasing diagnostic risk. The technology described here provides a realistic solution for targeted biopsy guidance to support more precise and evidence-based clinical decisions.

Corneal confocal microscopy (CCM) enables non-invasive imaging of the sub-basal nerve plexus for early diagnosis of diabetic neuropathy, but its utility is hindered by inherent noise and low contrast in raw images. We present NerveBoost, a weakly supervised framework for CCM denoising and enhancement guided by anatomical priors. Unlike supervised methods requiring paired clean data, NerveBoost uses binary nerve masks to construct a pseudo-target via region-specific gamma correction. A composite loss function integrates weighted reconstruction, gradient consistency, background smoothness, and foreground-background contrast constraints to jointly optimize noise suppression and structural enhancement within an encoder-decoder architecture. Results indicate that NerveBoost effectively enhances nerve visibility while maintaining structural fidelity, offering a robust and efficient preprocessing solution for clinical CCM analysis without requiring paired ground-truth data.

[This corrects the article on p. 2747 in vol. 17, PMID: 42145706.].