Detectability of hemodynamic oscillations in cerebral cortex through functional near-infrared spectroscopy: a simulation study.

Significance: We explore the feasibility of using time-domain (TD) and continuous-wave (CW) functional near-infrared spectroscopy (fNIRS) to monitor brain hemodynamic oscillations during resting-state activity in humans, a phenomenon that is of increasing interest in the scientific and medical community and appears to be crucial to advancing the understanding of both healthy and pathological brain functioning. Aim: Our general object is to maximize fNIRS sensitivity to brain resting-state oscillations. More specifically, we aim to define comprehensive guidelines for optimizing main operational parameters in fNIRS measurements [average photon count rate, measurement length, sampling frequency, and source-detector distance (SSD)]. In addition, we compare TD and CW fNIRS performance for the detection and localization of oscillations. Approach: A series of synthetic TD and CW fNIRS signals were generated by exploiting the solution of the diffusion equation for two different geometries of the probed medium: a homogeneous medium and a bilayer medium. Known and periodical perturbations of the concentrations of oxy- and deoxy-hemoglobin were imposed in the medium, determining changes in its optical properties. The homogeneous slab model was used to determine the effect of multiple measurement parameters on fNIRS sensitivity to oscillatory phenomena, and the bilayer model was used to evaluate and compare the abilities of TD and CW fNIRS in detecting and isolating oscillations occurring at different depths. For TD fNIRS, two approaches to enhance depth-selectivity were evaluated: first, a time-windowing of the photon distribution of time-of-flight was performed, and then, the time-dependent mean partial pathlength (TMPP) method was used to retrieve the hemoglobin concentrations in the medium. Results: In the homogeneous medium case, the sensitivity of TD and CW fNIRS to periodical perturbations of the optical properties increases proportionally with the average photon count rate, the measurement length, and the sampling frequency and approximatively with the square of the SSD. In the bilayer medium case, the time-windowing method can detect and correctly localize the presence of oscillatory components in the TD fNIRS signal, even in the presence of very low photon count rates. The TMPP method demonstrates how to correctly retrieve the periodical variation of hemoglobin at different depths from the TD fNIRS signal acquired at a single SSD. For CW fNIRS, measurements taken at typical SSDs used for short-separation channel regression show notable sensitivity to oscillations occurring in the deep layer, challenging the assumptions underlying this correction method when the focus is on analyzing oscillatory phenomena. Conclusions: We demonstrated that the TD fNIRS technique allows for the detection and depth-localization of periodical fluctuations of the hemoglobin concentrations within the probed medium using an acquisition at a single SSD, offering an alternative to multi-distance CW fNIRS setups. Moreover, we offered some valuable guidelines that can assist researchers in defining optimal experimental protocols for fNIRS studies.

Spectrum-based deep learning framework for dermatological pigment analysis and simulation.

BACKGROUND: Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer professional-level information like pigment distribution maps, but encounter practical limitations such as complex system requirements. METHODS: This study introduces a spectrum-based framework for training a deep learning model to generate melanin and hemoglobin distribution maps from skin images. This approach eliminates the need for manually prepared ground truth by synthesizing output maps into skin images for regression analysis. The framework is applied to acquire spectral data, create pigment distribution maps, and simulate pigment variations. RESULTS: Our model generated reflectance spectra and spectral images that accurately reflect pigment absorption properties, outperforming spectral upsampling methods. It produced pigment distribution maps with correlation coefficients of 0.913 for melanin and 0.941 for hemoglobin compared to the VISIA system. Additionally, the model's simulated images of pigment variations exhibited a proportional correlation with adjustments made to pigment levels. These evaluations are based on pigment absorption properties, the Individual Typology Angle (ITA), and pigment indices. CONCLUSION: The model produces pigment distribution maps comparable to those from specialized clinical equipment and simulated images with numerically adjusted pigment variations. This approach demonstrates significant promise for developing professional-level diagnostic tools for future clinical applications.

Tutorial on methods for estimation of optical absorption and scattering properties of tissue.

Significance: The estimation of tissue optical properties using diffuse optics has found a range of applications in disease detection, therapy monitoring, and general health care. Biomarkers derived from the estimated optical absorption and scattering coefficients can reflect the underlying progression of many biological processes in tissues. Aim: Complex light-tissue interactions make it challenging to disentangle the absorption and scattering coefficients, so dedicated measurement systems are required. We aim to help readers understand the measurement principles and practical considerations needed when choosing between different estimation methods based on diffuse optics. Approach: The estimation methods can be categorized as: steady state, time domain, time frequency domain (FD), spatial domain, and spatial FD. The experimental measurements are coupled with models of light-tissue interactions, which enable inverse solutions for the absorption and scattering coefficients from the measured tissue reflectance and/or transmittance. Results: The estimation of tissue optical properties has been applied to characterize a variety of ex vivo and in vivo tissues, as well as tissue-mimicking phantoms. Choosing a specific estimation method for a certain application has to trade-off its advantages and limitations. Conclusion: Optical absorption and scattering property estimation is an increasingly important and accessible approach for medical diagnosis and health monitoring.

Combined frequency domain near-infrared spectroscopy and diffuse correlation spectroscopy system for comprehensive metabolic monitoring of inspiratory muscles during loading.

Significance: Mechanical ventilation (MV) is a cornerstone technology in the intensive care unit as it assists with the delivery of oxygen in critically ill patients. The process of weaning patients from MV can be long and arduous and can lead to serious complications for many patients. Despite the known importance of inspiratory muscle function in the success of weaning, current clinical standards do not include direct monitoring of these muscles. Aim: The goal of this project was to develop and validate a combined frequency domain near-infrared spectroscopy (FD-NIRS) and diffuse correlation spectroscopy (DCS) system for the noninvasive characterization of inspiratory muscle response to a load. Approach: The system was fabricated by combining a custom digital FD-NIRS and DCS system. It was validated via liquid phantom titrations and a healthy volunteer study. The sternocleidomastoid (SCM), an accessory muscle of inspiration, was monitored during a short loading period in fourteen young, healthy volunteers. Volunteers performed two different respiratory exercises, a moderate load and a high load, which consisted of a one-minute baseline, a one-minute load, and a six-minute recovery period. Results: The system has low crosstalk between absorption, reduced scattering, and flow when tested in a set of liquid titrations. Faster dynamics were observed for changes in blood flow index (BFi), and metabolic rate of oxygen (MRO2) compared with hemoglobin + myoglobin (Hb+Mb) based parameters after the onset of loads in males. Additionally, larger percent changes in BFi, and MRO2 were observed compared with Hb+Mb parameters in both males and females. There were also sex differences in baseline values of oxygenated Hb+Mb, total Hb+Mb, and tissue saturation. Conclusions: The dynamic characteristics of Hb+Mb concentration and blood flow were distinct during loading of the SCM, suggesting that the combination of FD-NIRS and DCS may provide a more complete picture of inspiratory muscle dynamics.

Motion-resistant three-wavelength spatial frequency domain imaging system with ambient light suppression using an 8-tap CMOS image sensor.

Significance: We present a motion-resistant three-wavelength spatial frequency domain imaging (SFDI) system with ambient light suppression using an 8-tap complementary metal-oxide semiconductor (CMOS) image sensor (CIS) developed at Shizuoka University. The system addresses limitations in conventional SFDI systems, enabling reliable measurements in challenging imaging scenarios that are closer to real-world conditions. Aim: Our study demonstrates a three-wavelength SFDI system based on an 8-tap CIS. We demonstrate and evaluate the system's capability of mitigating motion artifacts and ambient light bias through tissue phantom reflectance experiments and in vivo volar forearm experiments. Approach: We incorporated the Hilbert transform to reduce the required number of projected patterns per wavelength from three to two per spatial frequency. The 8-tap image sensor has eight charge storage diodes per pixel; therefore, simultaneous image acquisition of eight images based on multi-exposure is possible. Taking advantage of this feature, the sensor simultaneously acquires images for planar illumination, sinusoidal pattern projection at three wavelengths, and ambient light. The ambient light bias is eliminated by subtracting the ambient light image from the others. Motion artifacts are suppressed by reducing the exposure and projection time for each pattern while maintaining sufficient signal levels by repeating the exposure. The system is compared to a conventional SFDI system in tissue phantom experiments and then in vivo measurements of human volar forearms. Results: The 8-tap image sensor-based SFDI system achieved an acquisition rate of 9.4 frame sets per second, with three repeated exposures during each accumulation period. The diffuse reflectance maps of three different tissue phantoms using the conventional SFDI system and the 8-tap image sensor-based SFDI system showed good agreement except for high scattering phantoms. For the in vivo volar forearm measurements, our system successfully measured total hemoglobin concentration, tissue oxygen saturation, and reduced scattering coefficient maps of the subject during motion (16.5 cm/s) and under ambient light (28.9 lx), exhibiting fewer motion artifacts compared with the conventional SFDI. Conclusions: We demonstrated the potential for motion-resistant three-wavelength SFDI system with ambient light suppression using an 8-tap CIS.

Single-distance and dual-slope frequency-domain near-infrared spectroscopy to assess skeletal muscle hemodynamics.

Significance: Non-invasive optical measurements of deep tissue (e.g., muscle) need to take into account confounding contributions from baseline and dynamic optical properties of superficial tissue (adipose tissue). Aim: Discriminate superficial and deep tissue hemodynamics using data collected with frequency-domain (FD) near-infrared spectroscopy (NIRS) in a dual-slope (DS) configuration. Approach: Experimental data were collected in vivo on the forearm of three human subjects during a 3-min arterial occlusion or 1-min venous occlusion. Theoretical data were generated using diffusion theory for two-layered media with varying values of the reduced scattering coefficient (µs') (range: 0.5 to 1.1 mm-1) and absorption coefficient (µa) (range: 0.005-0.015 mm-1) of the two layers, and top layer thickness (range: 2 to 8 mm). Data were analyzed using diffusion theory for a homogeneous semi-infinite medium. Results: Experimental data in vivo were consistent with simulated data for a two-layered medium with a larger µs' in the top layer, comparable absorption changes in the top and bottom layers during venous occlusion, and smaller absorption changes in the top vs. bottom layers during arterial occlusion. Conclusions: The dataset generated by DS FD-NIRS may allow for discrimination of superficial and deep absorption changes in two-layered media, thus lending itself to individual measurements of hemodynamics in adipose and muscle tissue.

Quantifying tissue properties and absolute hemodynamics using coherent spatial imaging.

Significance: Measuring hemodynamic function is crucial for health assessment. Optical signals provide relative hemoglobin concentration changes, but absolute measurements require costly, bulky technology. Speckleplethysmography (SPG) uses coherent light to detect speckle fluctuations. Combining SPG with multispectral measurements may provide important physiological information on blood flow and absolute hemoglobin concentration. Aim: To develop an affordable optical technology to measure tissue absorption, scattering, hemoglobin concentrations, tissue oxygen saturation (StO2), and blood flow. Approach: We integrated reflectance spectroscopy and laser speckle imaging to create coherent spatial imaging (CSI). CSI was validated against spatial frequency domain imaging (SFDI) using phantom-based measurements. In vivo arterial and venous occlusion experiments compared CSI with diffuse optical spectroscopy/diffuse correlation spectroscopy (DOS/DCS) measurements. Results: CSI and SFDI agreed on tissue absorption and scattering in phantom tests. CSI and DOS/DCS showed similar trends and agreement in arterial occlusion results. During venous occlusion, both uncorrected and corrected blood flow decreased with increasing pressure, with an ∼200% difference in overall blood flow decrease between the methods. CSI and DOS/DCS data showed expected hemoglobin concentrations, StO2, and blood flow trends. Conclusions: CSI provides affordable and comprehensive hemodynamic information. It can potentially detect dysfunction and improve measurements, such as blood pressure, SpO2, and metabolism.

Diffuse Optical Monitoring of Cerebral Hemodynamics and Oxygen Metabolism during and after Cardiopulmonary Bypass: Hematocrit Correction and Neurological Vulnerability.

Cardiopulmonary bypass (CPB) provides cerebral oxygenation and blood flow (CBF) during neonatal congenital heart surgery, but the impacts of CPB on brain oxygen supply and metabolic demands are generally unknown. To elucidate this physiology, we used diffuse correlation spectroscopy and frequency-domain diffuse optical spectroscopy to continuously measure CBF, oxygen extraction fraction (OEF), and oxygen metabolism (CMRO2) in 27 neonatal swine before, during, and up to 24 h after CPB. Concurrently, we sampled cerebral microdialysis biomarkers of metabolic distress (lactate-pyruvate ratio) and injury (glycerol). We applied a novel theoretical approach to correct for hematocrit variation during optical quantification of CBF in vivo. Without correction, a mean (95% CI) +53% (42, 63) increase in hematocrit resulted in a physiologically improbable +58% (27, 90) increase in CMRO2 relative to baseline at CPB initiation; following correction, CMRO2 did not differ from baseline at this timepoint. After CPB initiation, OEF increased but CBF and CMRO2 decreased with CPB time; these temporal trends persisted for 0-8 h following CPB and coincided with a 48% (7, 90) elevation of glycerol. The temporal trends and glycerol elevation resolved by 8-24 h. The hematocrit correction improved quantification of cerebral physiologic trends that precede and coincide with neurological injury following CPB.

Quantitative hemodynamic imaging: a method to correct the effects of optical properties on laser speckle imaging.

Significance: Studying cerebral hemodynamics may provide diagnostic information on neurological conditions. Wide-field imaging techniques, such as laser speckle imaging (LSI) and optical intrinsic signal imaging, are commonly used to study cerebral hemodynamics. However, they often do not account appropriately for the optical properties of the brain that can vary among subjects and even during a single measurement. Here, we describe the combination of LSI and spatial-frequency domain imaging (SFDI) into a wide-field quantitative hemodynamic imaging (QHI) system that can correct the effects of optical properties on LSI measurements to achieve a quantitative measurement of cerebral blood flow (CBF). Aim: We describe the design, fabrication, and testing of QHI. Approach: The QHI hardware combines LSI and SFDI with spatial and temporal synchronization. We characterized system sensitivity, accuracy, and precision with tissue-mimicking phantoms. With SFDI optical property measurements, we describe a method derived from dynamic light scattering to obtain absolute CBF values from LSI and SFDI measurements. We illustrate the potential benefits of absolute CBF measurements in resting-state and dynamic experiments. Results: QHI achieved a 50-Hz raw acquisition frame rate with a 10×10 mm field of view and flow sensitivity up to ∼4 mm/s. The extracted SFDI optical properties agreed well with a commercial system (R2≥0.98). The system showed high stability with low coefficients of variations over multiple sessions within the same day (<1%) and over multiple days (<4%). When optical properties were considered, the in-vivo hypercapnia gas challenge showed a slight difference in CBF (-1.5% to 0.5% difference). The in-vivo resting-state experiment showed a change in CBF ranking for nine out of 13 animals when the correction method was applied to LSI CBF measurements. Conclusions: We developed a wide-field QHI system to account for the confounding effects of optical properties on CBF LSI measurements using the information obtained from SFDI.

Comparison of numerical-integration-based methods for blood flow estimation in diffuse correlation spectroscopy.

BACKGROUND AND OBJECTIVE: Diffuse correlation spectroscopy (DCS) is an optical blood flow monitoring technology that has been utilized in various biomedical applications. In signal processing of DCS, nonlinear fitting of the experimental data and the theoretical model can be a hindrance in real-time blood flow monitoring. As one of the approaches to resolve the issue, INISg1, the inverse of numerical integration of squared g1 (a normalized electric field autocorrelation function), that could surpass the state-of-the-art technique at the time in terms of signal processing speed, has been introduced. While it is possible to implement INISg1 using various numerical integration methods, no relevant studies have been performed. Meanwhile, INISg1 was only tested within limited experimental conditions, which cannot guarantee the robustness of INISg1 in various experimental conditions. Thus, this study aims to introduce variants of INISg1 and perform a thorough comparison of the original INISg1 and its variants. METHODS: In this study, based on the right Riemann sum (RR) and trapezoid rule (TR) of numerical integration, INISg1_RR and INISg1_TR are suggested. They are thoroughly compared with the original INISg1 using model-based simulations that offer us control of most of the experimental conditions, including integration time, ß, and photon count rate. RESULTS: Except for some extreme cases, INISg1 performed more robustly than INISg1_RR and INISg1_TR. However, in extreme conditions, variants of INISg1 performed better than INISg1. With the same condition, the signal processing speed of INISg1 was 1.63 and 1.98 times faster than INISg1_RR and INISg1_TR, respectively. CONCLUSION: This study shows that INISg1 is robust in most cases and the study can be a guide for researchers using INISg1 and its variants in different types of DCS applications.

Deep learning-based pigment analysis model trained with optical approach and ground truth assistance.

This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth-like outputs, the input image resolution is restricted by computational resources. The optical approach-based regression method can alleviate this problem, but compromises performance. We propose a strategy to overcome the limitation of image resolution while preserving performance by incorporating ground truth within the optical approach-based learning structure. The proposed model decomposes skin images into melanin, hemoglobin, and shading maps, reconstructing them by solving the forward problem with reference to the ground truth for pigments. Evaluation against the VISIA system, a professional diagnostic equipment, yields correlation coefficients of 0.978 for melanin and 0.975 for hemoglobin. Furthermore, our model can produce pigment-modified images for applications like simulating treatment effects.

High-Speed Time-Domain Diffuse Optical Tomography with a Sensitivity Equation-based Neural Network.

Steady progress in time-domain diffuse optical tomography (TD-DOT) technology is allowing for the first time the design of low-cost, compact, and high-performance systems, thus promising more widespread clinical TD-DOT use, such as for recording brain tissue hemodynamics. TD-DOT is known to provide more accurate values of optical properties and physiological parameters compared to its frequency-domain or steady-state counterparts. However, achieving high temporal resolution is still difficult, as solving the inverse problem is computationally demanding, leading to relatively long reconstruction times. The runtime is further compromised by processes that involve 'nontrivial' empirical tuning of reconstruction parameters, which increases complexity and inefficiency. To address these challenges, we present a new reconstruction algorithm that combines a deep-learning approach with our previously introduced sensitivity-equation-based, non-iterative sparse optical reconstruction (SENSOR) code. The new algorithm (called SENSOR-NET) unfolds the iterations of SENSOR into a deep neural network. In this way, we achieve high-resolution sparse reconstruction using only learned parameters, thus eliminating the need to tune parameters prior to reconstruction empirically. Furthermore, once trained, the reconstruction time is not dependent on the number of sources or wavelengths used. We validate our method with numerical and experimental data and show that accurate reconstructions with 1 mm spatial resolution can be obtained in under 20 milliseconds regardless of the number of sources used in the setup. This opens the door for real-time brain monitoring and other high-speed DOT applications.

Feasibility of postprandial optical scattering of lipoproteins in blood as an optical marker of cardiovascular disease risk: modeling and experimental validation.

Significance: Blood lipid levels (i.e., triglycerides (TGs) and cholesterol) are a strong predictor of cardiovascular disease (CVD) risk. Current methods for measuring blood lipids require invasive blood draws and traditional lab testing, limiting their practicality for frequent monitoring. Optical measurements of lipoproteins, which carry TG and cholesterol in blood, may lead to simpler invasive or non-invasive methods for more frequent and rapid blood lipid measurements. Aim: To investigate the effect of lipoproteins on optical properties of blood before and after a high-fat meal (i.e., the pre- and post-prandial state). Approach: Simulations were performed using Mie theory to estimate lipoprotein scattering properties. A literature review was conducted to identify key simulation parameters including lipoprotein size distributions and number density. Experimental validation of ex-vivo blood samples was conducted using spatial frequency domain imaging. Results: Our results indicated that lipoproteins in blood, particularly very low-density lipoproteins and chylomicrons, are highly scattering in the visible and near-infrared wavelength region. Estimates of the increase in the reduced scattering coefficient (µs') of blood at 730 nm after a high-fat meal ranged from 4% for a healthy individual, to 15% for those with type 2 diabetes, to up to 64% for those suffering from hypertriglyceridemia. A reduction in blood scattering anisotropy (g) also occurred as a function of TG concentration increase. Conclusion: These findings lay the foundation for future research in the development of optical methods for invasive and non-invasive optical measure of blood lipoproteins, which could improve early detection and management of CVD risk.

A promising tool to explore functional impairment in neurodegeneration: A systematic review of near-infrared spectroscopy in dementia.

This systematic review aimed to evaluate previous studies which used near-infrared spectroscopy (NIRS) in dementia given its suitability as a diagnostic and investigative tool in this population. From 800 identified records which used NIRS in dementia and prodromal stages, 88 studies were evaluated which employed a range of tasks testing memory (29), word retrieval (24), motor (8) and visuo-spatial function (4), and which explored the resting state (32). Across these domains, dementia exhibited blunted haemodynamic responses, often localised to frontal regions of interest, and a lack of task-appropriate frontal lateralisation. Prodromal stages, such as mild cognitive impairment, revealed mixed results. Reduced cognitive performance accompanied by either diminished functional responses or hyperactivity was identified, the latter suggesting a compensatory response not present at the dementia stage. Despite clear evidence of alterations in brain oxygenation in dementia and prodromal stages, a consensus as to the nature of these changes is difficult to reach. This is likely partially due to the lack of standardisation in optical techniques and processing methods for the application of NIRS to dementia. Further studies are required exploring more naturalistic settings and a wider range of dementia subtypes.

Shortwave infrared diffuse optical wearable probe for quantification of water and lipid content in emulsion phantoms using deep learning.

Significance: The shortwave infrared (SWIR, ∼900 to 2000 nm) holds promise for label-free measurements of water and lipid content in thick tissue, owed to the chromophore-specific absorption features and low scattering in this range. In vivo water and lipid estimations have potential applications including the monitoring of hydration, volume status, edema, body composition, weight loss, and cancer. To the best of our knowledge, there are currently no point-of-care or wearable devices available that exploit the SWIR wavelength range, limiting clinical and at-home translation of this technology. Aim: To design and fabricate a diffuse optical wearable SWIR probe for water and lipid quantification in tissue. Approach: Simulations were first performed to confirm the theoretical advantage of SWIR wavelengths over near infrared (NIR). The probe was then fabricated, consisting of light emitting diodes at three wavelengths (980, 1200, 1300 nm) and four source-detector (S-D) separations (7, 10, 13, 16 mm). In vitro validation was then performed on emulsion phantoms containing varying concentrations of water, lipid, and deuterium oxide (D2O). A deep neural network was developed as the inverse model for quantity estimation. Results: Simulations indicated that SWIR wavelengths could reduce theoretical water and lipid extraction errors from ∼6% to ∼1% when compared to NIR wavelengths. The SWIR probe had good signal-to-noise ratio (>32 dB up to 10 mm S-D) and low drift (<1.1% up to 10 mm S-D). Quantification error in emulsion phantoms was 2.1±1.1% for water and -1.2±1.5% for lipid. Water estimation during a D2O dilution experiment had an error of 3.1±3.7%. Conclusions: This diffuse optical SWIR probe was able to quantify water and lipid contents in vitro with good accuracy, opening the door to human investigations.

Optimizing a two-layer method for hybrid diffuse correlation spectroscopy and frequency-domain diffuse optical spectroscopy cerebral measurements in adults.

Significance: The sensitivity to extracerebral tissues is a well-known confounder of diffuse optics. Two-layer (2L) head models can separate cerebral signals from extracerebral artifacts, but they also carry the risk of crosstalk between fitting parameters. Aim: We aim to implement a constrained 2L head model for hybrid diffuse correlation spectroscopy (DCS) and frequency-domain diffuse optical spectroscopy (FD-DOS) data and to characterize errors in cerebral blood flow and tissue absorption with the proposed model. Approach: The algorithm uses the analytical solution of a 2L cylinder and an a priori extracerebral layer thickness to fit multidistance FD-DOS (0.8 to 4 cm) and DCS (0.8 and 2.5 cm) data, assuming homogeneous tissue reduced scattering. We characterized the algorithm's accuracy for simulated data with noise generated using a 2L slab and realistic adult head geometries and for in vitro phantom data. Results: Our algorithm recovered the cerebral flow index with 6.3 [2.8, 13.2]% and 34 [30, 42]% (median absolute percent error [interquartile range]) for slab and head geometries, respectively. Corresponding errors in the cerebral absorption coefficient were 5.0 [3.0, 7.9]% and 4.6 [2.4, 7.2]% for the slab and head geometries and 8 [5, 12]% for our phantom experiment. Our results were minimally sensitive to second-layer scattering changes and were robust to cross-talk between fitting parameters. Conclusions: In adults, the constrained 2L algorithm promises to improve FD-DOS/DCS accuracy compared with the conventional semi-infinite approach.

Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector.

Significance: Interstitial fiber-based spectroscopy is gaining interest for real-time in vivo optical biopsies, endoscopic interventions, and local monitoring of therapy. Different from other photonics approaches, time-domain diffuse optical spectroscopy (TD-DOS) can probe the tissue at a few cm distance from the fiber tip and disentangle absorption from the scattering properties. Nevertheless, the signal detected at a short distance from the source is strongly dominated by the photons arriving early at the detector, thus hampering the possibility of resolving late photons, which are rich in information about depth and absorption. Aim: To fully benefit from the null-distance approach, a detector with an extremely high dynamic range is required to effectively collect the late photons; the goal of our paper is to test its feasibility to perform TD-DOS measurements at null source-detector separations (NSDS). Approach: In particular, we demonstrate the use of a superconducting nanowire single photon detector (SNSPD) to perform TD-DOS at almost NSDS ( ≈ 150 µ m ) by exploiting the high dynamic range and temporal resolution of the SNSPD to extract late arriving, deep-traveling photons from the burst of early photons. Results: This approach was demonstrated both on Monte Carlo simulations and on phantom measurements, achieving an accuracy in the retrieval of the water spectrum of better than 15%, spanning almost two decades of absorption change in the 700- to 1100-nm range. Additionally, we show that, for interstitial measurements at null source-detector distance, the scattering coefficient has a negligible effect on late photons, easing the retrieval of the absorption coefficient. Conclusions: Utilizing the SNSPD, broadband TD-DOS measurements were performed to successfully retrieve the absorption spectra of the liquid phantoms. Although the SNSPD has certain drawbacks for use in a clinical system, it is an emerging field with research progressing rapidly, and this makes the SNSPD a viable option and a good solution for future research in needle guided time-domain interstitial fiber spectroscopy.

Comparison of cerebral metabolic rate of oxygen, blood flow, and bispectral index under general anesthesia.

Significance: The optical measurement of cerebral oxygen metabolism was evaluated. Aim: Compare optically derived cerebral signals to the electroencephalographic bispectral index (BIS) sensors to monitor propofol-induced anesthesia during surgery. Approach: Relative cerebral metabolic rate of oxygen ( rCMRO 2 ) and blood flow (rCBF) were measured by time-resolved and diffuse correlation spectroscopies. Changes were tested against the relative BIS (rBIS) ones. The synchronism in the changes was also assessed by the R-Pearson correlation. Results: In 23 measurements, optically derived signals showed significant changes in agreement with rBIS: during propofol induction, rBIS decreased by 67% [interquartile ranges (IQR) 62% to 71%], rCMRO 2 by 33% (IQR 18% to 46%), and rCBF by 28% (IQR 10% to 37%). During recovery, a significant increase was observed for rBIS (48%, IQR 38% to 55%), rCMRO 2 (29%, IQR 17% to 39%), and rCBF (30%, IQR 10% to 44%). The significance and direction of the changes subject-by-subject were tested: the coupling between the rBIS, rCMRO 2 , and rCBF was witnessed in the majority of the cases (14/18 and 12/18 for rCBF and 19/21 and 13/18 for rCMRO 2 in the initial and final part, respectively). These changes were also correlated in time ( R > 0.69 to R = 1 , p - values < 0.05 ). Conclusions: Optics can reliably monitor rCMRO 2 in such conditions.

Influence of Tumor Volume on the Fluence Rate Within Human Breast Model Using Continuous-Wave Diffuse Optical Imaging: A Simulation Study.

Objective: This article investigates the effect of varying breast tumor size on the fluence rate distribution within a breast model during the diffuse optical imaging procedure. Background: Early detection of breast cancer is of significant importance owing to its wide spread among women worldwide. Mastectomy surgery became very common due to the late detection of breast cancers by the conventional diagnostic methods such as X-ray mammography and magnetic resonance imaging. On the contrary, optical imaging techniques provide a safe and more sensitive methodology, which is suitable for the early detection criteria. Methods: The implementation was performed based on simulating multiple detectors placed on the outer surface of a human breast model to compute the optical fluence rate after probing the breast (normal and different tumor sizes) with laser irradiation. Different laser wavelengths ranging from the red to near-infrared rays spectral range were examined to determine the optimum fluence rate that shows the highest capability to differentiate between normal and cancerous breasts. A three-dimensional breast model was created using the COMSOL multiphysics package where the optical fluence rate was estimated based on the finite-element solution of the diffusion equation. Results: To evaluate the efficiency of the suggested technique for identifying cancers and discriminate them from normal breast at various wavelengths (600-1000 nm) and several tumor sizes. Conclusions: The obtained results reveal different fluence rate distributions in the breast with different radius tumors, especially at 600 nm due to the significant differences in the scattering coefficient between malignancies and healthy tissue.

Functional Near-Infrared Spectroscopy as a Personalized Digital Healthcare Tool for Brain Monitoring.

The sustained growth of digital healthcare in the field of neurology relies on portable and cost-effective brain monitoring tools that can accurately monitor brain function in real time. Functional near-infrared spectroscopy (fNIRS) is one such tool that has become popular among researchers and clinicians as a practical alternative to functional magnetic resonance imaging, and as a complementary tool to modalities such as electroencephalography. This review covers the contribution of fNIRS to the personalized goals of digital healthcare in neurology by identifying two major trends that drive current fNIRS research. The first major trend is multimodal monitoring using fNIRS, which allows clinicians to access more data that will help them to understand the interconnection between the cerebral hemodynamics and other physiological phenomena in patients. This allows clinicians to make an overall assessment of physical health to obtain a more-detailed and individualized diagnosis. The second major trend is that fNIRS research is being conducted with naturalistic experimental paradigms that involve multisensory stimulation in familiar settings. Cerebral monitoring of multisensory stimulation during dynamic activities or within virtual reality helps to understand the complex brain activities that occur in everyday life. Finally, the scope of future fNIRS studies is discussed to facilitate more-accurate assessments of brain activation and the wider clinical acceptance of fNIRS as a medical device for digital healthcare.