Search

Significance: Selective plane illumination microscopy (SPIM) is an emerging fluorescent imaging technique suitable for noninvasive volumetric imaging of C. elegans. These promising microscopy systems, however, are scarce in academic and research institutions due to their high cost and technical complexities. Simple and low-cost solutions that enable conversion of commonplace wide-field microscopes to rapid SPIM platforms promote widespread adoption of SPIM by biologist for studying neuronal expressions of C. elegans. Aim: We sought to develop a simple and low-cost optofluidic add-on device that enables rapid and immobilization-free volumetric SPIM imaging of C. elegans with conventional fluorescent microscopes. Approach: A polydimethylsiloxane (PDMS)-based device with integrated optical and fluidic elements was developed as a low-cost and miniaturized SPIM add-on for the conventional wide-field microscope. The developed optofluidic chip contained an integrated PDMS cylindrical lens for on-chip generation of the light-sheet across a microchannel. Cross-sectional SPIM images of C. elegans were continuously acquired by the native objective of microscope as worms flowed in an L-shape microchannel and through the light sheet. Results: On-chip SPIM imaging of C. elegans strains demonstrated possibility of visualizing the entire neuronal system in few seconds at single-neuron resolution, with high contrast and without worm immobilization. Volumetric visualization of neuronal system from the acquired cross-sectional two-dimensional images is also demonstrated, enabling the standard microscope to acquire three-dimensional fluorescent images of C. elegans. The full-width at half-maximum width of the point spread function was measured as 1.1 and 2.4 μm in the lateral and axial directions, respectively. Conclusion: The developed low-cost optofluidic device is capable of continuous SPIM imaging of C. elegans model organism with a conventional fluorescent microscope, at high speed, and with single neuron resolution.

Significance: Quantitative stiffness information can be a powerful aid for tumor or fibrosis diagnosis. Currently, very promising elastography approaches developed for non-contact biomedical imaging are based on transient shear-waves imaging. Transient elastography offers quantitative stiffness information by tracking the propagation of a wave front. The most common method used to compute stiffness from the acquired propagation movie is based on shear-wave time-of-flight calculations. Aim: We introduce an approach to transient shear-wave elastography with spatially coherent sources, able to yield full-field quantitative stiffness maps with reduced artifacts compared to typical artifacts observed in time-of-flight. Approach: A noise-correlation algorithm developed for passive elastography is adapted to spatially coherent narrow or any band sources. This noise-correlation-inspired (NCi) method is employed in parallel with a classic time-of-flight approach. Testing is done on simulation images, experimental validation is conducted with a digital holography setup on controlled homogeneous samples, and full-field quantitative stiffness maps are presented for heterogeneous samples and ex-vivo biological tissues. Results: The NCi approach is first validated on simulations images. Stiffness images processed by the NCi approach on simulated inclusions display significantly less artifacts than with a time-of-flight reconstruction. The adaptability of the NCi algorithm to narrow or any band shear-wave sources was tested successfully. Experimental testing on homogeneous samples demonstrates similar values for both the time-of-flight and the NCi approach. Soft inclusions in agarose sample could be resolved using the NCi method and feasibility on ex-vivo biological tissues is presented. Conclusions: The presented NCi approach was successful in computing quantitative full-field stiffness maps with narrow and broadband source signals on simulation and experimental images from a digital holography setup. Results in heterogeneous media show that the NCi approach could provide stiffness maps with less artifacts than with time-of-flight, demonstrating that a NCi algorithm is a promising approach for shear-wave transient elastography with spatially coherent sources.

Significance: Spectral-domain optical coherence tomography (SD-OCT) offers depth-resolved imaging of optical scattering contrast but is limited in sensitivity to optical absorption. Dual-modality imaging combined with the noncontact absorption contrast of photoacoustic remote sensing (PARS) microscopy can augment SD-OCT applications with specific molecular and functional contrasts in an all-optical, fiber-based platform. Aim: To develop a fiber-based multimodal PARS and SD-OCT imaging system, which efficiently uses a common 1050-nm light source for SD-OCT and PARS interrogation. Approach: PARS microscopy has predominantly utilized a 1310-nm interrogation light source to date. Hence, a recent dual-modality PARS and 1050-nm SD-OCT imaging system required three distinct wavelengths including a 532-nm PARS excitation, necessitating a free-space optical architecture with discrete subsystems. Here, we validate the first use of a 1050-nm interrogation wavelength for PARS. This enables the transition to fiber-based interferometry as is standard in modern SD-OCT systems, though infeasible with inclusion of an additional 1310-nm wavelength. PARS interrogation functionality is integrated using a broadband optical circulator. Results: Dual-modality imaging is demonstrated in carbon fiber phantoms and a mouse ear in vivo. SD-OCT provided a 4.5-μm lateral resolution, 8.8-μm axial resolution in air, and >101 dB of sensitivity, and PARS contributed 532-nm optical absorption contrast with a 47-dB SNR, and lateral and axial resolutions of 2.4 and 35 μm, respectively. Total interrogation power was reduced from 90% to 58% of the ANSI limit compared to a previous three-wavelength approach. Conclusions: Adapting PARS to use the 1050-nm SD-OCT light source for interrogation enabled implementation of a fiber-based dual-modality system configuration, with image quality maintained. This will facilitate development of potential applications demanding handheld, catheter-based, or endoscopic form factors.

Significance: Longitudinal tracking of hemodynamic changes in the breast has shown potential for neoadjuvant chemotherapy (NAC) outcome prediction. Spatial frequency domain imaging (SFDI) could be suitable for frequent monitoring of shallow breast tumors, but strong sensitivity to superficial absorbers presents a challenge. Aim: We investigated the efficacy of a two-layer SFDI inverse model that accounts for varying melanin concentration in the skin to improve discrimination of optical properties of deep tissue of the breast. Approach: Hemodynamic changes in response to localized breast compression were measured in 13 healthy volunteers using a handheld SFDI device. Epidermis optical thickness was determined based on spectral fitting of the model output and used to calculate subcutaneous optical properties. Results: Optical properties from a homogeneous model yielded physiologically unreasonable absorption and scattering coefficients for highly pigmented volunteers. The two-layer model compensated for the effect of melanin and yielded properties in the expected range for healthy breast. Extracted epidermal optical thickness was higher for higher Fitzpatrick types. Compression induced a decrease in total hemoglobin consistent with tissue blanching. Conclusions: The handheld SFDI device and two-layer model show potential for imaging hemodynamic responses that potentially could help predict efficacy of NAC in patients of varying skin tones.

Significance: Despite its advantages in terms of safety, low cost, and portability, functional near-infrared spectroscopy applications can be challenging due to substantial signal contamination from hemodynamics in the extracerebral layer (ECL). Time-resolved near-infrared spectroscopy (tr NIRS) can improve sensitivity to brain activity but contamination from the ECL remains an issue. This study demonstrates how brain signal isolation can be further improved by applying regression analysis to tr data acquired at a single source–detector distance. Aim: To investigate if regression analysis can be applied to single-channel trNIRS data to further isolate the brain and reduce signal contamination from the ECL. Approach: Appropriate regressors for trNIRS were selected based on simulations, and performance was evaluated by applying the regression technique to oxygenation responses recording during hypercapnia and functional activation. Results: Compared to current methods of enhancing depth sensitivity for trNIRS (i.e., higher statistical moments and late gates), incorporating regression analysis using a signal sensitive to the ECL significantly improved the extraction of cerebral oxygenation signals. In addition, this study demonstrated that regression could be applied to trNIRS data from a single detector using the early arriving photons to capture hemodynamic changes in the ECL. Conclusion: Applying regression analysis to trNIRS metrics with different depth sensitivities improves the characterization of cerebral oxygenation signals.

Significance: Mid-infrared (MIR) light refers to wavelengths ranging from 3 to 30 μm and is the most attractive spectral region for ablation of soft and hard tissues. This is because building blocks of biological tissue, such as water, proteins, and lipids, exhibit molecular vibrational modes in the MIR wavelengths that result in strong MIR light absorption. To date, researchers investigating MIR lasers for surgical applications have used bulky light sources, such as free electron lasers, nonlinear light generators, and carbon dioxide lasers. We demonstrate the use of a tiny (a few microns wide, a few millimeters long) MIR interband cascade laser (ICL) for surgical thermal ablation applications. Aim: Our goal is to demonstrate the use of an ICL for surgical thermal ablation and demonstrate its efficacy in ablating normal fibroblasts and primary undifferentiated pleomorphic sarcoma tumor cells (C1619). Approach: We conducted Fourier transform infrared spectroscopy analysis of healthy and cancerous tissue samples, which indicated that the absorption of tumor tissue is higher than healthy tissue around 3.3-μm wavelength. These results enabled us to select an ICL emission wavelength, λ, of 3.3 μm to probe normal fibroblast and primary undifferentiated pleomorphic sarcoma cell survival after ICL exposure. Results: We show that the absorption of tumorous tissue is higher than that of healthy tissues around the 3-μm MIR wavelength. We demonstrate that the ICL is able to ablate cancer cells at very low-power levels that can be clinically implemented but that this effect does not appear to be specific to C1619 when compared to normal fibroblasts. Conclusions: Our study demonstrates that ICLs may represent an exciting new avenue toward precise laser-based thermal ablation.

Significance: Optical fluorescence spectroscopy technique has been explored extensively to quantify both glucose uptake and mitochondrial metabolism with proper fluorescent probes in small tumor models in vivo. However, it remains a great challenge to rapidly quantify the intrinsic metabolic fluorophores from the optically measured fluorescence spectra that contain significant distortions due to tissue absorption and scattering. Aim: To enable rapid spectral data processing and quantify the in vivo metabolic parameters in real-time, we present an empirical ratio-metric method for rapid fluorescence spectra attenuation correction with high accuracy. Approach: A first-order approximation of intrinsic fluorescence spectra can be obtained by dividing the fluorescence spectra by diffuse reflectance spectra with some variable powers. We further developed this approximation for rapid extraction of intrinsic key metabolic probes (2-NBDG for glucose uptake and TMRE for mitochondrial function) by dividing the distorted fluorescence spectra by diffuse reflectance intensities recorded at excitation and emission peak with a pair of system-dependent powers. Tissue-mimicking phantom studies were conducted to evaluate the method. Results: The tissue-mimicking phantom studies demonstrated that our empirical method could quantify the key intrinsic metabolic probes in near real-time with an average percent error of ∼5%. Conclusions: An empirical method was demonstrated for rapid quantification of key metabolic probes from fluorescence spectra measured on a tissue-mimicking turbid medium. The proposed method will potentially facilitate real-time monitoring of key metabolic parameters of tumor models in vivo using optical spectroscopy, which will significantly advance translational cancer research.

Significance: Digital holographic microscopy is widely used to get the quantitative phase information of transparent cells. Aim: However, the sample phase is superimposed with aberrations. To quantify the phase information, aberrations need to be fully compensated. Approach: We propose a technique to obtain aberration-free phase imaging, using the derivative-based principal component analysis (dPCA). Results: With dPCA, almost all aberrations can be extracted and compensated without requirements on background segmentation, making it efficient and convenient. Conclusions: It solves the problem that the conventional principal component analysis (PCA) algorithm cannot compensate the common but intricate higher order cross-term aberrations, such as astigmatism and coma. Moreover, the dPCA strategy proposed here is not only suitable for aberration compensation but also applicable for other cases where there exist cross-terms that cannot be analyzed with the PCA algorithm.

Significance: Intracranial pressure (ICP), variability in perfusion, and resulting ischemia are leading causes of secondary brain injury in patients treated in the neurointensive care unit. Continuous, accurate monitoring of cerebral blood flow (CBF) and ICP guide intervention and ultimately reduce morbidity and mortality. Currently, only invasive tools are used to monitor patients at high risk for intracranial hypertension. Aim: Diffuse correlation spectroscopy (DCS), a noninvasive near-infrared optical technique, is emerging as a possible method for continuous monitoring of CBF and critical closing pressure (CrCP or zero-flow pressure), a parameter directly related to ICP. Approach: We optimized DCS hardware and algorithms for the quantification of CrCP. Toward its clinical translation, we validated the DCS estimates of cerebral blood flow index (CBFi) and CrCP in ischemic stroke patients with respect to simultaneously acquired transcranial Doppler ultrasound (TCD) cerebral blood flow velocity (CBFV) and CrCP. Results: We found CrCP derived from DCS and TCD were highly linearly correlated (ipsilateral R2 = 0.77, p = 9 × 10 − 7; contralateral R2 = 0.83, p = 7 × 10 − 8). We found weaker correlations between CBFi and CBFV (ipsilateral R2 = 0.25, p = 0.03; contralateral R2 = 0.48, p = 1 × 10 − 3) probably due to the different vasculature measured. Conclusion: Our results suggest DCS is a valid alternative to TCD for continuous monitoring of CrCP.

Significance: We demonstrate the potential of probing the sO2 change under blood flow in vivo using photoacoustic (PA) imaging and sheds light on the complex relationship between RBC aggregation and oxygen delivery. Aim: To conduct in vivo assessments of the sO2 in the radial artery of healthy volunteers and simultaneously probe the relation between the sO2 and hemodynamic behavior such as red blood cell (RBC) aggregation. Approach: The effects of PA-based measurements of blood hemodynamics were studied as a function of the subjects’ age (20s, 30s, and 40s). The pulsatile blood flow in the human radial artery of 12 healthy subjects was imaged in the 700 to 900 nm optical wavelength range using a linear array-based PA system. Results: The PA power when blood velocity is minimum (Pamax) was larger than the one attained at maximum blood velocity (Pamin), consistent with predictions based on the cyclical variation of RBC aggregation during pulsatile flow. The difference between Pamin and Pamax at 800 nm (ΔPa800) increased with age (1.7, 2.2, and 2.6 dB for age group of 20s, 30s, and 40s, respectively). The sO2 computed from Pamax was larger than the one from Pamin. Conclusions: The ΔPa800 increased with participant age. The ΔPa800 metric could be a surrogate of noninvasively monitoring the age-induced changes in RBC aggregation. The sO2 change during a cycle of pulsatile blood flow also increased with age, demonstrating that RBC aggregation can affect the sO2 change.

Significance: Surgical simulators, both virtual and physical, are increasingly used as training tools for teaching and assessing surgical technical skills. However, the metrics used for assessment in these simulation environments are often subjective and inconsistent. Aim: We propose functional activation metrics, derived from brain imaging measurements, to objectively assess the correspondence between brain activation with surgical motor skills for subjects with varying degrees of surgical skill. Approach: Cortical activation based on changes in the oxygenated hemoglobin (HbO) of 36 subjects was measured using functional near-infrared spectroscopy at the prefrontal cortex (PFC), primary motor cortex, and supplementary motor area (SMA) due to their association with motor skill learning. Inter-regional functional connectivity metrics, namely, wavelet coherence (WCO) and wavelet phase coherence were derived from HbO changes to correlate brain activity to surgical motor skill levels objectively. Results: One-way multivariate analysis of variance found a statistically significant difference in the inter-regional WCO metrics for physical simulator based on Wilk’s Λ for expert versus novice, F ( 10,1 ) = 7495.5, p

Significance: Physiological parameters extracted from diffuse reflectance spectroscopy (DRS) provide clinicians quantitative information about tissue that helps aid in diagnosis. There is a great need for an accurate and cost-effective method for extracting parameters from DRS measurements. Aim: The aim is to explore the accuracy and speed of physiological parameter extraction using machine learning models compared to that of the widely used Monte Carlo lookup table (MCLUT) inverse model. Approach: Diffuse reflectance spectra were simulated using a light transport model based on Monte Carlo simulations and weighted to six wavelengths. Deep learning (DL), random forest (RF), gradient boosting machine (GBM), and generalized linear model (GLM) machine learning models were built using a training set of 10,000 spectra from the simulated data. The MCLUT and machine learning models were used to predict physiological parameters from a separate test set of 30,000 simulated spectra. Mean absolute errors were calculated to evaluate the accuracy and compare it among MCLUT and machine learning models. In addition, the computational time to predict parameters from the test set was recorded to compare the speed among MCLUT and machine learning models. Results: The DL, RF, GBM, and GLM models all had significantly lower errors than the MCLUT inverse method for six wavelengths. The DL model proved to have the lowest errors, with all absolute percent errors under 10%. The DL model had much faster runtimes than the MCLUT. Conclusions: Machine learning is promising for extracting physiological parameters from six-wavelength DRS data, with both lower errors and a faster runtime than the widely used MCLUT model.

Significance: Infrared (IR) inhibition can selectively block peripheral sensory nerve fibers, a potential treatment for autonomic-dysfunction-related diseases (e.g., neuropathic pain and interstitial cystitis). Lowering the IR inhibition threshold can increase its translational potentials. Aim: Infrared induces inhibition by enhancing potassium channel activation. We hypothesized that the IR dose threshold could be reduced by combining it with isotonic ion replacement. Approach: We tested the IR inhibition threshold on the pleural-abdominal connective of Aplysia californica. Using a customized chamber system, the IR inhibition was applied either in normal saline or in isotonic ion-replaced saline, which could be high glucose saline, high choline saline, or high glucose/high choline saline. Each modified saline was at a subthreshold concentration for inhibiting neural conduction. Results: We showed that isotonically replacing ions in saline with glucose and/or choline can reduce the IR threshold and temperature threshold of neural inhibition. Furthermore, the size selectivity of IR inhibition was preserved when combined with high glucose/high choline saline. Conclusions: The present work of IR inhibition combined with isotonic ion replacement will guide further development of a more effective size-selective IR inhibition modality for future research and translational applications.

Significance: Non-contact, camera-based heart rate variability estimation is desirable in numerous applications, including medical, automotive, and entertainment. Unfortunately, camera-based HRV accuracy and reliability suffer due to two challenges: (a) darker skin tones result in lower SNR and (b) relative motion induces measurement artifacts. Aim: We propose an algorithm HRVCam that provides sufficient robustness to low SNR and motion-induced artifacts commonly present in imaging photoplethysmography (iPPG) signals. Approach: HRVCam computes camera-based HRV from the instantaneous frequency of the iPPG signal. HRVCam uses automatic adaptive bandwidth filtering along with discrete energy separation to estimate the instantaneous frequency. The parameters of HRVCam use the observed characteristics of HRV and iPPG signals. Results: We capture a new dataset containing 16 participants with diverse skin tones. We demonstrate that HRVCam reduces the error in camera-based HRV metrics significantly (more than 50% reduction) for videos with dark skin and face motion. Conclusion: HRVCam can be used on top of iPPG estimation algorithms to provide robust HRV measurements making camera-based HRV practical.

Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.

Significance: Lupus nephritis (LuN) is a chronic inflammatory kidney disease. The cellular mechanisms by which LuN progresses to kidney failure are poorly characterized. Automated instance segmentation of immune cells in immunofluorescence images of LuN can probe these cellular interactions. Aim: Our specific goal is to quantify how sample fixation and staining panel design impact automated instance segmentation and characterization of immune cells. Approach: Convolutional neural networks (CNNs) were trained to segment immune cells in fluorescence confocal images of LuN biopsies. Three datasets were used to probe the effects of fixation methods on cell features and the effects of one-marker versus two-marker per cell staining panels on CNN performance. Results: Networks trained for multi-class instance segmentation on fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) samples stained with a two-marker panel had sensitivities of 0.87 and 0.91 and specificities of 0.82 and 0.88, respectively. Training on samples with a one-marker panel reduced sensitivity (0.72). Cell size and intercellular distances were significantly smaller in FFPE samples compared to fresh frozen (Kolmogorov–Smirnov, p≪0.0001). Conclusions: Fixation method significantly reduces cell size and intercellular distances in LuN biopsies. The use of two markers to identify cell subsets showed improved CNN sensitivity relative to using a single marker.

Significance: Line scanning-based temporal focusing multiphoton microscopy (TFMPM) has superior axial excitation confinement (AEC) compared to conventional widefield TFMPM, but the frame rate is limited due to the limitation of the single line-to-line scanning mechanism. The development of the multiline scanning-based TFMPM requires only eight multiline patterns for full-field uniform multiphoton excitation and it still maintains superior AEC. Aim: The optimized parallel multiline scanning TFMPM is developed, and the performance is verified with theoretical simulation. The system provides a sharp AEC equivalent to the line scanning-based TFMPM, but fewer scans are required. Approach: A digital micromirror device is integrated in the TFMPM system and generates the multiline pattern for excitation. Based on the result of single-line pattern with sharp AEC, we can further model the multiline pattern to find the best structure that has the highest duty cycle together with the best AEC performance. Results: The AEC is experimentally improved to 1.7 μm from the 3.5 μm of conventional TFMPM. The adopted multiline pattern is akin to a pulse-width-modulation pattern with a spatial period of four times the diffraction-limited line width. In other words, ideally only four π/2 spatial phase-shift scans are required to form a full two-dimensional image with superior AEC instead of image-size-dependent line-to-line scanning. Conclusions: We have demonstrated the developed parallel multiline scanning-based TFMPM has the multiline pattern for sharp AEC and the least scans required for full-field uniform excitation. In the experimental results, the temporal focusing-based multiphoton images of disordered biotissue of mouse skin with improved axial resolution due to the near-theoretical limit AEC are shown to clearly reduce background scattering.

Significance: There is an essential need to develop wearable multimodality technologies that can continuously measure both blood flow and oxygenation in deep tissues to investigate and manage various vascular/cellular diseases. Aim: To develop a wearable dual-wavelength diffuse speckle contrast flow oximetry (DSCFO) for simultaneous measurements of blood flow and oxygenation variations in deep tissues. Approach: A wearable fiber-free DSCFO probe was fabricated using 3D printing to confine two small near-infrared laser diodes and a tiny CMOS camera in positions for DSCFO measurements. The spatial diffuse speckle contrast and light intensity measurements at the two different wavelengths enable quantification of tissue blood flow and oxygenation, respectively. The DSCFO was first calibrated using tissue phantoms and then tested in adult forearms during artery cuff occlusion. Results: Phantom tests determined the largest effective source–detector distance (15 mm) and optimal camera exposure time (10 ms) and verified the accuracy of DSCFO in measuring absorption coefficient variations. The DSCFO detected substantial changes in forearm blood flow and oxygenation resulting from the artery occlusion, which meet physiological expectations and are consistent with previous study results. Conclusions: The wearable DSCFO may be used for continuous and simultaneous monitoring of blood flow and oxygenation variations in freely behaving subjects.

Significance: Water is a primary absorber of infrared (IR) laser energy, and urinary stones are immersed in fluid in the urinary tract and irrigated with saline during IR laser lithotripsy. Laser-induced vapor bubbles, formed during lithotripsy, contribute to the stone ablation mechanism and stone retropulsion effects. Aim: Introduction of a surfactant may enable manipulation of vapor bubble dimensions and duration, potentially for more efficient laser lithotripsy. Approach: A surfactant with concentrations of 0%, 5%, and 10% was tested. A single pulse from a thulium fiber laser with wavelength of 1940 nm was delivered to the surfactant through a 200-μm-core optical fiber, using a wide range of laser parameters, including energies of 0.05 to 0.5 J and pulse durations of 250 to 2500 μs. Results: Bubble length, width, and duration with surfactant increased on average by 29%, 17%, and 120%, compared with water only. Conclusions: Our study demonstrated successful manipulation of laser-induced vapor bubble dimensions and duration using a biocompatible and commercially available surfactant. With further study, use of a surfactant may potentially improve the “popcorn” technique of laser lithotripsy within the confined space of the kidney, enable non-contact laser lithotripsy at longer working distances, and provide more efficient laser lithotripsy.

Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index (CBFi) estimates. Aim: We explore the effectiveness of MC DCS models in recovering accurate CBFi changes in the presence of strong systemic physiology variations during a hypercapnia maneuver. Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate CBFi timecourses. The results of the MC CBFi values from a set of human subject hypercapnia sessions were compared with CBFi values estimated using a semi-infinite analytical model, as commonly used in the field. Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate CBFi changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses. Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.

Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies. Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS. Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection. Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS. Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.

Significance: Cellular layering is a hallmark of the mammalian neocortex with layer and cell type-specific connections within the cortical mantle and subcortical connections. A key challenge in studying circuit function within the neocortex is to understand the spatial and temporal patterns of information flow between different columns and layers. Aim: We aimed to investigate the three-dimensional (3D) layer- and area-specific interactions in mouse cortex in vivo. Approach: We applied a new promising neuroimaging method—fluorescence laminar optical tomography in combination with voltage-sensitive dye imaging (VSDi). VSDi is a powerful technique for interrogating membrane potential dynamics in assemblies of cortical neurons, but it is traditionally used for two-dimensional (2D) imaging. Our mesoscopic technique allows visualization of neuronal activity in a 3D manner with high temporal resolution. Results: We first demonstrated the depth-resolved capability of 3D mesoscopic imaging technology in Thy1-ChR2-YFP transgenic mice. Next, we recorded the long-range functional projections between sensory cortex (S1) and motor cortex (M1) in mice, in vivo, following single whisker deflection. Conclusions: The results show that mesoscopic imaging technique has the potential to investigate the layer-specific neural connectivity in the mouse cortex in vivo. Combination of mesoscopic imaging technique with optogenetic control strategy is a promising platform for determining depth-resolved interactions between cortical circuit elements.

Significance: One key pathological characteristic of seronegative spondyloarthropathy (SpA) is inflammation at the insertion of tendons and ligaments into the bone (enthesitis). Aim: We explore the potential of the emerging photoacoustic (PA) imaging in diagnosis of SpA and review its feasibility in detecting SpA-associated Achilles tendon enthesitis. Approach: A light-emitting diode (LED)-based PA and ultrasound combined system was employed. The PA images, both along the long and the short axes of each Achilles tendon insertion region, were acquired at 850-nm wavelength, which is sensitive in depicting increased blood volume (i.e., hyperemia). To assess the hyperemia indicating enthesis inflammation, two parameters were quantified in the imaged tendons, including the average intensity and the density of the color pixels in the pseudo-color PA images. Ten SpA patients, all of which met Assessment of SpA International Society (ASAS) criteria for SpA and were found to have Achilles enthesitis by clinical exam according to a board-certified rheumatologist, were included in the study. Results: The PA and Doppler ultrasound imaging of Achilles enthesitis resulting from these 10 SpA patients were compared to those from 10 healthy volunteers, leading to statistically significant differences (p

Significance: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase images efficiently, their applications in diagnostic testing are limited by the lack of transparency. More interpretable results such as morphological and biochemical characteristics of individual RBCs are highly desirable. Aim: An end-to-end deep-learning model was developed to efficiently discriminate thalassemic RBCs (tRBCs) from healthy RBCs (hRBCs) in quantitative phase images and segment RBCs for single-cell characterization. Approach: Two-dimensional quantitative phase images of hRBCs and tRBCs were acquired using digital holographic microscopy. A mask region-based convolutional neural network (Mask R-CNN) model was trained to discriminate tRBCs and segment individual RBCs. Characterization of tRBCs was achieved utilizing SHapley Additive exPlanation analysis and canonical correlation analysis on automatically segmented RBC phase images. Results: The implemented model achieved 97.8% accuracy in detecting tRBCs. Phase-shift statistics showed the highest influence on the correct classification of tRBCs. Associations between the phase-shift features and three-dimensional morphological features were revealed. Conclusions: The implemented Mask R-CNN model accurately identified tRBCs and segmented RBCs to provide single-RBC characterization, which has the potential to aid clinical decision-making.

Reducing the bit-depth is an effective approach to lower the cost of optical coherence tomography (OCT) systems and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit-depth will lead to the degeneration of the detection sensitivity thus reduce the signal-to-noise ratio (SNR) of OCT images. In this paper, we propose to use deep learning for the reconstruction of the high SNR OCT images from the low bit-depth acquisition. Its feasibility was preliminarily evaluated by applying the proposed method to the quantized $3\sim8$-bit data from native 12-bit interference fringes. We employed a pixel-to-pixel generative adversarial network architecture in the low to high bit-depth OCT image transition. Retinal OCT data of a healthy subject from a homemade spectral-domain OCT system was used in the study. Extensively qualitative and quantitative results show this deep-learning-based approach could significantly improve the SNR of the low bit-depth OCT images especially at the choroidal region. Superior similarity and SNR between the reconstructed images and the original 12-bit OCT images could be derived when the bit-depth $\geq 5$. This work demonstrates the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings., Comment: submitted

Significance: Optical coherence tomography (OCT) is widely used as a potential diagnostic tool for a variety of diseases including various types of cancer. However, sensitivity and specificity analyses of OCT in different cancers yield results varying from 11% to 100%. Hence, there is a need for more detailed statistical analysis of blinded reader studies. Aim: Extensive statistical analysis is performed on results from a blinded study involving OCT of breast tumor margins to assess the impact of reader variability on sensitivity and specificity. Approach: Five readers with varying levels of experience reading OCT images assessed 50 OCT images of breast tumor margins collected using an intraoperative OCT system. Statistical modeling and analysis was performed using the R language to analyze reader experience and variability. Results: Statistical analysis showed that the readers’ prior experience with OCT images was directly related to the probability of the readers’ assessment agreeing with histology. Additionally, results from readers with prior experience specific to OCT in breast cancer had a higher probability of agreement with histology compared to readers with experience with OCT in other (noncancer) diseases. Conclusions: The results from this study demonstrate the potential impact of reader training and experience in the assessment of sensitivity and specificity. They also demonstrate even greater potential improvement in diagnostic performance by combining results from multiple readers. These preliminary findings suggest valuable directions for further study.

Significance: Differential phase contrast (DPC) is a well-known imaging technique for phase imaging. However, simultaneously acquiring multidepth DPC images is a non-trivial task. We propose simultaneous multiplane DPC imaging using volume holographic microscopy (VHM). Aim: To design and implement a new configuration of DPC-VHM for multiplane imaging. Approach: The angularly multiplexed volume holographic gratings (AMVHGs) and the wavelength-coded volume holographic gratings (WC-VHGs) are used for this purpose. To obtain asymmetric illumination for DPC images, a dynamic illumination system is designed by modifying the regular Köhler illumination using a thin film transistor panel (TFT-panel). Results: Multidepth DPC images of standard resolution chart and biosamples were used to compare imaging performance with the corresponding bright-field images. An average contrast enhancement of around three times is observed for target resolution chart by DPC-VHM. Imaging performance of our system is studied by modulation transfer function analysis, which suggests that DPC-VHM not only suppresses the DC component but also enhances high-frequency information. Conclusions: Proposed DPC-VHM can acquire multidepth-resolved DPC images without axial scanning. The illumination part of the system is adjustable so that the system can be adapted to bright-field mode, phase contrast mode, and DPC mode by controlling the pattern on the TFT-panel.

Significance: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics. Aim: We aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration. Approach: Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves. Results: Our interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs. Conclusions: From a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.

Significance: Recent advances in nonlinear optics in neuroscience have focused on using two ultrafast lasers for activity imaging and optogenetic stimulation. Broadband femtosecond light sources can obviate the need for multiple lasers by spectral separation for chromatically targeted excitation. Aim: We present a photonic crystal fiber (PCF)-based supercontinuum source for spectrally resolved two-photon (2P) imaging and excitation of GCaMP6s and C1V1-mCherry, respectively. Approach: A PCF is pumped using a 20-MHz repetition rate femtosecond laser to generate a supercontinuum of light, which is spectrally separated, compressed, and recombined to image GCaMP6s (930 nm excitation) and stimulate the optogenetic protein, C1V1-mCherry (1060 nm excitation). Galvanometric spiral scanning is employed on a single-cell level for multiphoton excitation and high-speed resonant scanning is employed for imaging of calcium activity. Results: Continuous wave lasers were used to verify functionality of optogenetic activation followed by directed 2P excitation. Results from these experiments demonstrate the utility of a supercontinuum light source for simultaneous, single-cell excitation and calcium imaging. Conclusions: A PCF-based supercontinuum light source was employed for simultaneous imaging and excitation of calcium dynamics in brain tissue. Pumped PCFs can serve as powerful light sources for imaging and activation of neural activity, and overcome the limited spectra and space associated with multilaser approaches.

Significance: Mid-infrared (IR) imaging based on the vibrational transition of biomolecules provides good chemical-specific contrast in label-free imaging of biology tissues, making it a popular tool in both biomedical studies and clinical applications. However, the current technology typically requires thin and dried or extremely flat samples, whose complicated processing limits this technology’s broader translation. Aim: To address this issue, we report mid-IR photoacoustic microscopy (PAM), which can readily work with fresh and thick tissue samples, even when they have rough surfaces. Approach: We developed a transmission-mode mid-IR PAM system employing an optical parametric oscillation laser operating in the wavelength range from 2.5 to 12 μm. Due to its high sensitivity to optical absorption and the low ultrasonic attenuation of tissue, our PAM achieved greater probing depth than Fourier transform IR spectroscopy, thus enabling imaging fresh and thick tissue samples with rough surfaces. Results: In our spectroscopy study, the CH2 symmetric stretching at 2850 cm−1 (3508 nm) was found to be an excellent source of endogenous contrast for lipids. At this wavenumber, we demonstrated label-free imaging of the lipid composition in fresh, manually cut, and unprocessed tissue sections of up to 3-mm thickness. Conclusions: Our technology requires no time-consuming sample preparation procedure and has great potential in both fast clinical histological analysis and fundamental biological studies.

Significance: Cerebral oxygenation changes in the superior, middle, and medial gyri were used to elucidate spatial impairments of autonomic hemodynamic recovery during the head-up tilt table test (HUTT) in Parkinson’s disease (PD) patients with orthostatic intolerance (OI) symptoms. Aim: To analyze dynamic oxygenation changes during the HUTT and classify PD patients with OI symptoms using clinical and oxygenation features. Approach: Thirty-nine PD patients with OI symptoms [10: orthostatic hypotension (PD-OH); 29: normal HUTT results (PD-NOR)] and seven healthy controls (HCs) were recruited. Prefrontal oxyhemoglobin (HbO) changes during the HUTT were reconstructed with diffuse optical tomography and segmented using the automated anatomical labeling system. Decision trees were used for classification. Results: HCs and PD-NOR patients with positive rates of HbO change (PD-POS) showed the greatest HbO recovery in the superior frontal gyrus (SFG) during tilt. PD-OH and PD-NOR patients with negative rates of HbO change (PD-NEG) showed asymmetric reoxygenation. The classification accuracy was 89.4% for PD-POS versus PD-NEG, 71% for PD-NOR versus PD-OH, and 55.8% for PD-POS versus PD-NEG versus PD-OH. The oxygenation features were more discriminative than the clinical features. Conclusions: PD-OH showed decreased right SFG function, which may be associated with impaired compensatory autonomic responses to orthostatic stress.

Significance: Systematic studies of the physiological outputs induced by infrared (IR)-mediated inhibition of motor nerves can provide guidance for therapeutic applications and offer critical insights into IR light modulation of complex neural networks. Aim: We explore the IR-mediated inhibition of action potentials (APs) that either propagate along single axons or are initiated locally and their downstream synaptic transmission responses. Approach: APs were evoked locally by two-electrode current clamp or at a distance for propagating APs. The neuromuscular transmission was recorded with intracellular electrodes in muscle cells or macro-patch pipettes on terminal bouton clusters. Results: IR light pulses completely and reversibly terminate the locally initiated APs firing at low frequencies, which leads to blocking of the synaptic transmission. However, IR light pulses only suppress but do not block the amplitude and duration of propagating APs nor locally initiated APs firing at high frequencies. Such suppressed APs do not influence the postsynaptic responses at a distance. While the suppression of AP amplitude and duration is similar for propagating and locally evoked APs, only the former exhibits a 7% to 21% increase in the maximum time derivative of the AP rising phase. Conclusions: The suppressed APs of motor axons can resume their waveforms after passing the localized IR light illumination site, leaving the muscular and synaptic responses unchanged. IR-mediated modulation on propagating and locally evoked APs should be considered as two separate models for axonal and somatic modulations.

Significance: Despite recent developments in microscopy, temporal aliasing can arise when imaging dynamic samples. Modern sampling frameworks, such as generalized sampling, mitigate aliasing but require measurement of temporally overlapping and potentially negative-valued inner products. Conventional cameras cannot collect these directly as they operate sequentially and are only sensitive to light intensity., Aim: We aim to mitigate aliasing in microscopy of dynamic monochrome samples by implementing generalized sampling via the use of a color camera and modulated color illumination., Approach: We solve the overlap problem by spectrally multiplexing the acquisitions and using (positive) B-spline segments as projection kernels. Reconstruction involves spectral unmixing and inverse filtering. We implemented this method using a color LED illuminator. We evaluated its performance by imaging a rotating grid and its applicability by imaging the beating zebrafish embryo heart in transmission and light-sheet microscopes., Results: Compared to stroboscopic imaging, our method mitigates aliasing with performance improving as the projection order increases. The approach can be implemented in conventional microscopes but is limited by the number of available LED colors and camera channels., Conclusions: Generalized sampling can be implemented via color modulation in microscopy to mitigate temporal aliasing. The simple hardware requirements could make it applicable to other optical imaging modalities. (C) The Authors.

Significance: Ultrafast fiber lasers are an attractive alternative to bulk lasers for nonlinear optical microscopy for their compactness and low cost. The high relative intensity noise (RIN) of these lasers poses a challenge for pump-probe measurements such as transient absorption and stimulated Raman scattering, along with modalities that provide label-free contrast from the vibrational and electronic structure of molecules. Aim: Digital adaptive filtering was applied to determine the applicability for canceling laser RIN in a transient absorption microscope with an ultrafast fiber laser source. Approach: Digitized signals from the transmitted probe and reference photodetectors were fed to an adaptive filter in MATLAB, running in a noise canceling configuration. This result was then fed to a software lock-in algorithm to demodulate the pump-probe signal. Images were built up one line scan at a time with a 3.5-kHz resonant scanner, with 100× averaging. The imaging target was Bi4Ge3O12, which exhibits nondegenerate two-photon absorption at the pump/probe wavelengths used (530-nm pump and 490-nm probe). Results: Without adaptive noise cancellation, the lock-in output primarily passes the laser RIN within its detection bandwidth, resulting in images that closely follow the linear transmissivity and lack sensitivity to pump-probe time delay. With adaptive noise cancellation in front of the lock-in, the RIN rejection is enough to restore the z-sectioning and sensitivity to pump-probe delay, as expected for transient absorption. Results were limited primarily by noise from the photodetector and analog-to-digital converter. Conclusions: Digital adaptive noise cancellation, even when limited by electronics noise, can recover pump-probe signals by removal of laser RIN, under conditions where averaging alone fails.

Significance: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices. Approach: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of ∼4860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting. Results: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores=94% for non-zeros padding and F1-score=96% for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification. Conclusions: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability.

Significance: Diffuse correlation spectroscopy (DCS) is an established optical modality that enables noninvasive measurements of blood flow in deep tissue by quantifying the temporal light intensity fluctuations generated by dynamic scattering of moving red blood cells. Compared with near-infrared spectroscopy, DCS is hampered by a limited signal-to-noise ratio (SNR) due to the need to use small detection apertures to preserve speckle contrast. However, DCS is a dynamic light scattering technique and does not rely on hemoglobin contrast; thus, there are significant SNR advantages to using longer wavelengths (>1000 nm) for the DCS measurement due to a variety of biophysical and regulatory factors. Aim: We offer a quantitative assessment of the benefits and challenges of operating DCS at 1064 nm versus the typical 765 to 850 nm wavelength through simulations and experimental demonstrations. Approach: We evaluate the photon budget, depth sensitivity, and SNR for detecting blood flow changes using numerical simulations. We discuss continuous wave (CW) and time-domain (TD) DCS hardware considerations for 1064 nm operation. We report proof-of-concept measurements in tissue-like phantoms and healthy adult volunteers. Results: DCS at 1064 nm offers higher intrinsic sensitivity to deep tissue compared with DCS measurements at the typically used wavelength range (765 to 850 nm) due to increased photon counts and a slower autocorrelation decay. These advantages are explored using simulations and are demonstrated using phantom and in vivo measurements. We show the first high-speed (cardiac pulsation-resolved), high-SNR measurements at large source–detector separation (3 cm) for CW-DCS and late temporal gates (1 ns) for TD-DCS. Conclusions: DCS at 1064 nm offers a leap forward in the ability to monitor deep tissue blood flow and could be especially useful in increasing the reliability of cerebral blood flow monitoring in adults.

Significance: The use of diffuse correlation spectroscopy (DCS) has shown efficacy in research studies as a technique capable of noninvasively monitoring blood flow in tissue with applications in neuromonitoring, exercise science, and breast cancer management. The ability of DCS to resolve blood flow in these tissues is related to the optical sensitivity and signal-to-noise ratio (SNR) of the measurements, which in some cases, particularly adult cerebral blood flow measurements, is inadequate in a significant portion of the population. Improvements to DCS sensitivity and SNR could allow for greater clinical translation of this technique. Aim: Interferometric diffuse correlation spectroscopy (iDCS) was characterized and compared to traditional homodyne DCS to determine possible benefits of utilizing heterodyne detection. Approach: An iDCS system was constructed by modifying a homodyne DCS system with fused fiber couplers to create a Mach–Zehnder interferometer. Comparisons between homodyne and heterodyne detection were performed using an intralipid phantom characterized at two extended source–detector separations (2.4, 3.6 cm), different photon count rates, and a range of reference arm power levels. Characterization of the iDCS signal mixing was compared to theory. Precision of the estimation of the diffusion coefficient and SNR of the autocorrelation curve were compared between different measurement conditions that mimicked what would be seen in vivo. Results: The mixture of signals present in the heterodyne autocorrelation function was found to agree with the derived theory and resulted in accurate measurement of the diffusion coefficient of the phantom. Improvement of the SNR of the autocorrelation curve up to ∼2× and up to 80% reduction in the variability of the diffusion coefficient fit were observed for all measurement cases as a function of increased reference arm power. Conclusions: iDCS has the potential to improve characterization of blood flow in tissue at extended source–detector separations, enhancing depth sensitivity and SNR.

Significance: Functional near-infrared spectroscopy (fNIRS) uses surface-placed light sources and detectors to record underlying changes in the brain due to fluctuations in hemoglobin levels and oxygenation. Since these measurements are recorded from the surface of the scalp, the mapping from underlying regions-of-interest (ROIs) in the brain space to the fNIRS channel space measurements depends on the registration of the sensors, the anatomy of the head/brain, and the sensitivity of these diffuse measurements through the tissue. However, small displacements in the probe position can change the distribution of recorded brain activity across the fNIRS measurements. Aim: We propose an approach using either individual or atlas-based brain-space anatomical information to define ROI-based statistical hypotheses to test the null involvement of specific regions, which allows us to test the analogous ROI across subjects while adjusting for fNIRS probe placement and sensitivity differences due to head size variations without a localizer task. Approach: We use the optical forward model to project the underlying brain-space ROI into a tapered contrast vector, which defines the relative weighting of the fNIRS channels contributing to the ROI and allows us to test the null hypothesis of no brain activity in this region during a functional task. We demonstrate this method through simulation and compare the sensitivity-specificity of this approach to other conventional methods. Results: We examine the performance of this method in the scenario where head size and probe registration are both an accurately known parameters and where this is subject to unknown experimental errors. This method is compared with the performance of the conventional method using 364 different simulation parameter combinations. Conclusion: The proposed method is always recommended in ROI-based analysis, since it significantly improves the analysis performance without a localizer task, wherever the fNIRS probe registration is known or unknown.

Significance: Cardiac troponin I (cTnI) is a primary biomarker for diagnosis of myocardial infarction (MI). In contrast to central laboratory tests for cTnI, point-of-care (POC) testing has the advantage of providing results when the patient is first encountered, which helps high-risk patients to be treated more rapidly and low-risk patients to be released in a timely fashion. A paper fluidic platform is good for POC testing because the paper is abundant, low cost, and disposable. However, current cTnI assays on paper platforms use antibodies as the recognition element, which has limitations due to the high cost of production and antibody stability issues at the POC. Aim: To develop an aptamer-based assay on a paper strip using surface-enhanced resonance Raman spectroscopy (SERRS) for detection of cTnI in the clinically relevant range at the POC. Approach: Gold nanoparticles (AuNPs) were functionalized with a Raman reporter molecule, malachite green isothiocyanate. The functionalized AuNPs were encapsulated in a silica shell and provided a SERRS signal using a handheld Raman system with a 638-nm excitation wavelength. A primary aptamer and a secondary aptamer of cTnI were used in a sandwich assay format to bind the cTnI on a test line of a paper fluidic platform. By measuring the SERRS signal from the test line, the concentration of cTnI was quantitatively determined. Results: The aptamer-based SERRS assay on a paper strip had a detection range of 0.016 to 0.1 ng/ml for cTnI, had good selectivity for cTnI compared to three other markers, had good stability over 10 days, and had good performance in the more complex serum sample matrix. Conclusions: The aptamer-based SERRS assay on a paper strip has the potential to provide a sensitive, selective, stable, repeatable, and cost-effective platform for the detection of cTnI toward eventual use in diagnosis of MI at the POC.

Significance: Light-field microscopy (LFM) enables high signal-to-noise ratio (SNR) and light efficient volume imaging at fast frame rates. Voltage imaging with genetically encoded voltage indicators (GEVIs) stands to particularly benefit from LFM’s volumetric imaging capability due to high required sampling rates and limited probe brightness and functional sensitivity. Aim: We demonstrate subcellular resolution GEVI light-field imaging in acute mouse brain slices resolving dendritic voltage signals in three spatial dimensions. Approach: We imaged action potential-induced fluorescence transients in mouse brain slices sparsely expressing the GEVI VSFP-Butterfly 1.2 in wide-field microscopy (WFM) and LFM modes. We compared functional signal SNR and localization between different LFM reconstruction approaches and between LFM and WFM. Results: LFM enabled three-dimensional (3-D) localization of action potential-induced fluorescence transients in neuronal somata and dendrites. Nonregularized deconvolution decreased SNR with increased iteration number compared to synthetic refocusing but increased axial and lateral signal localization. SNR was unaffected for LFM compared to WFM. Conclusions: LFM enables 3-D localization of fluorescence transients, therefore eliminating the need for structures to lie in a single focal plane. These results demonstrate LFM’s potential for studying dendritic integration and action potential propagation in three spatial dimensions.

Significance: A major obstacle to studying resident microglia has been their similarity to infiltrating immune cell types and the lack of unique protein markers for identifying the functional state. Given the role of microglia in all neural diseases and insults, accurate tools for detecting their function beyond morphologic alterations are necessary. Aims: We hypothesized that microglia would have unique metabolic fluxes in reduced nicotinamide adenine dinucleotide (NADH) that would be detectable by relative changes in fluorescence lifetime imaging microscopy (FLIM) parameters, allowing for identification of their activation status. Fluorescence lifetime of NADH has been previously demonstrated to show differences in metabolic fluxes. Approach: Here, we investigate the use of the label-free method of FLIM-based detection of the endogenous metabolic cofactor NADH to identify microglia and characterize their activation status. To test whether microglial activation would also confer a unique NADH lifetime signature, murine primary microglial cultures and adult mice were treated with lipopolysaccharide (LPS). Results: We found that LPS-induced microglia activation correlates with detected changes in NADH lifetime and its free-bound ratio. This indicates that NADH lifetime can be used to monitor microglia activation in a label-free fashion. Moreover, we found that there is an LPS dose-dependent change associated with reactive microglia lifetime fluxes, which is also replicated over time after LPS treatment. Conclusion: We have demonstrated a label-free way of monitoring microglia activation via quantifying lifetime of endogenous metabolic coenzyme NADH. Upon LPS-induced activation, there is a significant change in the fluorescence lifetime following activation. Together, these results indicate that NADH FLIM approaches can be used as a method to characterize microglia activation state, both in vitro and ex vivo.

Significance: Wide-field measurement of cellular membrane dynamics with high spatiotemporal resolution can facilitate analysis of the computing properties of neuronal circuits. Quantum microscopy using a nitrogen-vacancy (NV) center is a promising technique to achieve this goal. Aim: We propose a proof-of-principle approach to NV-based neuron functional imaging. Approach: This goal is achieved by engineering NV quantum sensors in diamond nanopillar arrays and switching their sensing mode to detect the changes in the electric fields instead of the magnetic fields, which has the potential to greatly improve signal detection. Apart from containing the NV quantum sensors, nanopillars also function as waveguides, delivering the excitation/emission light to improve sensitivity. The nanopillars also improve the amplitude of the neuron electric field sensed by the NV by removing screening charges. When the nanopillar array is used as a cell niche, it acts as a cell scaffolds which makes the pillars function as biomechanical cues that facilitate the growth and formation of neuronal circuits. Based on these growth patterns, numerical modeling of the nanoelectromagnetics between the nanopillar and the neuron was also performed. Results: The growth study showed that nanopillars with a 2-μm pitch and a 200-nm diameter show ideal growth patterns for nanopillar sensing. The modeling showed an electric field amplitude as high as ≈1.02×1010 mV/m at an NV 100 nm from the membrane, a value almost 10 times the minimum field that the NV can detect. Conclusion: This proof-of-concept study demonstrated unprecedented NV sensing potential for the functional imaging of mammalian neuron signals.

Significance: The cerebral metabolic rate of oxygen (CMRO2) is an important indicator of brain function and pathology. Knowledge about its magnitude is also required for proper interpretation of the blood oxygenation level-dependent (BOLD) signal measured with functional MRI. Despite the need for estimating CMRO2, no gold standard exists. Traditionally, the estimation of CMRO2 has been pursued with somewhat indirect approaches combining several different types of measurements with mathematical modeling of the underlying physiological processes. The recent ability to measure the level of oxygen (pO2) in cortex with two-photon resolution in in vivo conditions has provided a more direct way for estimating CMRO2, but has so far only been used to estimate the average CMRO2 close to cortical penetrating arterioles in rats. Aim: The aim of this study was to propose a method to provide spatial maps of CMRO2 based on two-photon pO2 measurements. Approach: The method has two key steps. First, the pO2 maps are spatially smoothed to reduce the effects of noise in the measurements. Next, the Laplace operator (a double spatial derivative) in two spatial dimensions is applied on the smoothed pO2 maps to obtain spatially resolved CMRO2 estimates. Result: The smoothing introduces a bias, and a balance must be found where the effects of the noise are sufficiently reduced without introducing too much bias. In this model-based study, we explored this balance using synthetic model-based data, that is, data where the spatial maps of CMRO2 were preset and thus known. The corresponding pO2 maps were found by solving the Poisson equation, which relates CMRO2 and pO2. MATLAB code for using the method is provided. Conclusion: Through this model-based study, we propose a method for estimating CMRO2 with high spatial resolution based on measurements of pO2 in cerebral cortex.

Significance: Understanding how the valveless embryonic heart pumps blood is essential to elucidate biomechanical cues regulating cardiogenesis, which is important for the advancement of congenital heart defects research. However, methods capable of embryonic cardiac pumping analysis remain limited, and assessing this highly dynamic process in mammalian embryos is challenging. New approaches are critically needed to address this hurdle. Aim: We report an imaging-based approach for functional assessment of localized pumping dynamics in the early tubular embryonic mouse heart. Approach: Four-dimensional optical coherence tomography was used to obtain structural and Doppler hemodynamic imaging of the beating heart in live mouse embryos at embryonic day 9.25. The pumping assessment was performed based on the volumetric blood flow rate, flow resistance within the heart tube, and pressure gradient induced by heart wall movements. The relation between the blood flow, the pressure gradient, and the resistance to flow were evaluated through temporal analyses and Granger causality test. Results: In the ventricles, our method revealed connections between the temporal profiles of pressure gradient and volumetric blood flow rate. Statistically significant causal relation from the pressure gradient to the blood flow was demonstrated. Our analysis also suggests that cardiac pumping in the early ventricles is a combination of suction and pushing. In contrast, in the outflow tract, where the conduction wave is slower than the blood flow, we did not find significant causal relation from pressure to flow, suggesting that, different from ventricular regions, the local active contraction of the outflow tract is unlikely to drive the flow in that region. Conclusions: We present an imaging-based approach that enables localized assessment of pumping dynamics in the mouse tubular embryonic heart. This method creates a new opportunity for functional analysis of the pumping mechanism underlying the developing mammalian heart at early stages and could be useful for studying biomechanical changes in mutant embryonic hearts that model congenital heart defects.

Significance: The development of ultralow energy photoacoustic microscopy (PAM) on the clinically relevant pigmented rabbit eye model paves a road toward translation of the emerging PAM technology in ophthalmology clinics. Aim: Since the eye is particularly vulnerable to laser damage, we aim to develop an ultralow energy PAM system to significantly improve the laser safety of PAM by increasing the sensitivity of the system and reducing the incident laser energy for imaging. Approach: A multichannel data acquisition circuit with two-stage signal amplification was specially designed, which, in combination with the application of 3 by 3 median filter in the spatial domain, significantly improved the signal-to-noise ratio of the PAM system. The safety of this system was validated by histopathology, fluorescein angiography, and fundus photography. Results: Experiments performed on pigmented rabbits demonstrated that, when using this ultralow energy PAM system, satisfactory image quality can be achieved in the eye with an incident laser fluence that is only 1% of the American National Standards Institute safety limit. Fundus photography, fluorescein angiography, and histopathology were performed after the imaging procedure, and no retinal or ocular damage was observed. Conclusions: The proposed ultralow energy PAM system has excellent safety and holds potential to be developed into a clinical tool for ocular imaging.

Significance: Research in tissue engineering and in vitro organ formation has recently intensified. To assess tissue morphology, the method of choice today is restricted primarily to histology. Thus novel tools are required to enable noninvasive, and preferably label-free, three-dimensional imaging that is more compatible with futuristic organ-on-a-chip models. Aim: We investigate the potential for using multiphoton microscopy (MPM) as a label-free in vitro approach to monitor calcium-induced epidermal differentiation. Approach: In vitro epidermis was cultured at the air–liquid interface in varying calcium concentrations. Morphology and tissue architecture were investigated using MPM based on visualizing cellular autofluorescence. Results: Distinct morphologies corresponding to epidermal differentiation were observed. In addition, Ca2+-induced effects could be distinguished based on the architectural differences in stratification in the tissue cultures. Conclusions: Our study shows that MPM based on cellular autofluorescence enables visualization of Ca2+-induced differentiation in epidermal skin models in vitro. The technique has potential to be further adapted as a noninvasive, label-free, and real-time tool to monitor tissue regeneration and organ formation in vitro.

Significance: Knee osteoarthritis (OA) is a common joint disease causing chronic pain and functional alterations (stiffness and swelling) in the elderly population. OA is currently treated pharmacologically with analgesics, although neuromodulation via transcranial direct current stimulation (tDCS) has recently generated a growing interest as a safe side-effect free treatment alternative or a complement to medications for chronic pain conditions. Although a number of studies have shown that tDCS has a beneficial effect on behavioral measures of pain, the mechanistic action of neuromodulation on pain sensitivity and coping at the central nervous system is not well understood. Aim: We aimed at observing longitudinal changes of cortical hemodynamics in older adults with knee OA associated with a two-week-long tDCS self-treatment protocol. Approach: Hemodynamics was measured bilaterally in the motor and somatosensory cortices with functional near-infrared spectroscopy (fNIRS) in response to thermal pain induced ipsilaterally to the knee primarily affected by OA. Results: We found that both oxyhemoglobin- and deoxyhemoglobin-related functional activations significantly increased during the course of the tDCS treatment, supporting the notion that tDCS yields an increased cortical excitability. Concurrently, clinical measures of pain decreased with tDCS treatment, hinting at a potential spatial dissociation between cortically mediated pain perception and suppression and the prevalence of neuromodulatory effects over cortical pain processing. Conclusions: fNIRS is a valid method for objectively tracking pain in an ambulatory setting and it could potentially be used to inform strategies for optimized tDCS treatment and to develop innovative tDCS protocols.

Significance: We present a Monte Carlo (MC) computational framework that simulates near-infrared (NIR) hyperspectral imaging (HSI) aimed at assisting quantification of the in vivo hemodynamic and metabolic states of the exposed cerebral cortex in small animal experiments. This can be done by targeting the NIR spectral signatures of oxygenated (HbO2) and deoxygenated (HHb) hemoglobin for hemodynamics as well as the oxidative state of cytochrome-c-oxidase (oxCCO) for measuring tissue metabolism. Aim: The aim of this work is to investigate the performances of HSI for this specific application as well as to assess key factors for the future design and operation of a benchtop system. Approach: The MC framework, based on Mesh-based Monte Carlo (MMC), reproduces a section of the exposed cortex of a mouse from an in vivo image and replicates hyperspectral illumination and detection at multiple NIR wavelengths (up to 121). Results: The results demonstrate: (1) the fitness of the MC framework to correctly simulate hyperspectral data acquisition; (2) the capability of HSI to reconstruct spatial changes in the concentrations of HbO2, HHb, and oxCCO during a simulated hypoxic condition; (3) that eight optimally selected wavelengths between 780 and 900 nm provide minimal differences in the accuracy of the hyperspectral results, compared to the “gold standard” of 121 wavelengths; and (4) the possibility to mitigate partial pathlength effects in the reconstructed data and to enhance quantification of the hemodynamic and metabolic responses. Conclusions: The MC framework is proved to be a flexible and useful tool for simulating HSI also for different applications and targets.

Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Methods capable of revealing nonlinearities in fNIRS recordings can provide new insights about how the brain functions. Aim: The temporal characteristics of fNIRS signals are explored by comprehensively investigating their fractal properties. Approach: Fractality of fNIRS signals is analyzed using scaled windowed variance (SWV), as well as using visibility graph (VG), a method which converts a given time series into a graph. Additionally, the fractality of fNIRS signals obtained under resting-state and task-based conditions is compared, and the application of fractality in differentiating brain states is demonstrated for the first time via various classification approaches. Results: Results from SWV analysis show the existence of high fractality in fNIRS recordings. It is shown that differences in the temporal characteristics of fNIRS signals related to task-based and resting-state conditions can be revealed via the VGs constructed for each case. Conclusions: fNIRS recordings, regardless of the experimental conditions, exhibit high fractality. Furthermore, VG-based metrics can be employed to differentiate rest and task-execution brain states.

Significance. Quantifying human milk composition is important for daily nutritional management in neonatal intensive cares worldwide. Photonic solutions based on visible light can potentially aid in this analysis, as energy content of human milk depends largely on fat content, and the optical scattering properties of human milk predominantly depend on the size and concentration of fat globules. However, it is expected that human milk scattering changes upon homogenization, routinely done before analysis, which may affect fat globule size. Aim. The first aim of this study was to investigate how the most common homogenization methods (gently inverting by hand, vortexing, and sonication) affect the optical properties of human milk. The second aim was to estimate the scattering contribution of casein micelles, the second most dominant scatterers in human milk. Approach. We combined diffuse reflectance spectroscopy with spectroscopic optical coherence tomography to measure the scattering coefficient μs, reduced scattering coefficient μs′, and anisotropy g between 450 and 600 nm. Results. Sonication induced the strongest changes in μs, μs′, and g compared to the gently inverted samples (203%, 202%, and 7%, respectively, at 550 nm), but also vortexing changed μs′ with 20%. Although casein micelles only showed a modest contribution to μs and g at 550 nm (7% and 1%, respectively), their contribution to μs′ was 29%. Conclusions. The scattering properties of human milk strongly depend on the homogenization method that is employed, and gentle inversion should be the preferred method. The contribution of casein micelles was relatively small for μs and g but considerably larger for μs′.