.SignificanceThe relationship between spatial offset and tissue sensing depth is not well understood in spatial offset Raman spectroscopy (SORS). Detection of the subsurface biochemical composition could improve clinical translation of SORS-based methods, including for lumpectomy margin characterization in breast cancer surgery.AimWe aimed at developing an experimental method to establish a relationship between spatial offset in SORS and sampling depth. The technique was developed using a custom hyperspectral line-scanning imaging system optimized for Raman spectroscopy detection.ApproachBilayer phantoms were produced with top and bottom layers made of material with different Raman spectroscopy signatures, i.e., poly(dimethylsiloxane) polymer (PDMS) and Nylon. The top layer of PDMS had different values of absorption and reduced elastic scattering coefficients, as well as a thickness up to . A metric was used, called spectral angle mapper, that allowed for comparing SORS measurements with reference spectra of pure PDMS and Nylon. That metric was used to develop a technique predicting sensing depth for different values of spatial offset. A proof-of-concept study was performed to assess the performance of the method in biological tissue, demonstrating detectability of protein-rich tissue across layers of Intralipid and porcine fat to simulate the optical properties of human adipose tissue.ResultsA total of 60 optical phantoms with varying optical properties and top layer thicknesses were imaged and processed to estimate sampling depth as a function of spatial offset. The study demonstrated the detectability of the underlying Nylon layer across a PDMS layer up to 3 mm in thickness. Similarly, the detectability of protein-rich tissue was demonstrated across layers of Intralipid up to 3 mm thick and for porcine fat.ConclusionsWe showed the feasibility of using bilayer solid optical phantoms to create correlation curves between the optimal spatial offset for a desired probed depth given the optical properties of the top layer. The technique could facilitate the clinical translation of SORS measurements for tumor detection and margins assessment.

.SignificanceNear-infrared spectroscopy (NIRS) imaging modalities are used to provide noncontact measurements of tissue oxygenation in diabetic foot ulcers. However, the curved surface of the diabetic foot introduces inaccurate tissue oxygenation measurement. The changes in spatial NIRS optical measurements may result from variations in the underlying physiology or from the curvature of the tissue surface. Therefore, the effect of tissue curvature must be accounted for to ensure the accurate measurement of tissue oxygenation (or hemoglobin parameters) in clinical applications.AimOur aim is to develop and validate mathematical curvature correction models to account for the effects of tissue curvature on diffuse reflectance (DR) in NIRS imaging and assess their effect on the hemoglobin parameters as well.ApproachMonte-Carlo-based light propagation simulations were performed to develop correction models and applied to three-layered curved geometries in MCMatlab. Four curvature correction models based on height and/or angle were developed via Monte Carlo simulation studies. All the correction models were applied to the simulated DR signals obtained from various curved geometries (concave, convex, and wound-mimicking) using Gaussian light sources at 690 and 830 nm. The effect of correction models on DR signals and hemoglobin parameters was determined.ResultsSimulation results showed that a concave curved surface did not require correction, whereas convex and wound-mimicking geometries showed a reduced median error upon using an empirical height/angle correction model. In addition, the correction model also reduced the median error significantly for the oxygen-saturation-based hemoglobin parameter in both the convex and wound-mimicking geometries.ConclusionsThe developed mathematical model effectively corrected tissue curvature effects in NIRS DR signals and hemoglobin parameters for wound-mimicking irregular geometry. Ongoing work focuses on experimental validation of these correction models on curved phantoms, prior to in vivo imaging studies.

.SignificanceAirway wall elastography (AWE) is promising for evaluating upper airway obstructive disorders and airway injuries. Technologies for AWE based on endoscopic optical coherence tomography (OCT) provide micron-scale resolution to capture airway wall deformations during tidal breathing. Combined with an intraluminal pressure probe, these technologies can provide quantitative AWE as part of a routine bronchoscopy exam. However, scan times must be of short duration to mitigate risk.AimOur objective is to reduce the scan time necessary to perform OCT elastography over a 50 mm length of the airway wall to less than 1 min.ApproachWe introduce an innovative, 4D OCT imaging technique that scans in a sawtooth pattern to revisit each axial position of the airway over a diversity of respiratory phases. An anatomical (long-range) OCT system capable of capturing cross-sections of the upper airway was employed in conjunction with an intraluminal pressure catheter. Scanned data are retrospectively sorted into axial bins with high- and low-pressure thresholds used to compute cross-sectional compliance (CC) within each bin across the length of the upper airway.Results4D OCT was tested in simulation, on rigid and deformable samples, and on in vivo pigs undergoing bronchoscopy. A precise CC measurement with a 0.5 mm sampling resolution over a 50 mm scan length in under 42 s was achieved.ConclusionsThe retrospective, respiratory-gated 4D aOCT scanning method is a minimally invasive technique for measuring airway wall CC. The method exhibited high precision in controlled models, effectively detected elastic heterogeneity, and yielded clinically relevant results in in vivo pigs.

.SignificanceFetal oxygenation level is a critical indicator of fetal health throughout pregnancy and labor. Existing clinical devices predominantly employ invasive techniques, posing risks to both the fetus and the mother.AimTo address this concern, we present a noninvasive method for precise fetal oxygenation level monitoring using time-domain near-infrared spectroscopy (TD-NIRS).ApproachOur method leverages the advantages of time-domain information for accurate separation of optical properties from two distinct layers, thereby enabling noninvasive detection of deep tissue oxygenation levels.ResultsWe validate the proposed method via Monte Carlo simulations and develop a fiber-based TD-NIRS prototype system. In a two-layer tissue phantom experiment, the system accurately estimates the deep-layer absorption coefficient. We further demonstrate the ability of our prototype system to detect changes in the tissue oxygenation index (TOI) through in vivo experiments: (1) TOI measurements of the human forearm with a tissue phantom and (2) transabdominal fetal monitoring on a pregnant ewe.ConclusionsThe results suggest a significant potential for the use of TD-NIRS to noninvasively and safely monitor the fetal oxygenation level.

.SignificanceThe spatially resolved spectroscopy (SRS) approach is widely used in continuous wave near-infrared spectroscopy to estimate tissue oxygen saturation in the skeletal muscle and cerebral cortex. The extension of the SRS approach to the time domain (TD) has never been proposed. We hypothesize that the time-domain spatially resolved spectroscopy (TD SRS) approach, relying on simple models and linear fit, avoiding nonlinear model-based analysis approaches, could be able to assess the homogeneity of the scattering of the explored tissue.AimWe aim to explore the potential of the TD SRS approach for estimating from the spatial derivative of the measured signal in a homogeneous and in a two-layer medium and by considering also the effect of the instrument response function (IRF).ApproachA theoretical expression for depending on the spatial derivative of the attenuation is derived. Then, numerical simulations are conducted using solutions of the radiative transfer equation under the diffusion approximation. We consider a reflectance geometry with source–detector distance in the range 1 to 5 cm in 0.5 cm step, either in a homogenous semi-infinite or two-layer diffusive medium. Convolution with a real IRF is also carried out to mimic experimental scenarios.ResultsIn a homogeneous medium, the TD SRS approach is able to retrieve over a large range of values, being minimally affected by the IRF. In a two-layer medium, the TD SRS approach can only provide information on the changes of with depth but fails to provide a robust estimate of the absolute value of in either of the two layers. Moreover, the IRF can greatly affect the results in the case of the two-layer medium.ConclusionsThe TD SRS approach can be a simple way to estimate spatial changes of but not the absolute value of . Care should be taken to use a TD system with proper IRF.

.SignificanceThree-dimensional (3D) two-photon patterned illumination using a combination of computer-generated holography (CGH) and wide-field temporal focusing (TF) has emerged as a highly effective approach for photostimulation. However, even though the axial full-width at half-maximum of a single-spot by TF is smaller than the single-cell size of , the axial resolution of 3D multispot patterns produced by CGH with TF is lower than the single-cell resolution as a result of interference among multispots.AimWe aim to achieve 3D two-photon patterned illumination with single-cell resolution by combining CGH with time-multiplexed multiline temporal focusing (TM-ML-TF), which is implemented by adding an echelle grating at a position conjugate to the focal plane of the TF-CGH system.ApproachWe measure the 3D two-photon fluorescence distributions generated by the TF-CGH and TM-ML-TF-CGH systems.ResultsIn TM-ML-TF, the crosstalk artifacts between the target spots in two proximal planes with an axial distance of were suppressed from 81% to 15% compared with those in TF. We successfully achieved the photoconversion of 3D target cells in spheroids with single-cell resolution.ConclusionsTM-ML-TF successfully suppresses the interference among multispots, enabling the TM-ML-TF-CGH system to provide precise 3D patterned illumination with single-cell resolution.

.SignificanceDiffuse optical tomography (DOT) is a noninvasive functional imaging technique; however, the reconstruction of high-quality images from DOT data is a challenging task because of the ill-posed nature of the inverse problem. We introduce a hybrid machine learning model that combines the strengths of autoencoders (AEs) and generative adversarial networks (GANs) for robust DOT reconstruction.AimWe leveraged a hybrid machine learning model for robust ultrasound-guided DOT reconstruction.ApproachA hybrid model, DOT-AE-GAN, that combines the strengths of AEs and GANs to enhance the robustness of DOT reconstruction is introduced. The proposed model utilizes an AE to efficiently encode the DOT measurement to reconstruction and decode back to measurement, modeling the inverse and forward process of reconstruction. In parallel, a GAN framework is incorporated to enhance the robustness of the reconstruction for irregularly shaped lesions, utilizing adversarial training.ResultsThe DOT-AE-GAN model is first trained and validated using simulations, demonstrating reconstruction accuracy in absorption coefficients and lateral dimensions of the targets. The DOT-AE-GAN is then fine-tuned with phantom data and compared with the AE model, showing the improvement over the AE model in the reconstructed target lateral dimension while keeping similar accuracy in absorption coefficient. The DOT-AE-GAN is validated with patient data, revealing that the DOT-AE-GAN-reconstructed breast lesion lateral dimensions follow size measurements of co-registered ultrasound significantly better than the optimization-based reconstruction algorithm and AE model with improved absorption contrast between malignant and benign lesions.ConclusionsOur results demonstrate that the DOT-AE-GAN model has great potential in ultrasound-guided DOT reconstruction.

Determining the depth of injury in burn wounds is critical to inform surgical decision-making and enhance outcomes. Clinical assessment yields poor accuracy in the early post-burn period, and histologic analysis of biopsies (the gold standard) is time-consuming and clinically unfeasible. Indocyanine green angiography (ICGA) has provided very promising results; however, the evidence is still limited, and the details on instrumentation, measurement setup, and data processing/analysis (when reported) are considerably heterogeneous. A processing and analysis pipeline was developed to interpret ICGA data from experimental burn studies in a way that provides objective, generalizable, and reproducible interpretation. Different burns were created on the dorsal aspect of adult pigs, and ICGA was performed. ICGA measurements were then compared with different processing steps. Features were extracted from the indocyanine green angiography (ICG) kinetics curves at specific regions of interests and ran individual and group analyses to decide on the wound severity. To this end, the features were analyzed both separately and groupwise. The repeatability of the study was enhanced by processing steps where ICG curves were normalized by their area under the curve (AUC). Peak value ( ), residual AUC (rAUC), mean transit time (MTT), full width at half maximum (FWHM), and ingress ( ) and egress ( ) slopes presented the strongest correlation with burn severity. MTT and FWHM were almost independent of the processing steps included in the pipeline, providing high reliability between imaging sessions and inter-subject comparisons. Superficial burns presented significantly higher , rAUC, , and , as well as lower FWHM, when compared with the ICG kinetics from normal tissue, whereas the contrary happens for deep burns. We highlight the utility of a pre-processing step and judicious choice of parameters to use when interpreting ICGA data from indeterminate depth burn wounds to maximize the accuracy in severity estimation.

We provide the first direct evidence of the urothelial response to water transport through the urothelium, traditionally considered impermeable. Using optical coherence tomography (OCT), we observe that the urothelium absorbs and expels water under varying concentrations of NaCl, challenging long-held views about its impermeability. The discovery that osmotic stress can induce urothelial damage has implications for bladder disorders such as interstitial cystitis and overactive bladder, where urothelial integrity is compromised. Traditionally considered impermeable, the urothelium has recently been implicated in water transport due to the presence of aquaporins. Despite this, direct evidence of the urothelial response to water movement through the urothelium remains elusive. We aim to provide such evidence by examining urothelial responses to NaCl solutions using OCT. Fresh porcine bladder samples were subjected to OCT imaging to observe urothelial responses under varying osmolarity conditions, using NaCl solutions ranging from 0.31 to . Urothelial optical pathlength thickness was measured pre-NaCl and post-NaCl application. In addition, histological and scanning electron microscopy (SEM) analyses were conducted to assess cellular integrity and damage. OCT imaging revealed a significant increase in urothelial optical pathlength thickness following deionized water application, indicative of water absorption. Conversely, exposure to higher osmolarity NaCl solutions resulted in urothelial shrinkage, suggesting water efflux. Histological analysis demonstrated intact cellular structures at lower osmolarities ( ) but significant cellular disruption at higher concentrations ( ). SEM analysis corroborated these findings, showing progressive damage to umbrella cells with increasing osmolarity. We provide evidence that the urothelium is a dynamic barrier capable of water transport, influenced by osmotic gradients. The observed osmotic-induced urothelial damage may have important implications for the pathophysiology of conditions such as interstitial cystitis and overactive bladder, offering insights into potential diagnostic and therapeutic strategies. These findings warrant further investigation using human tissue.

Optoacoustic tomography systems commonly employ bulky and expensive solid-state laser sources readily capable of generating dozens of millijoules of optical energy per pulse. Light-emitting diodes (LEDs) may offer a significantly more affordable and compact solution with excellent pulse-to-pulse stability. Yet, the optical design must optimize the energy density delivered to the target, given the relatively low per-pulse energy output of LEDs. We exploit a full-view LED-based optoacoustic tomography (FLOAT) configuration for in vivo imaging of mice. The system features panoramic light illumination delivering 0.48 mJ of total per-pulse energy with an array of 160 LEDs stacked into a cylindrically focused circular ultrasound array transducer. We characterize the imaging performance of the FLOAT system in tissue-mimicking phantoms, subsequently demonstrating its ability for in vivo cross-sectional mouse imaging. It is anticipated that the compact, low-cost FLOAT imaging system will open up new venues in resource-limited settings for studying large-scale biodynamics such as pharmacokinetics and biodistribution of molecular agents and drugs on a whole-body level.