Time-domain NIRS system based on supercontinuum light source and multi-wavelength detection: validation for tissue oxygenation studies

Minimum spectral resolution for continuous-wave hyperspectral near-infrared tissue spectroscopy

Significance

Continuous-wave hyperspectral near-infrared spectroscopy (h-NIRS) is a reliable and cost-effective technique for noninvasive monitoring of tissue blood content and oxygenation at the point-of-care; however, most h-NIRS devices are based on expensive custom-built spectrometers. For widespread adoption, low-cost, miniature, off-the-shelf spectrometers are needed. To guide the development of such spectrometers, a standard for spectral resolution must first be defined.

Aim

We aim to identify the minimum spectral resolution needed for h-NIRS devices to accurately measure oxy- and deoxy-hemoglobin (HbO and Hb) concentrations in tissue.

Approach

h-NIRS measurements were acquired from fully oxygenated and deoxygenated blood-lipid phantoms at 13 spectral resolutions. Data for other oxygenation levels were simulated using NIRFAST. HbO and Hb concentrations were estimated at each resolution and compared with the ground truth hemoglobin concentration.

Results

The concentration of Hb was estimated with high accuracy for resolutions up to 10 nm, whereas HbO estimates were more variable. For both chromophores, the accuracy of the estimation gradually decreased with resolutions beyond 10 nm.

Conclusions

Spectral resolutions up to 10 nm can be used for h-NIRS without compromising the accuracy of estimating tissue blood content and oxygenation.

Brain Abnormalities During Self-Referential Task in Subthreshold Depression: A Near-Infrared Spectroscopy Study

Background and aim Subthreshold depression is a potential risk factor for major depressive disorder. Although the neurobiological mechanism underlying major depressive disorder is well-established, the mechanism underlying subthreshold depression has not yet been fully elucidated. We investigated the characteristics of brain abnormalities in participants with subthreshold depression using near-infrared spectroscopy (NIRS) owing to its portability. Methods A total of 53 college students were recruited, and all participants performed three tasks: self-referential task (SRT), verbal fluency task (VFT), and category fluency task (CFT). Hemodynamic changes in the prefrontal cortex (PFC) during the tasks were also measured using NIRS. A generalized linear model was employed for the NIRS data analysis. Subsequently, we evaluated the relationship between depressive severity and NIRS data during task performance. Results Our analysis revealed a positive correlation between hemodynamic changes in the right PFC during the SRT and depression severity (SRTL_coeff: 0.12), suggesting that increased activation in this region may be associated with higher levels of depressive severity. In contrast, hemodynamic changes in the left PFC during the SRT did not significantly influence the severity of depression. Additionally, hemodynamic changes during the VFT and CFT did not significantly influence the severity of depression. Conclusions Hyperactivation of the right PFC, which is a characteristic of subthreshold depression, may cause a negative bias, leading to high sensitivity to negative stimuli. These results provide novel insights into the neural mechanism of subthreshold depression and highlight the utility of NIRS for evaluating brain function related to negative bias in the right PFC.

Validation of multispectral imaging-based tissue oxygen saturation detecting system for wound healing recognition on open wounds

Significance

The multispectral imaging-based tissue oxygen saturation detecting (TOSD) system offers deeper penetration ( ∼ 2 to 3 mm) and comprehensive tissue oxygen saturation (StO 2 ) assessment and recognizes the wound healing phase at a low cost and computational requirement. The potential for miniaturization and integration of TOSD into telemedicine platforms could revolutionize wound care in the challenging pandemic era.

Aim

We aim to validate TOSD's application in detectingStO 2 by comparing it with wound closure rates and laser speckle contrast imaging (LSCI), demonstrating TOSD's ability to recognize the wound healing process.

Approach

Utilizing a murine model, we compared TOSD with digital photography and LSCI for comprehensive wound observation in five mice with 6-mm back wounds. Sequential biochemical analysis of wound discharge was investigated for the translational relevance of TOSD.

Results

TOSD demonstrated constant signals on unwounded skin with differential changes on open wounds. Compared with LSCI, TOSD provides indicative recognition of the proliferative phase during wound healing, with a higher correlation coefficient to wound closure rate (TOSD: 0.58; LSCI: 0.44).StO 2 detected by TOSD was further correlated with proliferative phase angiogenesis markers.

Conclusions

Our findings suggest TOSD's enhanced utility in wound management protocols, evaluating clinical staging and therapeutic outcomes. By offering a noncontact, convenient monitoring tool, TOSD can be applied to telemedicine, aiming to advance wound care and regeneration, potentially improving patient outcomes and reducing healthcare costs associated with chronic wounds.

Leveraging 3D convolutional neural network and 3D visible-near-infrared multimodal imaging for enhanced contactless oximetry

Significance

Monitoring oxygen saturation (SpO 2 ) is important in healthcare, especially for diagnosing and managing pulmonary diseases. Non-contact approaches broaden the potential applications ofSpO 2 measurement by better hygiene, comfort, and capability for long-term monitoring. However, existing studies often encounter challenges such as lower signal-to-noise ratios and stringent environmental conditions.

Aim

We aim to develop and validate a contactlessSpO 2 measurement approach using 3D convolutional neural networks (3D CNN) and 3D visible-near-infrared (VIS-NIR) multimodal imaging, to offer a convenient, accurate, and robust alternative forSpO 2 monitoring.

Approach

We propose an approach that utilizes a 3D VIS-NIR multimodal camera system to capture facial videos, in whichSpO 2 is estimated through 3D CNN by simultaneously extracting spatial and temporal features. Our approach includes registration of multimodal images, tracking of the 3D region of interest, spatial and temporal preprocessing, and 3D CNN-based feature extraction andSpO 2 regression.

Results

In a breath-holding experiment involving 23 healthy participants, we obtained multimodal video data with referenceSpO 2 values ranging from 80% to 99% measured by pulse oximeter on the fingertip. The approach achieved a mean absolute error (MAE) of 2.31% and a Pearson correlation coefficient of 0.64 in the experiment, demonstrating good agreement with traditional pulse oximetry. The discrepancy of estimatedSpO 2 values was within 3% of the referenceSpO 2 for ∼ 80 % of all 1-s time points. Besides, in clinical trials involving patients with sleep apnea syndrome, our approach demonstrated robust performance, with an MAE of less than 2% inSpO 2 estimations compared to gold-standard polysomnography.

Conclusions

The proposed approach offers a promising alternative for non-contact oxygen saturation measurement with good sensitivity to desaturation, showing potential for applications in clinical settings.

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.

A Novel Approach to Evaluating Crosstalk for Near-Infrared Spectrometers

Multi-channel and multi-parameter near-infrared spectroscopy (NIRS) has gradually become a new research direction and hot spot due to its ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks for the evaluation of the reliability, sensitivity, stability and response consistency of the NIRS instruments. In this study, a set of test methods (a human blood model test, ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analyzing crosstalk and verifying the reliability of NIRS was conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency of the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for NIRS instrument reliability testing.

HyperTRCSS: A hyperspectral time-resolved compressive sensing spectrometer for depth-sensitive monitoring of cytochrome-c-oxidase and blood oxygenation

Significance

Hyperspectral time-resolved (TR) near-infrared spectroscopy offers the potential to monitor cytochrome-c-oxidase (oxCCO) and blood oxygenation in the adult brain with minimal scalp/skull contamination. We introduce a hyperspectral TR spectrometer that uses compressive sensing to minimize acquisition time without compromising spectral range or resolution and demonstrate oxCCO and blood oxygenation monitoring in deep tissue.

Aim

Develop a hyperspectral TR compressive sensing spectrometer and use it to monitor oxCCO and blood oxygenation in deep tissue.

Approach

Homogeneous tissue-mimicking phantom experiments were conducted to confirm the spectrometer's sensitivity to oxCCO and blood oxygenation. Two-layer phantoms were used to evaluate the spectrometer's sensitivity to oxCCO and blood oxygenation in the bottom layer through a 10 mm thick static top layer.

Results

The spectrometer was sensitive to oxCCO and blood oxygenation changes in the bottom layer of the two-layer phantoms, as confirmed by concomitant measurements acquired directly from the bottom layer. Measures of oxCCO and blood oxygenation by the spectrometer were highly correlated with "gold standard" measures in the homogeneous and two-layer phantom experiments.

Conclusions

The results show that the hyperspectral TR compressive sensing spectrometer is sensitive to changes in oxCCO and blood oxygenation in deep tissue through a thick static top layer.

Two-layered blood-lipid phantom and method to determine absorption and oxygenation employing changes in moments of DTOFs

Near-infrared spectroscopy (NIRS) is an established technique for measuring tissue oxygen saturation (StO₂), which is of high clinical value. For tissues that have layered structures, it is challenging but clinically relevant to obtain StO₂ of the different layers, e.g. brain and scalp. For this aim, we present a new method of data analysis for time-domain NIRS (TD-NIRS) and a new two-layered blood-lipid phantom. The new analysis method enables accurate determination of even large changes of the absorption coefficient (Δµ_a) in multiple layers. By adding Δµ_a to the baseline µ_a, this method provides absolute µ_a and hence StO₂ in multiple layers. The method utilizes (i) changes in statistical moments of the distributions of times of flight of photons (DTOFs), (ii) an analytical solution of the diffusion equation for an N-layered medium, (iii) and the Levenberg-Marquardt algorithm (LMA) to determine Δµ_a in multiple layers from the changes in moments. The method is suitable for NIRS tissue oximetry (relying on µ_a) as well as functional NIRS (fNIRS) applications (relying on Δµ_a). Experiments were conducted on a new phantom, which enabled us to simulate dynamic StO₂ changes in two layers for the first time. Two separate compartments, which mimic superficial and deep layers, hold blood-lipid mixtures that can be deoxygenated (using yeast) and oxygenated (by bubbling oxygen) independently. Simultaneous NIRS measurements can be performed on the two-layered medium (variable superficial layer thickness, L), the deep (homogeneous), and/or the superficial (homogeneous). In two experiments involving ink, we increased the nominal µ_a in one of two compartments from 0.05 to 0.25 cm^-1, L set to 14.5 mm. In three experiments involving blood (L set to 12, 15, or 17 mm), we used a protocol consisting of six deoxygenation cycles. A state-of-the-art multi-wavelength TD-NIRS system measured simultaneously on the two-layered medium, as well as on the deep compartment for a reference. The new method accurately determined µ_a (and hence StO₂) in both compartments. The method is a significant progress in overcoming the contamination from the superficial layer, which is beneficial for NIRS and fNIRS applications, and may improve the determination of StO₂ in the brain from measurements on the head. The advanced phantom may assist in the ongoing effort towards more realistic standardized performance tests in NIRS tissue oximetry. Data and MATLAB codes used in this study were made publicly available.

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model

To evaluate corn quality quickly, the feasibility of near-infrared spectroscopy (NIRS) coupled with chemometrics was analyzed to detect the moisture, oil, protein, and starch content in corn. A backward interval partial least squares (BiPLS)-principal component analysis (PCA)-extreme learning machine (ELM) quantitative analysis model was constructed based on BiPLS in conjunction with PCA and the ELM. The selection of characteristic spectral intervals was accomplished by BiPLS. The best principal components were determined by the prediction residual error sum of squares of Monte Carlo cross validation. In addition, a genetic simulated annealing algorithm was utilized to optimize the parameters of the ELM regression model. The established regression models for moisture, oil, protein, and starch can meet the demand for corn component detection with the prediction determination coefficients of 0.996, 0.990, 0.974, and 0.976; the prediction root means square errors of 0.018, 0.016, 0.067, and 0.109; and the residual prediction deviations of 15.704, 9.741, 6.330, and 6.236, respectively. The results show that the NIRS rapid detection model has higher robustness and accuracy based on the selection of characteristic spectral intervals in conjunction with spectral data dimensionality reduction and nonlinear modeling and can be used as an alternative strategy to detect multiple components in corn rapidly.

Continuous-wave parallel interferometric near-infrared spectroscopy (CW NIRS) with a fast two-dimensional camera

Interferometric near-infrared spectroscopy (iNIRS) is an optical method that noninvasively measures the optical and dynamic properties of the human brain in vivo. However, the original iNIRS technique uses single-mode fibers for light collection, which reduces the detected light throughput. The reduced light throughput is compensated by the relatively long measurement or integration times (∼1 sec), which preclude monitoring of rapid blood flow changes that could be linked to neural activation. Here, we propose parallel interferometric near-infrared spectroscopy (πNIRS) to overcome this limitation. In πNIRS we use multi-mode fibers for light collection and a high-speed, two-dimensional camera for light detection. Each camera pixel acts effectively as a single iNIRS channel. So, the processed signals from each pixel are spatially averaged to reduce the overall integration time. Moreover, interferometric detection provides us with the unique capability of accessing complex information (amplitude and phase) about the light remitted from the sample, which with more than 8000 parallel channels, enabled us to sense the cerebral blood flow with only a 10 msec integration time (∼100x faster than conventional iNIRS). In this report, we have described the theoretical foundations and possible ways to implement πNIRS. Then, we developed a prototype continuous wave (CW) πNIRS system and validated it in liquid phantoms. We used our CW πNIRS to monitor the pulsatile blood flow in a human forearm in vivo. Finally, we demonstrated that CW πNIRS could monitor activation of the prefrontal cortex by recording the change in blood flow in the forehead of the subject while he was reading an unknown text.

Multi-laboratory performance assessment of diffuse optics instruments: the BitMap exercise

SIGNIFICANCE

Multi-laboratory initiatives are essential in performance assessment and standardization-crucial for bringing biophotonics to mature clinical use-to establish protocols and develop reference tissue phantoms that all will allow universal instrument comparison.

AIM

The largest multi-laboratory comparison of performance assessment in near-infrared diffuse optics is presented, involving 28 instruments and 12 institutions on a total of eight experiments based on three consolidated protocols (BIP, MEDPHOT, and NEUROPT) as implemented on three kits of tissue phantoms. A total of 20 synthetic indicators were extracted from the dataset, some of them defined here anew.

APPROACH

The exercise stems from the Innovative Training Network BitMap funded by the European Commission and expanded to include other European laboratories. A large variety of diffuse optics instruments were considered, based on different approaches (time domain/frequency domain/continuous wave), at various stages of maturity and designed for different applications (e.g., oximetry, spectroscopy, and imaging).

RESULTS

This study highlights a substantial difference in hardware performances (e.g., nine decades in responsivity, four decades in dark count rate, and one decade in temporal resolution). Agreement in the estimates of homogeneous optical properties was within 12% of the median value for half of the systems, with a temporal stability of <5  %   over 1 h, and day-to-day reproducibility of <3  %  . Other tests encompassed linearity, crosstalk, uncertainty, and detection of optical inhomogeneities.

CONCLUSIONS

This extensive multi-laboratory exercise provides a detailed assessment of near-infrared Diffuse optical instruments and can be used for reference grading. The dataset-available soon in an open data repository-can be evaluated in multiple ways, for instance, to compare different analysis tools or study the impact of hardware implementations.

Machine learning enabled multiple illumination quantitative optoacoustic oximetry imaging in humans

Optoacoustic (OA) imaging is a promising modality for quantifying blood oxygen saturation (sO2) in various biomedical applications - in diagnosis, monitoring of organ function, or even tumor treatment planning. We present an accurate and practically feasible real-time capable method for quantitative imaging of sO2 based on combining multispectral (MS) and multiple illumination (MI) OA imaging with learned spectral decoloring (LSD). For this purpose we developed a hybrid real-time MI MS OA imaging setup with ultrasound (US) imaging capability; we trained gradient boosting machines on MI spectrally colored absorbed energy spectra generated by generic Monte Carlo simulations and used the trained models to estimate sO2 on real OA measurements. We validated MI-LSD in silico and on in vivo image sequences of radial arteries and accompanying veins of five healthy human volunteers. We compared the performance of the method to prior LSD work and conventional linear unmixing. MI-LSD provided highly accurate results in silico and consistently plausible results in vivo. This preliminary study shows a potentially high applicability of quantitative OA oximetry imaging, using our method.