Journal of Biomedical Optics

Short-wave infrared (SWIR) sensors promise to expand the capabilities of optical sensing technologies but the lack of robust data characterising tissue-constituent optical properties in the SWIR makes instrument design challenging. We characterise and evaluate the optical properties of the dominant chromophores in tissue and tissue-mimicking phantoms, from visible to SWIR wavelengths. Using single-integrating sphere systems, we measured the optical properties of single-component chromophores (H2O, haemoglobin, corn oil, synthetic melanin) and multi-component tissues (whole blood, lard), to decouple contributions from optical scattering, H2O absorption and other contributing chromophores; we also characterised commonly-used phantom materials and investigated their potential to mimic soft tissues in the SWIR range using simulations. We provide a consistent dataset of absorption and reduced scattering coefficients that characterise the dominant tissue chromophores from 450 nm out to 1600 nm. These results were shown to be consistent with literature data, where available. We integrate these data into an open-source Python toolkit, SIMPA, for optical modelling and demonstrate soft tissue simulations that can be probed continuously from visible to SWIR wavelengths. Our findings are compared with tissue-mimicking phantoms, highlighting a need for additives for polymer-based phantoms that mimic SWIR water absorption. By providing this open-source dataset, we aim to enable future studies exploring SWIR light-tissue interactions that facilitate rapid assessment and prototyping of next-generation spectroscopy and imaging techniques.

Spatial frequency domain imaging (SFDI) has demonstrated the ability to provide early, quantitative assessment of burn wound severity. Previous studies using a research-grade SFDI platform showed that high-dimensional datasets incorporating multiple spatial frequencies and wavelengths can predict healing outcomes in controlled porcine models of graded burns. To assess the impact of reduced measurement dimensionality on diagnostic performance, we compared classifications derived from a research-grade SFDI system (Reflect RS) with those obtained using a simplified commercial SFDI platform (Clarifi RS). Both systems were used to image graded burns 24 hours after injury. Pixel-level classifiers were trained using regions defined by 28-day healing outcomes, and models based on the full Reflect dataset were compared with classifiers generated from reduced Reflect feature sets and datasets designed to mimic Clarifi acquisition features. Performance was evaluated using leave-one-subject-out validation. The full Reflect dataset achieved the highest classification performance, with mean F1 scores approaching 0.88. Although reducing the number of measured features decreased classification accuracy, simplified models and Clarifi-based datasets maintained F1 scores greater than 0.8 for binary classification. These findings indicate that dimensionality reduction produces a measurable but manageable loss in performance and support the development of clinically practical, application-specific SFDI systems for burn assessment.

We present a non-invasive approach for continuous monitoring of lactate dynamics in-vivo using near-infrared (NIR) spectroscopy. Lactate-related spectral features were measured non-invasively within the overtone region (1600-1850 nm). Several anatomical measurement sites were evaluated, and the middle phalanx of the dorsal finger emerged as the most promising location due to its superior spectral quality and stable tissue perfusion, becoming the exclusive site for all further experiments. Across multiple exercise sessions, predictive models achieved high within-day accuracy (R2[≥] 0.8), while cross-day performance was affected by spectral drift and physiological variability. A dynamic offset-correction procedure effectively mitigated these baseline shifts, enabling stable prediction accuracy across days, weeks, and subjects. These findings demonstrate the feasibility of NIR-based lactate estimation and highlight the importance of adaptive correction strategies for reliable long-term, non-invasive monitoring.

Continuous wave diffuse correlation spectroscopy (cw-DCS) is a noninvasive optical technique to monitor the tissues blood flow changes. This technique measures the tissue blood flow index (BFI) by evaluating the decay rate of the autocorrelation function. The derived BFI is proportional to mean squared displacements of the red blood cells considered as the fast-dynamic scatterer component of tissue in time. However, biological tissue contains static scatterer component and slow-dynamic scatterer component which affect the decay rate of autocorrelation function and as a result the derived BFI. In this study, we assessed the fractional contribution of static, slow-dynamic and fast-dynamic scatterer components of a medium in the flow index derived by cw-DCS. The measurements performed on Agar-based phantom with tube showed that presence of static scatterer component and slow-dynamic scatterer component led to substantial underestimation ({approx} 123%) of the flow index derived by Siegert relation, compared to effective diffusion coefficient of fast-dynamic scatterers components derived by modified Siegert relation and bi-exponential model. The less underestimation was observed for the corresponding parameters obtained from the liquid phantom measurements ({approx} 25%) as well as during the forearm occlusion test and respiratory challenges ({approx} 16% - 26%).

Hyperspectral imaging (HSI) has gained increasing attention for bone assessment because it captures rich wavelength dependent information associated with mineralised tissue. HSI provides detailed spectral information related to material composition, while 3D geometric information supports the analysis of surface morphology and structural detail. However, integrating spectral and geometric information remains challenging, particularly when conventional reconstruction pipelines depend on external pose estimation. To address this challenge, we propose BoNeRF-HS, a self-calibrated hyperspectral neural radiance field for 3D reconstruction. BoNeRF-HS jointly optimises camera intrinsics, volume density, and hyperspectral radiance, removing the need for COLMAP based poses. To improve spectral modelling, we incorporate a gated spectral adapter head that learns wavelength dependent radiance features for hyperspectral view synthesis. We evaluate BoNeRF-HS on a multi-view hyperspectral dataset containing mouse bone, trabecular bone analogue, and cortical bone analogue samples. Experimental results demonstrate that our framework achieves improved reconstruction quality, and better preservation of bone surface details compared with existing approaches.

Fluorescence imaging in the second near-infrared window (NIR-II, 950-1700 nm) offers reduced scattering, lower autofluorescence, and deeper tissue penetration than NIR-I imaging, but its adoption is limited by the need for custom-synthesized fluorophores. Here, we identify commercially available dyes that exhibit usable NIR-II emission. Eleven visible, far-red, and NIR-I fluorophores were screened under twelve acquisition configurations combining 670, 760, and 808 nm excitation with band-pass (950 nm, 1400 nm) or long-pass (1000 nm, 1250 nm) emission filters. Output varied markedly with fluorophore identity and excitation/emission configuration. Among hydrophobic dyes, DiR exhibited strong emission across almost all excitation and emission filters. Among hydrophilic dyes, strong NIR-II fluorescence was observed for IRDye 680RD (excitation at 670 nm), sulfo-cyanine 7 (excitation at 670 nm and 760 nm), and indocyanine green (excitation at 808 nm). DiR showed a linear concentration-response under 760 nm excitation with BP1400 detection. Upon encapsulation in PEGylated liposomes, strong NIR-II fluorescence was retained. In an in vivo study in mice, NIR-II resolved vasculature that NIR-I could not consistently delineate, and enabled pharmacokinetic analysis. Both windows returned similar ex vivo organ distributions. NIR-II imaging is therefore accessible using commercial off-the-shelf fluorophores, provided the dye is matched to the intended excitation/emission configuration.

Human skin colors occupy a characteristic banana-shaped region in CIE L*a*b* space, but why skin color coordinates are restricted to this region and how they relate to melanin and blood remain incompletely understood. We developed a physics-based framework linking skin chromophore content to colorimeter-derived skin color coordinates using two complementary three-layer light transport models. Across physiologic ranges of epidermal melanosome volume fraction and dermal blood volume fraction, simulated reflectance spectra were converted to CIE L*a*b* coordinates and compared with human skin color measurements from the International Skin Spectra Archive. Physiologic variation in melanin and blood reproduced the observed banana-shaped locus and revealed distinct chromophore-specific trajectories. Iso-melanin trajectories became progressively more linear as melanin increased, whereas iso-blood trajectories retained the curvature of the skin color locus. As melanin increased, perceptible color differences from blood volume changes were reduced, providing a mechanistic explanation for reduced erythema visibility in highly pigmented skin. These relationships were stable across plausible variations in layer thickness and tissue oxygenation and agreed with external validation data. The framework also identified when the Individual Typology Angle is confounded by blood or distorted by dermal melanin. Together, these findings establish a mechanistic optical basis for interpreting colorimeter-derived skin color coordinates.

SignificanceDiffuse Correlation Spectroscopy (DCS) is an established technique for non-invasive monitoring of Cerebral Blood Flow (CBF), but existing applications primarily measure CBF changes averaged over cortical tissue volumes. A method capable of targeting blood flow within a particular intracranial artery would expand the utility of DCS for vessel-specific cerebral perfusion monitoring and continuous bedside assessment. AimWe aim to investigate the feasibility of targeted, non-invasive monitoring of blood flow in the Anterior Cerebral Artery (ACA) using DCS through optimization of probe geometry and placement. ApproachA custom-built DCS system operating at 785 nm was used to probe ACA from the glabellar region. Source-detector (SD) separation, probe orientation, and probe location were systematically optimized using lower-limb motor tasks and a mental arithmetic task. The optimized configuration was evaluated using an ACA-mimicking multilayer phantom and validated in forty healthy volunteers during lower-limb activation tasks and postural changes. ResultsAn SD separation of 17 mm, vertical probe orientation, and glabellar placement provided the highest sensitivity to ACA-related blood flow changes. Phantom experiments demonstrated sensitivity to flow changes in a vessel located 45 mm beneath the scalp and showed that the frontal sinus, cerebrospinal fluid, and the absence of cortical tissue beneath the glabella along the longitudinal fissure together provide the best optical window for probing deep ACA flow. Using the optimized probe configuration, significant increases in relative CBF were observed during standing leg marching (66.4 {+/-} 38.6%) and supine leg crunches (39.4 {+/-} 32.2%) (p < 0.01), with consistent responses during supine-to-stand postural transitions. ConclusionThe proposed DCS approach enables targeted measurement of blood flow within the ACA territory. This technique provides a framework for continuous, non-invasive monitoring of ACA perfusion and has potential applications in cerebrovascular monitoring and stroke care.

Fluorescent voltage-sensitive dyes (VSDs) enable non-invasive, high-throughput optical measurement of membrane potential in living cells, but the analytical reliability of such measurements depends critically on whether the dye and associated imaging conditions perturb the system under study. Here, we systematically characterise the photophysical performance and cell-perturbing effects of FluoVolt, a widely adopted VSD, across cancer cell lines (GIN31 glioblastoma and SK-MEL-30 melanoma) and primary human macrophages. Photobleaching kinetics were strongly cell-type-dependent, with SK-MEL-30 cells exhibiting complete fluorescence loss within 400 seconds under standard widefield conditions. FluoVolt staining combined with laser excitation caused an approximately 2.5-fold increase in cell detachment relative to unstained controls, and dual-wavelength excitation (488 + 405 nm) reduced GIN31 cell viability by approximately 17.5%. Critically, morphological changes, a transition from elongated to amoeboid-like phenotypes, were detected under staining conditions alone, prior to any laser exposure, indicating baseline dye-induced perturbation independent of phototoxicity. Halving dye concentration and loading time significantly attenuated these effects while preserving measurable fluorescence signal. These findings identify FluoVolt staining and excitation as previously uncharacterised sources of systematic measurement artefact and provide practical, actionable guidance for protocol design, control selection, and data interpretation in optical membrane potential imaging.

Alterations of fluorescence properties in retinal pigment epithelium (RPE) cells caused by diseases such as age-related macular degeneration (AMD) highlight the need for detailed analysis of the fluorescent RPE granules at the individual level. Precise segmentation and classification of these granules remain challenging due to their limited visual separability. In this study, we present Classi4RPE, a computational algorithm designed to accurately segment RPE granules and classify them into three categories -- lipofuscin (L), melanolipofuscin (ML), and melanin (M) -- based on fluorescence lifetime imaging data, which provide distinctive contrast. The method is implemented in a custom Python framework and employs seeded watershed segmentation to isolate individual granules. Lipofuscin granules are identified as hyperfluorescent structures with longer lifetimes, while granules with shorter lifetimes are further analyzed based on their spatial lifetime distribution from the center to edge, enabling discrimination of ML from other melanin-rich granules. Our approach achieves high performance, with mean sensitivities of 0.99 for L granules and 0.90 for ML granules, and corresponding specificities of 0.93 and 0.98, respectively, compared to manually annotated ground truth. These results demonstrate the potential of Classi4RPE to surpass human visual limitations and provide a robust tool for quantitative RPE analysis.

Multispectral imaging is a cornerstone of chemical imaging, with bioluminescence, fluorescence, and phosphorescence imaging underpin much of preclinical and analytical chemical measurements. However, conventional implementations are typically proprietary, costly, and resolve spectral content from the reflectance of a broadband source, without control over the excitation spectrum. This architecture cannot isolate excitation-dependent photophysical processes. Spectral discrimination through sequentially resolved narrowband excitation, detected on a single broadband-sensitive camera, instead enables excitation-dependent fluorescence, phosphorescence, and reflectance measurements not available to such broadband approaches. We describe AURORA-MSI, an open-source multispectral platform that inverts this arrangement with fourteen narrowband LEDs spanning 367-940 nm, a 120-element annular illumination ring with eight independently addressable azimuthal sectors, and a thermoelectrically cooled monochrome CMOS camera. Radiometric calibration, spatial uniformity mapping, and camera noise characterization establish the quantitative measurement foundation. In bioluminescence imaging, AURORA-MSI localized sources in a calibrated tissue-mimicking mouse phantom comparably to a commercial Revvity IVIS Spectrum, detected luciferase-expressing HSJD-GBM1-001 glioblastoma cells across a dilution series, with reduced replicate consistency at the lowest densities, and mapped substrate-free fungal-pathway emission in an intact bioluminescent Petunia hybrida, none requiring photon-counting instrumentation. Time-resolved phosphorescence decay imaging of six inorganic phosphors over 33 minutes found tri-exponential models adequate at early times, while distributed-lifetime models were preferred at extended timescales. Multispectral image-texture features extracted across all fourteen excitation bands discriminated ten pharmaceutical and household powders spanning distinct chemical compositions and, in two cases, distinct formulations of the same compound. Generality beyond these regimes was established through dual-excitation fluorescence fingerprinting of fifteen mineral specimens and wavelength-selective plant-tissue imaging exploiting UV and NIR penetration-depth differences. Excitation-side spectral encoding enables photophysical measurements that detection-side systems with uncontrolled broadband illumination cannot isolate, and the cooled-camera architecture supports weak-signal modalities without photon-counting instrumentation.

Chemical imaging is a powerful branch of modern microscopy encumbered by a lack of flexible, high-throughput analysis tools. Bespoke analytical pipelines typically perform ratiometric analysis on two layers in a multispectral image to describe the relative composition of molecules in a sample. This strategy has been implemented across fields, spanning histopathology, cell biology, environmental science, and materials science. The commercialization of chemical imaging microscopes has facilitated the collection of large multispectral datasets, necessitating accessible ways to process them. This paper describes Multispectral Analysis Graphical User Interface (msaGUI), a desktop graphical user interface to analyze individual and batch datasets of multispectral images. Data is loaded as CSV, TSV, or TIFFs and processed through a user-defined sequence of modular image operations that can be flexibly combined, e.g. to reduce spectral crosstalk or background noise. After analysis, data is visualized as exportable images, histograms, and statistics. To yield publication-quality figures, outputted images are fully customizable. Written in Python with open-source libraries, the msaGUI program is packaged into an executable for Windows and Mac for a fully no-code application. Other operating systems are supported via the Python source code. In summary, msaGUI provides a rapid and user-friendly solution for analyzing and visualizing multispectral data.

Designing transcutaneous skeletal muscle oxygenation (SmO2) sensors requires jointly optimizing source--detector geometry and wavelength selection while guaranteeing performance across populations that vary in subcutaneous fat thickness and skin pigmentation. We present a multi-fidelity Bayesian optimization (MFBO) framework that couples Monte Carlo light-transport simulations at two photon-count fidelities to a distributionally robust design objective. An autoregressive Gaussian-process surrogate learns the correlation between inexpensive low-photon-count and accurate high-photon-count simulations, and a cost-aware acquisition function decides both where and at what fidelity to sample. Robustness across the population is enforced with Conditional Value-at-Risk (CVaR) and entropic-risk (ERM) objectives that target worst-case subjects rather than the population average. On a five-layer forearm tissue model with anthropometric variability we find (i) a fidelity regime that is favorable for MFBO where the low-fidelity surrogate is rank-informative (Spearman {rho} = 0.84) but biased, at 100x lower cost; (ii) MFBO attains 23% higher robust sensitivity than a strong high-fidelity single-fidelity baseline at equal budget (p = 0.035), and avoids the optimistic bias that causes low-fidelity-only optimization to collapse when its designs are validated at high fidelity; (iii) CVaR/ERM objectives improve worst-case tail performance by {approx}23% relative to a mean objective without sacrificing average sensitivity; and (iv) discovered designs improve robust tail sensitivity by roughly 3--6x over commercial and heuristic optode layouts, with the largest gains in the high-fat and high-melanin subpopulations. The methodology bridges stochastic light-transport physics with sample-efficient machine-learning optimization and generalizes to cerebral oximetry, photodynamic therapy planning, and wearable physiological monitors.

Purpose: To evaluate whether fluorescence lifetime imaging ophthalmoscopy (FLIO) combined with deep learning can detect metabolic signatures for classification of type 2 diabetes mellitus (T2DM). Design: Cross-sectional analysis of participants included AI-READI dataset (version 3) with FLIO imaging and and hemoglobin A1c (HbA1c) measurement. Subjects: 1,783 participants from the AI-READI dataset (version 3) with HbA1c measurements and FLIO imaging scans (6,912 total): 671 normoglycemic, 726 prediabetic, and 386 diabetic. Methods: Mean fluorescence lifetime maps were generated using a center-of-mass approach and used as inputs to AI models. We trained convolutional neural networks (CNNs), ResNet-18, and XGBoost under three-class (normal, prediabetic, diabetic) and two binary (normal vs. impaired; normal vs. diabetic) classification schemes, using nested 5-fold cross-validation with participant-level grouping. Main Outcome Measures: Macro-averaged accuracy, F1 score, area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value (PPV). Results: Group-averaged lifetime maps demonstrated consistent spatial differences across glycemic groups, with progressively longer lifetimes from normal to diabetic participants. The CNN achieved the best overall performance in the 3-class classification (accuracy 0.41 +/- 0.03, F1 score 0.39 +/- 0.02, AUROC 0.58 +/- 0.02), compared to the random classifier for 3-class classification (AUROC = 0.50; accuracy = F1 = 0.33). ResNet-18 and XGBoost showed similar performance (AUROC 0.53-0.58). Confusion matrices revealed substantial overlap between classes, with frequent misclassification toward the prediabetes group. Binary reformulation (normal vs. diabetic) improved performance substantially, with the CNN resulting in AUROC 0.63 +/- 0.02 and XGBoost 0.67 +/- 0.07. Conclusions: FLIO-derived lifetime maps capture metabolic signals associated with glycemic status but yield modest classification performance with current AI models. These findings highlight both the potential and the challenges of using FLIO for early metabolic screening and monitoring, informing future development of clinically applicable imaging biomarkers.

Significance: Continuous, noninvasive, and depth-resolved monitoring of blood-flow-related tissue dynamics remains an important unmet need. Speckle-contrast optical spectroscopy (SCOS), including interferometric implementations such as iSCOS, provides a scalable optical route to blood-flow sensing, but conventional continuous-wave approaches lack intrinsic depth selectivity. Time-of-flight (TOF) gating offers a way to separate superficial and deeper dynamic contributions in layered tissues, such as skin-muscle or scalp-cortex, by resolving photon path lengths. Aim: We introduce a swept-source, single-channel implementation of interferometric speckle-contrast optical spectroscopy (iSCOS) to obtain TOF-resolved temporal speckle contrast, {kappa}^2, from the measured field autocorrelation g_1, and evaluate its feasibility for depth-resolved blood-flow sensing. Approach: A swept-source iNIRS system operating at 780 nm acquired interferometric signals, which were Fourier-transformed along the optical-frequency axis to recover complex TOF-resolved speckle fields. Temporal speckle contrast was then estimated at each TOF gate indirectly from g_1 using the speckle-visibility relation. Diffusion-based numerical simulations were first used to compare the direct variance-based estimator and the indirect g_1-based estimator under varying reduced scattering coefficient, diffusion coefficient, additive noise level, and bi-layer geometry. Because the simulations showed that the g_1-derived {kappa}^2 estimator was substantially less sensitive to additive noise than the direct estimator, this estimator was used for the main phantom and in vivo analyses, while the direct estimator served as a simulation comparator. The g_1-derived estimator was then applied to liquid and bi-layer phantoms, followed by proof-of-concept in vivo measurements on the human forearm during cuff occlusion and on the forehead during a Sudoku task. Results: TOF-resolved kappa2 curves recovered with the g_1-derived estimator matched DWS theory across scattering coefficients, photon path lengths, and exposure times. The estimator preserved theoretical accuracy for additive noise amplitudes up to 50% of the field amplitude, whereas the direct variance estimator showed substantial noise-induced bias and required correction. Bi-layer simulations and phantom experiments reproduced the predicted direction and onset of TOF-dependent decorrelation-rate trends in layered media. In vivo, the recovered blood-flow index tracked the expected TOF-dependent cuff-occlusion and reactive-hyperemia response in the forearm. During the single-subject Sudoku task, the left-forehead recording showed a TOF-dependent relative blood-flow-index increase of +0.8 {+/-} 1.9% at TOF = 400 ps, +9.8 {+/-} 2.2% at TOF = 600 ps, and +15.2 {+/-} 5.6% at TOF = 800 ps. This pattern is consistent with increased sensitivity to deeper tissue at longer photon path lengths, but requires cohort-level validation before quantitative interpretation as cognitive activation. Conclusions: Coupling temporal speckle-contrast analysis with swept-source iNIRS yields a proof-of-concept, depth-resolved platform for blood-flow sensing. By estimating TOF-resolved speckle contrast through the g_1-derived {kappa}^2 route, TOF-iSCOS suppresses additive-noise bias while preserving sensitivity to deeper dynamic tissue layers. The present single-channel results bridge continuous-wave iSCOS, interferometric NIRS and time-domain diffuse correlation spectroscopy (TD-DCS), and motivate future multi-channel and cohort studies for scalable cortical hemodynamic monitoring.

PurposeTo quantify parasitic losses in ultra-high field (UHF) magnetic resonance imaging (MRI) receive coils and determine how they contribute to the mismatch between numerically predicted and experimentally realized signal-to-noise ratio (SNR), with the goal of guiding receive-array designs toward ultimate intrinsic SNR (uiSNR). MethodsSNR was measured across multiple field strengths (3T, 7T, 10.5T) using commercial and custom-built arrays. To quantify parasitic losses, unloaded-to-loaded quality factor ratio (QR) measurements were performed on representative loop resonators and practical RF coils. Measured losses were combined with single-loop electromagnetic simulations to separate conductor, radiation, and component losses. These bench-derived loss estimates were then incorporated into full-array electromagnetic simulations of a 128-channel receive array to evaluate their impact on predicted intrinsic SNR. ResultsMeasurements across field strengths supported the expected supralinear increase of SNR with B0. QR analysis showed that, at UHF, radiation loss must be excluded from unloaded-Q measurements to avoid overestimating electronic-noise penalties, and that multiple seemingly modest parasitic losses collectively impose substantial SNR degradation. In the 128-channel array, simulations including only conductor and radiation losses predicted 93% of central uiSNR, whereas inclusion of the full measured parasitic-loss budget reduced predicted performance to 78%, in close agreement with the experimentally measured 77%. ConclusionsThe gap between predicted and realized SNR performance of high-channel-count 10.5T loop arrays can be largely explained by parasitic losses that are not captured in conventional simulations. A bench-measurement-informed simulation framework enables more realistic prediction of coil performance and provides practical guidance for optimizing future UHF receive arrays.

Spectroscopic single-molecule localization microscopy (sSMLM) enables multiplexed super-resolution imaging by simultaneously acquiring the spatial position and spectral information of individual fluorophores. Dual-wedge prism (DWP)-based implementations provide a compact, alignment-stable approach to spectral dispersion, but trade-offs between localization precision, spectral precision, and experimental complexity remain. We systematically compare five DWP-based sSMLM configurations, including two-dimensional (2D) and three-dimensional (3D) implementations using single DWP (DWP-sSMLM) and symmetrically-dispersed DWP (SDDWP-sSMLM). We evaluate lateral precision, spectral precision, and ease of use. SDDWP configurations acquire spectral images in both channels and utilize both for spatial localization, yielding the highest lateral and spectral precision. However, for applications that do not require axial information, 2D-DWP provides a simple, plug-and-play solution with robust performance. This work offers a guideline for selecting DWP configurations based on experimental needs.

This study presents the use of sub-micron nanobubbles (NBs) as contrast agents for ultrasound localization microscopy (ULM), a super-resolution imaging technique that visualizes microvascular structure and flow beyond the acoustic diffraction limit. While ULM has traditionally relied on micron-sized microbubbles (MBs), the reduced dimensions and prolonged circulation times of NBs make them attractive candidates for localization-based imaging. However, their weaker acoustic responses present significant challenges for reliable detection and tracking. To address this challenge, we developed the ULM Master GUI, an interactive framework for optimization of the complete ULM processing pipeline. Using custom ultrasound-compatible wall-less gelatin flow phantoms containing vessel-mimicking channels and bifurcations ranging from 100 to 500 m, we demonstrate that NB-based ULM achieves velocity reconstruction and flow partitioning measurements comparable to conventional MB-based ULM. Across all investigated geometries, NBs faithfully reproduced the underlying flow patterns and hemodynamic behavior despite their substantially reduced acoustic scattering. These findings establish the feasibility of NB-based ULM, expand the range of contrast agents available for localization microscopy, and provide a foundation for future super-resolution ultrasound imaging using nanoscale acoustic contrast agents. The ULM processing GUI is publicly available at https://github.com/grisha1998/ulm-super-resolution-toolbox.

Light reaching the retina is a primary regulator of human circadian physiology, acting largely through melanopsin-expressing retinal ganglion cells with peak short-wavelength sensitivity. Delivering known, repeatable retinal doses outside the laboratory is difficult because conventional light sources leave viewing geometry, gaze, and ambient conditions uncontrolled. Consumer extended-reality (XR) glasses fix a bright binocular display in constant geometry relative to the eye, but their suitability as calibrated photic stimulators has not been established. Here we validate a commercial micro-OLED XR display (VITURE Luma Ultra) for controlled retinal photostimulation. A purpose-built host application renders exact 8-bit RGB stimuli while independently controlling hardware brightness and logging all intensity-determining state; spectral radiance was measured at the retinal position of a 3D-printed phantom head with an open-source miniature spectroradiometer, anchored to absolute units by a luminance transfer calibration. The blue primary peaks at 461 nm (FWHM 43 nm), is spectrally invariant across a >10-fold intensity range, and at maximum output delivers an estimated 299 lx melanopic equivalent daylight illuminance, above consensus daytime recommendations, while remaining roughly two orders of magnitude below photobiological safety limits. The red primary is visually effective with minimal melanopic drive (melanopic DER 0.10), enabling spectrally shifted evening stimulation. Unlike the immersive virtual-reality headsets previously used for calibrated light delivery, the see-through form factor preserves the wearer's view of the surroundings--relevant for clinical monitoring in supervised settings such as the intensive care unit. These results show that consumer XR glasses can serve as a dose-calibrated platform for wearable photostimulation using an open-source measurement chain, and provide groundwork for application-layer dose-response studies.

Optical coherence tomography (OCT) is widely used for retinal disease assessment, but automated quantitative analysis remains challenging because of anatomical variability and noisy imaging conditions. This study presents an interpretable OCT classification framework based on four anatomically guided retinal layers, combining preprocessing, adaptive segmentation, targeted feature engineering, and supervised classification to identify Normal, CNV, DME, and Drusen cases. Layer-specific descriptors included statistical, derivative, fluid-related, and GLCM texture markers. Feature correlation and ranking analyses showed that the proposed descriptors were highly complementary, that the most informative features were concentrated in layers 2 and 4, and that layers 1 and 3 contributed supportive structural information. Among the evaluated classifiers, the neural network performed best, achieving an accuracy of 98.17%, sensitivity of 97.88%, specificity of 99.38%, and AUC of 0.9985. Computational analysis showed efficient training and inference, with a total training time of 2216.3 s, prediction speed of approximately 160000 observations per second, and a compact model size of about 11 kB. These results demonstrated that anatomically guided feature extraction can provide accurate, efficient, and interpretable OCT disease classification, offering a practical alternative to less transparent end-to-end deep learning approaches.