Journal of Biomedical Optics最新文献

Significance: Optical coherence tomography (OCT) systems are traditionally decomposed into engine and scanner components with an expensive and noise-prone analog interface to communicate the scan pattern between the two. Although simple and convenient, analog signals are susceptible to interference and require expensive hardware to generate with appropriate precision for OCT.

Aim: To overcome these limitations, we implemented a digital interface for our OCT system using low-cost embedded microprocessors and custom PC software, exploiting recent trends toward digital servo drivers for optical scanning.

Approach: Our interface features USB interfacing with a PC for scan pattern download and position feedback upload, 50-kHz communication rate, external triggers with adjustable downsampling, and no external power requirements.

Results: We quantitatively assessed the latency, noise characteristics, and imaging performance of our digital interface in comparison with a conventional analog system that is an order of magnitude more expensive. The signal analysis confirmed that the digital interface reliably transmitted the intended scan pattern to the galvanometer driver and significantly reduced noise in the position feedback signal. High-speed laser trajectory tracking during sparse raster scanning revealed that discrepancies in the analog feedback signal did not reflect actual galvanometer positioning errors; both interfaces achieved equivalent spatial accuracy. Resolution testing demonstrated that both interfaces produced comparable OCT image quality, with no discernible difference up to the system's resolution limit, whereas reconstruction based on digital interface position feedback outperformed other methods when demanding scan patterns, such as spiral scanning, were applied. To support reproducibility and system integration, we developed a custom printed circuit board (PCB), enabling a compact and robust configuration for future OCT deployments. A simplified version of the firmware is supported by our open-source library vortex.

Conclusions: Together, these results demonstrate quantitative and qualitative equivalence of the interfaces, despite an order of magnitude reduction in cost. We released open-source software, PCB schematics, design files, and a bill of materials so that the OCT community can benefit from these improvements and cost savings.

Significance: The hybrid diffuse optical system (hDOS) is a fully automated platform designed to bring advanced optical monitoring closer to clinical practice. Many existing systems lack automation, multiparametric output, and operator independence, limiting their use in demanding environments such as intensive care. hDOS integrates time-domain near-infrared spectroscopy, diffuse correlation spectroscopy, and pulse oximetry to assess both tissue oxygenation and perfusion. Although initially developed in the context of vascular occlusion tests, its modular design makes it suitable for broader applications, including trauma, surgery, anesthesia, and studies in healthy subjects.

Aim: It aims to design and validate hDOS, focusing on precision, repeatability, and usability for peripheral microvascular monitoring in both clinical and research settings.

Approach: Validation included test-retest measurements, a 7-month clinical deployment in the critical care, and a comparison with a commercial continuous wave NIRS device (INVOS 5100C).

Results: The device underwent extensive validation, accumulating over 200 h of usage across $\sim 150$ measurement sessions. The system showed high precision (test-retest CV $<1.2\%$ for oxygenation, $<13\%$ for perfusion), stable long-term performance, and lower variability than INVOS. It has also detected statistically significant differences during VOTs and detected hemodynamic impairment in ICU patients ( $n=100$ ) compared with healthy volunteers ( $n=37$ ).

Conclusions: hDOS performed well in both bench and clinical settings, offering a unique combination of parameters in a fully automated, self-contained platform.

Significance: Photoplethysmography (PPG) is a widely used optical technique for the noninvasive monitoring of cardiovascular parameters. However, its accuracy may be affected by variations in skin pigmentation due to the strong absorption properties of melanin, particularly at visible wavelengths.

Aim: We aimed to investigate how skin tone influences PPG signal signals by developing a pulsatile vascular finger phantom with interchangeable skin layers, characterizing their optical properties across green, red, and infrared wavelengths and evaluating their impact on PPG signal features.

Approach: The finger phantom included three optically characterized, interchangeable skin layers representing pale, medium, and dark tones, as well as a custom-made silicone vessel embedded in an anatomically and mechanically characterized structure. PPG signals were recorded in reflectance mode using a custom-made finger clip probe in an in vitro cardiovascular system. Signal features, including signal-to-noise ratio, peak-to-peak amplitude, and area under the curve, were analyzed.

Results: Analysis revealed statistically significant differences ( $p<0.001$ ) between skin tones, with signal degradation increasing with skin pigmentation.

Conclusions: These findings suggest there is a measurable impact of skin pigmentation on the PPG signal and highlight the need for further research to improve the equity of light-based sensing technologies across all populations. We provide an advancement for future work in developing in vitro models to assess optical sensing performance across diverse skin tones.

Significance: Although multi-layer diffuse correlation spectroscopy (DCS) analytical models have been proposed to reduce contamination from superficial signals when probing cerebral blood flow index (CBFi), a comprehensive comparison and clear guidance for model selection remain lacking. This report aims to address this gap.

Aim: We aim to systematically compare three DCS analytical models: the semi-infinite, two-layer, and three-layer models, with a focus on their fundamental differences, data processing approaches, and the accuracy and reliability of CBFi estimation. We also provide practical recommendations for selecting the most appropriate model based on specific application scenarios to support researchers in applying DCS effectively.

Approach: Experimental data were generated by simulating a four-layer slab head model using the Monte Carlo eXtreme toolkit. We evaluated various fitting strategies for three DCS models: early time lag range (ETLR) fitting with or without treating the coherence factor $\beta$ as a fitting parameter for the semi-infinite model, single-distance (SD) and multi-distance (MD) fitting for the two- and three-layer models. We then compared their performance in terms of CBF sensitivity, recovery of relative CBFi (rCBFi) changes, accuracy of absolute CBFi estimates across different source-to-detector separations ( $\rho =20$ , 25, 30, and 35 mm), ability to separate the crosstalk from extracerebral layers [scalp BFi (SBFi), and skull BFi (BBFi)], sensitivity to parameter assumption errors, and time-to-result, using the respective optimal fitting strategies for each model.

Results: The optimal fitting methods for estimating CBFi are ETLR fitting with a constant $\beta$ for the semi-infinite model, SD fitting with $\beta$ fixed for the two-layer model, and MD fitting for the three-layer model. The two-layer and three-layer models exhibit enhanced CBFi sensitivity, approaching 100%, compared with 36.8% for the semi-infinite model at $\rho =30\mathrm{mm}$ . The semi-infinite model is suitable only for rCBFi recovery at a larger $\rho$ ( $\ge 30\mathrm{mm}$ ). In contrast, the two-layer model is appropriate for both CBFi and rCBFi recovery across all tested $\rho$ values (20, 25, 30, and 35 mm in this work), although its robustness declines as $\rho$ increases. The three-layer model enables simultaneous recovering of CBFi, SBFi, and rCBFi. Among these, the two-layer model is the most effective at mitigating the influence of extracerebral BFi, whereas CBFi estimates from the semi-inf

Significance: Optical coherence tomography angiography (OCTA) offers dye-free, three-dimensional views of skin microvasculature, yet progress in developing reliable and quantitative solutions for vessel architecture analysis is slowed by heterogeneous preprocessing practices, scarce annotated data, and limited evaluation metrics.

Aim: We assess how typical OCTA preprocessing steps influence the accuracy of deep learning vessel segmentation, and we identify network designs and metrics best suited to OCTA dermatological data.

Approach: Experiments use the open DERMA-OCTA dataset containing 330 volumes from different skin conditions; each volume is additionally provided in five progressively pre-processed versions: original, Bscan normalization, projection artifact attenuation, contrast enhancement, and vesselness filtering. Segmentation is performed with representative 2D and 3D deep learning approaches. Besides standard segmentation metrics, evaluation includes the connectivity-area-length index, which proved particularly effective for assessing dermatological vessel segmentation.

Results: The analysis shows that Bscan normalization, projection artifact attenuation, and contrast enhancement incrementally improve segmentation accuracy, whereas vesselness enhancement can impair segmentation performance. Among the tested architectures, 2D models achieved the highest segmentation performance, although 3D approaches proved more effective for deeper tissue layers. Testing across different pathologies revealed challenges in model generalization to varied vascular patterns.

Conclusions: Combining 2D and 3D models and using topology-aware indices provide a full, clinically relevant evaluation of algorithm performance.

Significance: Accurate intraoperative assessment of intestinal tissue viability is critical in determining the extent of resection in cases of intestinal ischemia. Current evaluation methods are largely subjective and lack the precision required for reliable decision-making during surgery.

Aim: We aim to develop and validate a hyperspectral imaging (HSI) system combined with machine learning (ML) to objectively assess intestinal wall viability and differentiate between reversible and irreversible ischemia.

Approach: A portable HSI system was used to acquire spectral data from rat models with induced intestinal ischemia at different time points (1, 6, and 12 h). Tissue oxygen saturation was calculated using a two-wavelength algorithm. Spectral data were classified using an ML pipeline based on principal component analysis (PCA) and the XGBoost algorithm, trained on histologically validated tissue classes.

Results: Tissue saturation decreased with prolonged ischemia (from 66% in healthy tissue to 21% after 12 h). Classification accuracy using PCA features reached 98% for intact tissue, 95% for possibly reversible ischemia, and 97% for irreversible ischemia. Classification maps closely matched tissue saturation distributions and histological findings. Initial clinical testing confirmed the system's sensitivity to ischemic changes in human subjects, although further training on human data is required for ML application.

Conclusions: HSI combined with ML provides an effective, non-invasive tool for real-time intraoperative assessment of intestinal viability. This approach improves the objectivity of surgical decision-making and may reduce unnecessary resections.

Significance: Understanding thermal effects on tissue optical properties is fundamental for optimizing laser-based medical interventions. We address the critical knowledge gap of temperature-dependent changes in porcine dermis optical properties.

Aim: We explore the thermal damage influence on the excised dermis optical properties at wavelengths from 400 to 1100 nm.

Approach: Using a double-integrating-sphere system and inverse adding-doubling, we determined absorption, ${\mu }_a$ , and reduced scattering, ${\mu }_s^{\text{'}}$ , coefficients before and after a 2.5-min thermal exposure.

Results: We observed non-linear changes in both ${\mu }_a$ and ${\mu }_s^{\text{'}}$ across temperature regimes. Minimal changes occurred at 37°C and 43°C. At 50°C, slight increases in both coefficients were observed. Significant alterations occurred at 60°C, with substantial increases in ${\mu }_s^{\text{'}}$ and variable changes in ${\mu }_a$ depending on wavelength region. At 70°C, ${\mu }_s^{\text{'}}$ values remained elevated, whereas ${\mu }_a$ showed mixed responses, with some wavelength regions decreasing, indicating progressive structural breakdown. The Arrhenius damage model showed an exponential increase with temperature.

Conclusions: We reveal complex thermal-induced changes in tissue optical properties, particularly at higher temperatures. Findings reinforce a critical threshold between 50°C and 60°C where significant changes occur. The non-linear, wavelength-dependent responses emphasize the need for comprehensive data in laser-tissue interaction modeling, with important implications for optimizing laser-based medical treatments.

Significance: Lumen segmentation in intravascular optical coherence tomography (IVOCT) images is essential for quantifying vascular stenosis severity, location, and length. Current methods relying on manual parameter tuning or single-frame spatial features struggle with image artifacts, limiting clinical utility.

Aim: We aim to develop a temporal residual U-Net (TR-Unet) leveraging spatiotemporal feature fusion for robust IVOCT lumen segmentation, particularly in artifact-corrupted images.

Approach: We integrate convolutional long short-term memory networks to capture vascular morphology evolution across pullback sequences, enhanced ResUnet for spatial feature extraction, and coordinate attention mechanisms for adaptive spatiotemporal fusion.

Results: By processing 2451 clinical images, the proposed TR-Unet model shows a well performance as Dice coefficient = 98.54%, Jaccard similarity (JS) = 97.17%, and recall = 98.26%. Evaluations on severely blood artifact-corrupted images reveal improvements of 3.01% (Dice), 1.3% (ACC), 5.24% (JS), 2.15% (recall), and 2.06% (precision) over competing methods.

Conclusions: TR-Unet establishes a robust and effective spatiotemporal fusion paradigm for IVOCT segmentation, demonstrating significant robustness to artifacts and providing architectural insights for temporal modeling optimization.

Significance: The effects of optogenetic stimulation (OS) on in vitro neural network behavior were studied through a reservoir computing-based obstacle avoidance task, revealing its impact on the task-processing capabilities of the network. Furthermore, it is demonstrated that a minimal output of signals from 15 neurons in the network is sufficient to achieve stable task control, with a success rate exceeding 95%. The optogenetically enhanced biological reservoir computing frame could find applications in neuro-robotic control and brain-inspired intelligence.

Aim: We aim to utilize optogenetically controlled in vitro neural networks and the first-order reduced and controlled error (FORCE) learning algorithm to achieve obstacle avoidance in neuro-robotic systems.

Approach: We presented an all-optical biological reservoir computing framework that leverages optogenetics and calcium imaging to precisely regulate and record neuronal activities. A closed-loop system was developed incorporating the FORCE learning algorithm, which guided a virtual car through obstacle avoidance tasks.

Results: The system demonstrated high accuracy and efficiency in navigating obstacles, achieving optimal performance after $\sim 150s$ of training. OS significantly improved the obstacle avoidance success rate, enhancing the system's adaptability and accuracy.

Conclusions: The results highlight the potential of optogenetically controlled biological neural networks in neuro-robotic systems, showcasing their capability to achieve accurate and efficient obstacle avoidance through physical reservoir computing.