We used the effect of temperature on the localized reflectance of human skin to assess the role of noise sources on the correlation between temperature-induced fractional change in optical density of human skin (ODT) and blood glucose concentration [BG]. Two temperature-controlled optical probes at 30°C contacted the skin, one was then cooled by –10°C; the other was heated by +10°C. ODT upon cooling or heating was correlated with capillary [BG] of diabetic volunteers over a period of three days. Calibration models in the first two days were used to predict [BG] in the third day. We examined the conditions where the correlation coefficient (R2) for predicting [BG] in a third day ranked higher than R2 values resulting from fitting permutations of randomized [BG] to the same ODT values. It was possible to establish a four-term linear regression correlation between ODT upon cooling and [BG] with a correlation coefficient higher than that of an established noise threshold in diabetic patients that were mostly females with less than 20 years of diabetes duration. The ability to predict [BG] values with a correlation coefficient above biological and body-interface noise varied between the cases of cooling and heating.
We designed a dual-sensor instrument for measuring optical signals from the arms of human volunteers. The instrument had two temperature-controlled localized reflectance optical probes. Each probe had one illumination fiber and four detection fibers at different source-detector distances. The two probes were maintained at 30 °C. Thirty seconds after contact with the skin one was heated and the other was cooled at the same rate. The effect of heating and cooling on the signal was measured and correlated with blood glucose concentration. The measurements were performed 3 to 5 times a day for each volunteer over the span of three weeks. The data points from the first two weeks were used to establish a calibration model for each volunteer, which was used to predict glucose values from the third week optical data. Successftil calibration was possible for two of the three volunteers.
We used a temperature controlled localized reflectance optical probe to test the effect of distance source detector distance, temperature and wavelength on the calibration of localized reflectance signals versus glucose concentration. Successful calibration models were established. The data suggests that the interplay of source-detector distances, wavelengths and temperature may lead to selecting a defined subcutaneous volume, where the signal can correlate better with glucose.
We studied the temperature-controlled localized reflectance of the dorsal arm of diabetic and non-diabetic subjects. The thermo-optical response of human skin at small source-detector distances is related to the diabetic status of the subject. It was possible to segregate diabetic from non-diabetic data based on the values of μa, μs' and δ; and the change in their values (Δμa, Δμs', and Δδ) as a function of temperature. The segregation into diabetic and non-diabetic based on the thermal response of μa, μs' is consistent with cross-linking of vascular and tissue proteins by excess glucose during frequent hyperglycemic episodes.
We observed a difference in the thermal response of localized reflectance signal of human skin between type-2 diabetic and non-diabetic volunteers. We investigated the use of this thermo-optical behavior as a basis for a non-invasive method for the determination of the diabetic status of a subject. We used a two-site temperature differential method, which is predicated upon the measurement of localized reflectance from two areas on the surface of the skin, each of these areas is subjected to a different thermal perturbation. The response of skin localized reflectance to temperature was measured and used in a classification algorithm. We used a discriminant function to classify subjects as diabetics or non-diabetics. In a prediction set of 24 non-invasive tests collected from 6 diabetics and 6 non-diabetics, the sensitivity ranged between 73% and 100%, and the specificity ranged between 75% and 100%, depending on the thermal conditions and probe-skin contact time. The difference in thermo-optical response of the skin of the two groups may be explained in terms of difference in response of cutaneous microcirculation to temperature, which is manifested as a difference in the near infrared light absorption and scattering. Another factor is the difference in the temperature response of the scattering coefficient between the two groups, which may be caused by cutaneous structural differences induced by non-enzymatic glycation of skin protein fibers, and/or by the difference in blood cell aggregation.
It is possible to delineate the contribution of surface layers from the bulk optical properties of the medium by using selective adjacent distances of the detection fibers in an optical probe with small source-detectors distances. Using an optical probe with small source-detector separation, the measurement at r < 0.8 mm carries more information about the surface layers, the measurement at greater r > 1.4 mm is dominated by optical properties of deeper layers and is less sensitive to differences in surface optical properties.
KEYWORDS: Skin, Temperature metrology, Scattering, In vivo imaging, Absorption, Refractive index, Human subjects, Tissue optics, Diffuse reflectance spectroscopy, Optical fibers
We examined the effect of temperature change on the diffuse reflectance of the skin. The optical probe consists of several optical fibers located at the center of a thermal electric device, which controls the temperature at the surface of the skin in contact. Measured light reflectance profile between 0.4-1.9 mm was fitted to a mathematical model obtained by Monte Carlo simulation, and absorption and scattering coefficients were estimated. The reduced scattering coefficient of the forearms consistently showed a positive relationship with temperature between 22 and 42 degree(s)C. This dependency was reversible without apparent delay. The same effect was observed on ex vivo pigskin. It is possible to explain the positive instantaneous dependency of scattering on temperature by the change of the refractive index of intercellular fluid. The scattering coefficient of the subcutaneous fat of pigskin showed a negative dependence on temperature. This negative dependency of scattering can be attributed to a phase change as a function of temperature. The absorption coefficient in vivo also increased with temperature from 22 to 42 degree(s)C. But the change was not immediately reversible after temperature reached 40 degree(s)C. This relationship was similar to the nonlinear increase in blood perfusion observed in laser Doppler measurements.
We conducted visible/near infrared optical measurements on the forearm of human subjects using a commercial diffuse reflectance spectrophotometer, and a breadboard temperature- controlled localized reflectance tissue photometer. Calibration relationships were established between skin reflectance signal and reference blood hemoglobin (Hb) concentration, or hematocrit values (Hct). These were then used to predict Hb and Hct values from optical measurement in a cross validation analysis. Different linear least- squares models for the prediction of Hb and Hct are presented and shows the ability to predict both. It was possible to screen prospective blood donors with low Hb concentration. It was possible to predict anemic subjects in the limited prospective blood donor population.
We describe a non-invasive method for the determination of optical parameters of highly scattering media, such as biological tissue. An advantage of this method is that it does not rely on diffusion theory, thus it is applicable to strongly absorbing media and at small source-detector separations. Monte Carlo simulations and phantom measurements are used to illustrate the achievable accuracy of the system. The method was applied to non-invasive in- vivo tracking of haemoglobin concentration in biological tissue. The results correlated well to clinically determined Hb concentrations.
We present a multivariate fitting method to determine the absorption and reduced scattering coefficients of turbid biological tissue, from diffuse reflectance spatial distribution in the range of 0.4 to 2mm. This range of distance is about 0.5 to 5 times the reduced mean free path for most of the optical parameters we measure. Monte Carlo simulation provides numerical estimates of reflectance profiles for a limited set of optical parameter combinations. This forms a Òsampling gridÓ of the forward mapping from the two dimensional space of absorption and scattering coefficients to the reflectance profiles represented by N values. Through interpolation and least square fitting, in the inverse process the optical parameters can be determined with finer resolution than the sampling grid. We present a few strategies to search for the least square fit, including one step and iterative refinement. The precision of absorption and scattering coefficients is affected by the number of detection points, measurement errors, forward mapping grid points, and Monte Carlo simulation statistical errors. There is a trade-off between the resolution of optical coefficients and computation time or memory. Examples of tissue phantoms and in vivo skin optical property determination are presented.
Analysis of white blood cell flow cytometry light scatter data remains a challenging problem. Conventional methods for analyzing flow cytometry data use 2D scatterplots of multidimensional data. We have developed an automated method to locate and characterize clusters within WBC data. Our method uses the full dimensionality of the data and employs the recursive application of a two-step algorithm. Input to the first step is a dataset of cellular events divided into two assumed clusters. A clustering algorithm iteratively refines these initial clusters. This algorithm is based upon an extension of the k-means clustering algorithm. If two populations are confirmed, the data for each cluster are passed, in turn, to the second step, a splitting algorithm. This algorithm determines the potential for further subdivision of the data. When this potential exists, an approximate division of the data is made. These new subclusters are passed as input to step one and the process is repeated. The process terminates when either the clustering algorithm converges to a single population or when the splitting algorithm finds no potential for further subdivisions. Using the full dimensionality of the data, our method can characterize clusters within WBC data even in cases where these clusters overlap in the standard 2D scatterplots.
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