Scanning interferometric near-infrared spectroscopy.
Ontology highlight
ABSTRACT:
SUBMITTER: Kholiqov O
PROVIDER: S-EPMC9281567 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Sensing and imaging methods based on the dynamic scattering of coherent light (including laser speckle, laser Doppler, diffuse correlation spectroscopy, dynamic light scattering, and diffusing wave spectroscopy) quantify scatterer motion using light intensity fluctuations. The underlying optical field autocorrelation, rather than being measured directly, is typically inferred from the intensity autocorrelation through the Siegert relationship, assuming that the scattered field obeys Gaussian statistics. Here, we demonstrate interferometric near-infrared spectroscopy for measuring the time-of-flight (TOF) resolved field and intensity autocorrelations in turbid media. We find that the Siegert relationship breaks down for short TOFs due to static paths whose optical field does not decorrelate over experimental time scales. We also show that eliminating such paths by polarization gating restores the validity of the Siegert relationship. The unique capability of measuring optical field autocorrelations, as demonstrated here, enables the study of non-Gaussian and non-ergodic light scattering processes. Moreover, direct measurements of field autocorrelations are more efficient than indirect measurements based on intensity autocorrelations. Thus, optical field measurements may improve the quantiffcation of scatterer dynamics with coherent light.
| S-EPMC6205232 | biostudies-literature
Project description:Interferometric near-infrared spectroscopy (iNIRS) is a new technique that measures time-of-flight- (TOF-) resolved autocorrelations in turbid media, enabling simultaneous estimation of optical and dynamical properties. Here, we demonstrate reflectance-mode iNIRS for noninvasive monitoring of a mouse brain in vivo. A method for more precise quantification with less static interference from superficial layers, based on separating static and dynamic components of the optical field autocorrelation, is presented. Absolute values of absorption, reduced scattering, and blood flow index (BFI) are measured, and changes in BFI and absorption are monitored during a hypercapnic challenge. Absorption changes from TOF-resolved iNIRS agree with absorption changes from continuous wave NIRS analysis, based on TOF-integrated light intensity changes, an effective path length, and the modified Beer-Lambert Law. Thus, iNIRS is a promising approach for quantitative and noninvasive monitoring of perfusion and optical properties in vivo.
| S-EPMC5565174 | biostudies-other
Project description:Accurate dry matter determination (DM) in Hass avocados is vital for optimal harvesting and ensuring fruit quality. Predictive models based on NIRS need to capture fruit DM gradient. This work aimed to determine the DM content in Hass avocado whole by NIRS scanning different fruit zones. Spectra were recorded for each zone of the fruit: peduncle (P), equator (E), and base (B). The calibration and validation included fruit from different orchards in two harvest cycles. The results show a DM gradient within the fruit: 24.47% (E), 24.68% (B), and 24.79% (P). The DM gradient was observed within the spectra using the RMSi (root mean square) criterion and PCA. The results show that at least one spectrum per fruit zone was needed to represent the variability within the fruit. The performances of the calibration using the whole set of data were R2: 0.74 and standard error of cross-validation (SECV) = 1.18%. In the validation stage using independent validation sets, the models showed similar performance (R2: 0.75, SECV 1.15%) with low values of the standard error of prediction (SEP): 1.62%. These results demonstrate the potential of near-infrared spectroscopy for high-throughput sorting of avocados based on their commercial quality.
| S-EPMC10490472 | biostudies-literature
Project description:Near-infrared spectroscopy (NiRS) is a relatively new technology of brain imaging with its potential in the assessment of cerebrovascular health only recently discovered. Encouraging early results suggest that NiRS can be used as an inexpensive and portable cerebrovascular health tracking device using a recently proposed pulse relaxation function (PReFx). In this paper, we propose a new NiRS timing index, [Formula: see text], of cerebrovascular health. [Formula: see text] is a novel use of the NiRS technology. [Formula: see text] is motivated by the previously proved relationship of the timing of the reflected wave with vascular resistance and compliance in the context of pressure waveforms. We correlated both [Formula: see text] and PReFx against age, a non-exercise cardiorespiratory fitness (CRF) index, and two existing indices of cerebrovascular health, namely transcranial Doppler (TCD) augmentation index, [Formula: see text], and magnetic resonance imaging (MRI) blood flow pulsatility index, [Formula: see text]. The [Formula: see text] correlations with Age, CRF, [Formula: see text] and [Formula: see text] all are significant, i.e., [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]), respectively. PReFx, however, did not have significant correlations with any of the vascular health factors. The proposed timing index is a reliable indicator of cerebrovascular aging factors in the NiRS waveform.
| S-EPMC8292377 | biostudies-literature
Project description:In this paper, a theory for detection of the absolute concentrations of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) from hemodynamic responses using a bundled-optode configuration in functional near-infrared spectroscopy (fNIRS) is proposed. The proposed method is then applied to the identification of two fingers (i.e., little and thumb) during their flexion and extension. This experiment involves a continuous-wave-type dual-wavelength (760 and 830 nm) fNIRS and five healthy male subjects. The active brain locations of two finger movements are identified based on the analysis of the t- and p-values of the averaged HbOs, which are quite distinctive. Our experimental results, furthermore, revealed that the hemodynamic responses of two-finger movements are different: The mean, peak, and time-to-peak of little finger movements are higher than those of thumb movements. It is noteworthy that the developed method can be extended to 3-dimensional fNIRS imaging.
| S-EPMC5082888 | biostudies-literature
Project description: Not available
| S-EPMC10790886 | biostudies-literature
Project description:ObjectivesWe sought to examine near-infrared spectroscopy (NIRS) imaging findings of aortocoronary saphenous vein grafts (SVGs).BackgroundSVGs are prone to develop atherosclerosis similar to native coronary arteries. They have received little study using NIRS.MethodsWe examined the clinical characteristics and imaging findings from 43 patients who underwent NIRS imaging of 45 SVGs at our institution between 2009 and 2016.ResultsThe mean patient age was 67 ± 7 years and 98% were men, with high prevalence of diabetes mellitus (56%), hypertension (95%), and dyslipidemia (95%). Mean SVG age was 7 ± 7 years, mean SVG lipid core burden index (LCBI) was 53 ± 60 and mean maxLCBI4 mm was 194 ± 234. Twelve SVGs (27%) had lipid core plaques (2 yellow blocks on the block chemogram), with a higher prevalence in SVGs older than 5 years (46% vs. 5%, P = 0.002). Older SVG age was associated with higher LCBI (r = 0.480, P < 0.001) and higher maxLCBI4 mm (r = 0.567, P < 0.001). On univariate analysis, greater annual total cholesterol exposure was associated with higher SVG LCBI (r = 0.30, P = 0.042) and annual LDL-cholesterol and triglyceride exposure were associated with higher SVG maxLCBI4 mm (LDL-C: r = 0.41, P = 0.020; triglycerides: r = 0.36, P = 0.043). On multivariate analysis, the only independent predictor of SVG LCBI and maxLCBI4mm was SVG age. SVG percutaneous coronary intervention was performed in 63% of the patients. An embolic protection device was used in 96% of SVG PCIs. Periprocedural myocardial infarction occurred in one patient.ConclusionsOlder SVG age and greater lipid exposure are associated with higher SVG lipid burden. © 2016 Wiley Periodicals, Inc.
| S-EPMC5316385 | biostudies-literature
Project description:Implantable, near infrared (nIR) fluorescent nanosensors are advantageous for in vivo monitoring of biological analytes since they can be rendered selective for a particular target molecule while utilizing their unique optical properties and the nIR tissue transparency window for information transfer without an internal power source or telemetry. However, basic questions remain regarding the optimal encapsulation platform, geometrical properties, and concentration ranges required for high signal to noise ratio and effective detection through biological tissue. In this work, we systematically explore these variables quantitatively to optimize the performance of such optical nanosensors for biomedical applications. We investigate both alginate and polyethylene glycol (PEG) as model hydrogel systems, encapsulating d(GT)15 ssDNA-wrapped single-walled carbon nanotubes (SWNT) as model fluorescent nanoparticle sensors, responsive to riboflavin. Hydrogel sensors implanted 0.5 mm into thick tissue samples exhibit 50% reduction of initial fluorescence intensity, allowing an optical detection limit of 5.4 mm and 5.1 mm depth in tissue for alginate and PEG gels, respectively, at a SWNT concentration of 10 mg L(-1), and 785 nm laser excitation of 80 mW and 30 s exposure. These findings are supported with in vivo nIR fluorescent imaging of SWNT hydrogels implanted subcutaneously in mice. For the case of SWNT, we find that the alginate system is preferable in terms of emission intensity, sensor response, rheological properties, and shelf life.
| S-EPMC5307332 | biostudies-literature
Project description:BackgroundCerebral hypoperfusion may aggravate neurological damage after cardiac arrest. Near-infrared spectroscopy (NIRS) provides information on cerebral oxygenation but its relevance during post-resuscitation care is undefined. We investigated whether cerebral oxygen saturation (rSO2) measured with NIRS correlates with the serum concentration of neuron-specific enolase (NSE), a marker of neurological injury, and with clinical outcome in out-of-hospital cardiac arrest (OHCA) patients.MethodsWe performed a post hoc analysis of a randomised clinical trial (COMACARE, NCT02698917) comparing two different levels of carbon dioxide, oxygen and arterial pressure after resuscitation from OHCA with ventricular fibrillation as the initial rhythm. We measured rSO2 in 118 OHCA patients with NIRS during the first 36 h of intensive care. We determined the NSE concentrations from serum samples at 48 h after cardiac arrest and assessed neurological outcome with the Cerebral Performance Category (CPC) scale at 6 months. We evaluated the association between rSO2 and serum NSE concentrations and the association between rSO2 and good (CPC 1-2) and poor (CPC 3-5) neurological outcome.ResultsThe median (inter-quartile range (IQR)) NSE concentration at 48 h was 17.5 (13.4-25.0) μg/l in patients with good neurological outcome and 35.2 (22.6-95.8) μg/l in those with poor outcome, p < 0.001. We found no significant correlation between median rSO2 and NSE at 48 h, rs = - 0.08, p = 0.392. The median (IQR) rSO2 during the first 36 h of intensive care was 70.0% (63.5-77.0%) in patients with good outcome and 71.8% (63.3-74.0%) in patients with poor outcome, p = 0.943. There was no significant association between rSO2 over time and neurological outcome. In a binary logistic regression model, rSO2 was not a statistically significant predictor of good neurological outcome (odds ratio 0.99, 95% confidence interval 0.94-1.04, p = 0.635).ConclusionsWe found no association between cerebral oxygenation measured with NIRS and NSE concentrations or outcome in patients resuscitated from OHCA.Trial registrationClinicalTrials.gov, NCT02698917 . Registered on 26 January 2016.
| S-EPMC6518726 | biostudies-literature
Project description:The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
| S-EPMC9784128 | biostudies-literature