Biomedical optics express最新文献

Chemical burns account for 10-22% of ocular injuries, with alkali burns being the most frequent and severe. There is a significant need for treatment of these injuries, as well as an effective imaging method to monitor both the injury and treatment progression. Currently, there are limited options for monitoring the burn and treatment response of the sub-micron structures within the corneal tissue in vivo and non-destructively, due to factors such as the resolution limit of optical imaging systems. Optical coherence tomography (OCT) is a non-invasive imaging technique that provides high-resolution, cross-sectional imaging of biological tissue at good imaging depths. Due to the diffraction limit, OCT resolution is limited to the micron scale, preventing monitoring of nanoscale changes. Nanosensitive optical coherence tomography (nsOCT) overcomes this limitation by recovering high spatial frequency information through spectral encoding, enabling assessment of sub-micron tissue features. Here, we present variations of nsOCT with improved sensitivity for detecting nanoscale tissue changes. These methods were applied to a corneal alkali burn model and a treatment model using mesenchymal stromal cell-derived extracellular vesicles. Correlation of axial spatial frequency profiles (ASFP) and mapping of ASFP differences enabled sensitive detection of nanoscale structural changes post-injury and during treatment progression. A statistically significant disruption of the sub-micron structure was observed after injury, followed by partial restoration during the reparative phase. The proposed methods provide improved sensitivity to nanoscale tissue changes and may support monitoring of cell therapy and other medical applications for diagnostic purposes.

Multi-contrast polarization-sensitive optical coherence tomography (PS-OCT) provides complementary structural and polarization information that may improve epidural tissue classification. Here, we evaluated deep learning fusion of four PS-OCT channels, including intensity, phase retardation, degree of polarization uniformity (DOPU), and optic axis, using porcine (n = 6) and human (n = 5) spinal specimens. We benchmarked six multi-channel fusion strategies: Probability averaging, feature concatenation, trainable weighted output, shared-stage resnet, merged multi-channel input, and pooled data. Across subject-level nested cross-validation, multi-channel methods achieved modest but consistent accuracy improvements over the best single-channel baselines while reducing subject-to-subject variability. On porcine data, Probability Averaging increased mean validation accuracy by 3.46% (93.07% vs. 89.61% for the best single-channel baseline). On human data, fusion methods maintained the high single-channel baseline (approximately 97% to 98%) while modestly improving stability, with probability averaging achieving the highest mean validation accuracy (98.26%). In cross-testing, trainable weighted output achieved 92.32% versus 91.53% for the best porcine single-channel baseline, and probability averaging achieved 97.87% versus 97.75% for the best human single-channel baseline. On the human cohort, the gain in mean accuracy was small, but multi-channel fusion produced a statistically significant reduction in between-subject variance (Levene's p = 0.0108) and removed the need to know in advance which single channel would generalize best. Overall, multi-channel fusion improved classification performance and robustness, with probability averaging offering a favorable balance between accuracy and complexity because it requires no additional training beyond single-channel models.

For accurate glaucoma diagnosis and monitoring, reliable retinal layer segmentation in OCT images is essential. However, existing 2D segmentation methods often suffer from slice-to-slice inconsistencies due to the lack of contextual information across adjacent B-scans. 3D segmentation methods are better for capturing slice-to-slice context, but they require expensive computational resources. To address these limitations, we propose a 2.5D segmentation framework that incorporates a novel cross-slice feature fusion (CFF) module into a U-Net-like architecture. The CFF module fuses inter-slice features to effectively capture contextual information, enabling consistent boundary detection across slices and improved robustness in noisy regions. The framework was validated on both a clinical dataset and the publicly available DUKE DME dataset. Compared to other segmentation methods without the CFF module, the proposed method achieved an 8.56% reduction in mean absolute distance and a 13.92% reduction in root mean square error, demonstrating improved segmentation accuracy and robustness. Overall, the proposed 2.5D framework balances contextual awareness and computational efficiency, enabling anatomically reliable retinal layer delineation for automated glaucoma evaluation and potential clinical applications.

Oblique plane microscopy (OPM) is a single-objective implementation of light sheet fluorescence microscopy (LSFM) that enables volumetric imaging in an open-top configuration suitable for large tissue specimens. Such applications require both detailed visualization of cellular structures and rapid coverage of large areas. However, achieving these two capabilities simultaneously within a single OPM system remains challenging. Here, we present a dual-scale and dual-channel OPM platform (D²OPM) that alternates between two primary objectives mounted on a turret to enable micro-scale high-resolution imaging and meso-scale high-throughput imaging within a unified optical architecture. To maintain consistent remote-focus performance during objective switching, the illumination geometry is adjusted without introducing additional imaging arms. The micro-scale mode provides submicron lateral resolution for cellular assessment, while the meso-scale mode enables rapid centimeter-scale image acquisition. Because high-resolution volumetric imaging inherently limits acquisition speed, controlled under-sampling is applied in the micro-scale mode and combined with deep learning-based image restoration to preserve morphologic detail while increasing throughput. Dual-channel fluorescence imaging is achieved using dual-wavelength excitation and spectral separation onto a single camera. Imaging of human colon, stomach, and pancreatic specimens demonstrates structural and cytologic features comparable to hematoxylin and eosin histology, with quantitative agreement in nucleus size measurements. These results establish D²OPM as a compact multi-resolution LSFM platform for rapid histomorphologic assessment.

Optoretinography (ORG) enables non-invasive measurement of stimulus-evoked photoreceptor deformation using phase-sensitive optical coherence tomography (OCT). In this study, we evaluated how lateral resolution, acquisition speed, and spatial and inter-trial averaging influence velocity-based phase ORG measured with a raster-scanning spectral-domain OCT platform configured to resemble clinically realistic hardware. Seven healthy eyes were imaged while varying beam diameter (1.6-4.8 mm), B-scan rate (400-800 Hz), and averaging strategies. The characteristic biphasic response, a rapid contraction followed by slower elongation, was consistently observed across all conditions. Increasing NA improved structural cone visibility but did not proportionally enhance averaged ORG metrics, while moderate NA often yielded more stable signals. Higher acquisition speeds sharpened the measured contraction dynamics but reduced the signal-to-noise ratio due to increased phase noise. Both spatial and trial averaging substantially improved SNR. These findings demonstrate that repeatable phase-based ORGs can be achieved without cellular resolution or ultrahigh scan rates, and provide practical guidance for implementing proto-clinical ORG on conventional OCT systems.

Autofluorescence imaging provides a snapshot of tissue biochemistry that can be used to identify dysplastic tissue. However, deployment in multimodal single-fiber imaging systems is limited by system background fluorescence. We propose to improve the signal-to-background ratio (SBR) through the removal of background emission in fiber-optic catheters for autofluorescence and optical coherence tomography. We collected power and spectral measurements of optical components to characterize their emissions using 450 nm excitation. We measured the per-meter fluorescence contribution of single-mode, double-clad, and multimode optical fibers. By tailoring the detection filter to the emission spectra of the target tissue and system background, we have demonstrated an average of 7.9x improvement in SBR.

[This corrects the article on p. 1953 in vol. 17, PMID: 42110997.].

Camera-based contactless oxygen saturation estimation enables comfort and hygienic long-term monitoring. Deep learning methods have shown promise in extracting rich spatiotemporal features from facial videos, yet they often struggle to generalize across domain shifts, such as inter-subject variability, skin tone differences, spectral discrepancies, sensor system changes, and lab-to-clinic transitions. To address this, we propose a multi-level target unsupervised domain adaptation framework for contactless oximetry from multispectral facial video. Building upon a previously established spatiotemporal 3D CNN baseline, the proposed method aligns source and target feature distributions across multiple network hierarchies together with a dynamically weighted training scheduler and adaptive batch normalization. The framework is systematically evaluated on three custom datasets acquired with two different sensor systems, covering controlled laboratory experiments and real-world clinical recordings. In leave-one-participant-out validation on a breath-holding dataset of 23 healthy subjects, the proposed method reduces the mean absolute error (MAE) from 2.31 % to 1.98 %, desaturation-specific MAE from 3.26 % to 2.60 %, and improves Pearson's correlation coefficient from 0.64 to 0.71. Consistent performance gains are observed under cross-skin-type, cross-spectral, and cross-sensor-system domain shifts. In a clinical validation involving real sleep apnea patients with 796 oxygen desaturation events, the error between our estimations and polysomnography ground truth stays within 2 % for 90 % of the recorded time. These results demonstrate that the proposed deep domain adaptation framework substantially enhances the robustness and generalization of camera-based contactless oximetry, demonstrating its potential for practical deployment in clinical and real-world monitoring.

Wavefront shaping (WS) is a powerful technique to overcome optical scattering in biological tissue, enabling deep-tissue focusing and advancing biomedical optics. For practical in vivo applications, WS must adapt to dynamic scattering environments induced by physiological activities such as blood flow. Conventional strategies to address this challenge rely solely on accelerating operational speed, yet still face the challenge of keeping pace with rapid decorrelation caused by fast-varying components such as flowing red blood cells, leading to degraded focusing efficiency. To overcome this limitation, we propose a channel-selected WS (CS-WS) strategy that classifies scattering channels into slow-varying (extravascular tissue) and fast-varying (blood vessels) based on speckle decorrelation speed, optimizing only slow-varying channels while deactivating fast-varying ones. We validate CS-WS via in vivo experiments on live mouse ears using a fast closed-loop feedback system with a single iteration time of 4 ms. Results show that CS-WS achieves 38% faster convergence (398 vs. 642 iterations) and 27% higher focal enhancement (14.85 vs. 11.68) than conventional full-aperture WS. This simple approach provides a practical solution for dynamic in vivo scenarios, offering broad potential for robust deep-tissue optical focusing in biomedical applications.

Accurate beam targeting is critical in radiation therapy, particularly for stereotactic treatments of mobile organs where fiducial markers are routinely used for image guidance. However, current approaches do not provide real-time confirmation that the therapeutic radiation beam is accurately delivered to the intended target during irradiation. This study presents a preliminary proof-of-concept evaluation of a dual-modality fiducial marker that combines a liquid injectable fiducial material for X-ray computed tomography (CT) contrast with a biocompatible radioluminescent ink, enabling both pre-treatment localization and intra-treatment optical verification. The proposed formulation can be delivered by syringe into anatomical sites and readily visualized on CT for treatment planning and alignment. During irradiation, Cherenkov light generated within tissue excites the marker, producing visible luminescence detectable with a time-gated imaging system and enabling Cherenkov-excited luminescence imaging (CELI) of beam position in real time. Phantom studies demonstrated detection sensitivity for injected volumes as small as 20 µL and radiation doses as low as 2.8 cGy, with measurable signals at depths up to 8 mm in tissue-mimicking media. The liquid fiducial provided clear CT visualization of the injection site prior to treatment, while CELI enabled real-time visualization of the radiation beam during irradiation in vivo. A strong spatial correlation was observed between emitted luminescence and radiation beam location. These results demonstrate the feasibility of a simple subcutaneous injectable fiducial marker system for real-time beam verification in radiation therapy using clinically Cherenkov imaging cameras.