It is estimated that 750,000 cases of severe sepsis occur in the United States
annually, at least 225,000 of which are fatal, resulting in significant utilization of
healthcare resources and expenses. Significant progress in the understanding of
pathophysiology and treatment of this condition has been made lately. Among the newer
treatment strategies for critically ill patients are the administration of early goal directed
therapy, and Recombinant Human Activated Protein C (Drotrecogrin alfa (activated)
[DTAA]) for severe sepsis. However, mortality remains unacceptably high.
Muscle pH is an important indicator of inadequate blood flow and available oxygen. Muscle pH can be used to triage and help treat trauma victims and indicate poor peripheral blood flow in diabetic patients. Muscle pH can also be used to indicate exercise intensity and fatigue. We have developed methods to non-invasively measure muscle pH using Near-Infrared Spectroscopy (NIRS) and Partial Least Squares (PLS) analysis. A multi-subject PLS model correlating near infrared tissue spectra, acquired from healthy subjects during repetitive hand-grip exercise, to invasive tissue pH measurements, has been developed and validated. Subject related variations in the spectral signal; impede the development of viable multi-subject model. Within-subject variations in tissue NIR spectra often result from uncontrolled motion or blood volume changes during exercise, while subject-to-subject variations arise from differences in skin pigmentation and the fat layer thickness. We have developed signal processing techniques to account for these mitigating factors. By incorporating this signal processing techniques with PLS calibration, we can generate a pH model that has a relative standard error of prediction of 1.7%
The application of multivariate calibration models, specifically those using partial least squares (PLS) regression to relate near infrared (NIR) spectral data to analyte concentrations, relies upon accurate knowledge of the concentrations during model building. In a physiologic system, such as human skeletal muscle, these concentrations can be measured using invasive sensors which may have material properties that limit diffusion of analytes to the sensing chemistry, thus taking several minutes to fully respond to an analyte change which actually occurs in seconds. This results in a poor time correlation between reference measurements of analyte concentrations and spectral data, which in turn degrades the performance of the PLS model. We mathematically modeled the response of an invasive sensor measurement and used this response to develop a filter to time-match the raw NIR spectra before building the PLS model. PLS models for interstitial pH in exercising human flexor digitorum profundus muscle were developed with and without the time-matching filter. In a single exercising subject, root mean square error of prediction (RMSEP) = 0.05 pH units and r2 = 0.39 without filtering, but improved to RMSEP = 0.02 pH units with r2 = 0.91 after the time-matching filter was implemented. The time-matching filter was shown to be effective in improving model performance when spectral response is more rapid than the invasive sensor reference measurement.
In order to measure muscle physiological parameters such as pH and oxygen partial pressure (PO2) by continuous wave (CW) diffuse reflectance near-infrared spectroscopy (NIRS), light must penetrate through skin and subcutaneous fat layers overlying muscle. In this study, the effect of skin and subcutaneous fat layer and on the spatial sensitivity profile of CW diffuse reflectance near-infrared spectra is investigated through Monte Carlo simulations. The simulation model uses a semi-infinite medium consisting of skin, fat and muscle. The optical properties of each layer are taken from the reported optical data at 750 nm. The skin color is either Caucasian or Negroid and the fat thickness is varied from 0 ~ 20 mm. The spatial sensitivity profile, penetration depth, and sensitivity ratio as functions of optical fiber source-detector separation (SD, 2.5 mm, 5.0 mm, 10.0 mm, 20.0 mm, 30.0 mm and 40.0 mm), skin color and fat thicknesses are predicted by the simulations. It is shown that skin color only slightly influenced the spatial sensitivity profile, while the presence of the fat layer greatly decreased the detector sensitivity. It is also shown that probes with longer SD separations can detect light from deeper inside the medium. The simulation results are used to design a fiber optic probe which ensures that enough light is propagated inside the muscle in NIRS measurement on a leg with a fat layer of normal thickness.
KEYWORDS: Diffuse reflectance spectroscopy, Optical properties, Monte Carlo methods, Absorption, Scattering, Blood, Near infrared spectroscopy, Photons, Tissue optics, Refractive index
Continuous wave near-IR spectroscopy (CW-NIRS) has been increasingly applied for the noninvasive, in vivo measurement of tissue and blood chemistry. It is hypothesized that there is a quantifiable relationship between fat thickness and near infrared diffuse reflectance spectra at all wavelengths, and this relationship can be used to remove the spectral influence of the overlying fat layer from the muscle spectrum. The hypothesis was investigated at a single wavelength using Monte Carlo simulations of a two-layer structure and with phantom experiments. The influence of a range of optical coefficients (absorption and reduced scattering) for fat and muscle over the known range of human physiological values was also investigated. A polynomial relationship was established between the fat thickness and the detected diffuse reflectance. It is also shown that the optical properties of the muscle and fat layers influence this relationship under certain conditions. Subject-to-subject variation in the fat optical coefficients and thickness can be ignored if the fat thickness is less than 5 mm, such as on the forearm. If NIRS measurement is to be performed on an anatomical region with a thicker fat layer, a spectral correction for fat will be needed to account for its thickness and the variation in optical coefficients for both the fat and the muscle layers.
We have previously demonstrated the correlation of continuous-wave near infrared (CW-NIR) tissue measurements, to blood and tissue metabolic parameters using Partial Least Squares (PLS) regression. The practical use of this non-invasive measurement technique depends on the transfer of PLS calibration models from a single calibration unit to multiple secondary units. Variations in the spectral characteristics of the optical components across multiple units result in marked differences in the spectral output, preventing the direct transfer of parameter models from one unit to another. Consequently, we have developed a method for standardizing the spectral output across units that utilizes physical, traceable, reference materials for aligning the wavelength and intensity axes to fixed values, followed by spectral normalization via Standard Normal Variate transformation. The approach employed in this study adjusts the slope and bias differences in the optical spectra across multiple units, without the loss of useful information needed for parameter estimation. In this study, phantoms containing Agar, intralipid and lyophilized human hemoglobin (met-hemoglobin) were used to mimic human tissue. Using PLS regression, a hemoglobin calibration model was developed on the tissue-like phantoms on a prototype of the portable NIR medical monitor. The calibration model was successfully transferred to a second, distinctly different system. The Root Mean Squared Error of Prediction of met-hemoglobin in the phantom samples measured in the second system, improved from 4.94g/dl to 1.15g/dl after the standardization procedure. This compares favorably the PLS model error on the primary instrument (0.94g/dl).
A visible-near IR (500-1,000nm) fiber optic sensor is under development that is intended to non-invasively assess muscle metabolism through the measurement of tissue pH and oxygen partial pressure. These parameters are calculated from the spectra of hemoglobin and myoglobin in muscle. The sensor consists of transmit (illumination) fibers and receive (detection) fibers that are coupled to a spectrometer. Light from the probe must penetrate below the surface of the skin and into a 5-10mm thick layer of muscle. A study was conducted to quantify the relationship between transmit and receive fiber separation and sensor penetration depth below the surface of the skin. A liquid phantom was created to replicate the absorption (μa) and reduced scatter coefficient (μs') profiles typically found in human blood and tissue. The phantom consisted of a solution of Intralipid and India ink in the appropriate concentrations to achieve desired reduced scatter coefficient and absorption profiles. The reduced scatter coefficient of the liquid phantom was achieved to an accuracy of +/-10% compared to previously published data. A fixed illumination fiber and translatable detector fiber were placed in the liquid phantom, and the fiber separation was varied from 3-40mm. Values of μa and μs' varied from 0.03-0.40 cm-1 and 5.0-15.0 cm-1 respectively. Results from the experiment demonstrate a strong correlation between penetration depth and fiber separation. Additionally, it was found that penetration depth was not substantially influenced by absorption and scatter concentration. As signal-to-noise is an important parameter in many non-invasive biomedical applications, the relative signal as a function of fiber separation was determined to follow an exponential relationship.
Rapid quantitative imaging of chemical species is an important tool for investigating heterogenous mixtures, such as laminated plastics, biological samples and vapor plumes. Using traditional spectroscopic methods requires difficult computations on very large data sets. By embedding a spectral pattern that corresponds to a target analyte in an interference filter in a beamsplitter arrangement; the chemical information in an image can be obtained rapidly and with a minimal amount of computation. A candidate filter design that is tolerant to the angles present in an imaging arrangement is evaluated in near-infrared spectral region for an organic analyte and an interferent.
Quantitative multivariate spectroscopic methods seek spectral patterns that correspond to analyte concentrations even in the presence of interferents.By embedding a spectral pattern that corresponds to a target analyte in an interference filter in a beamsplitter arrangement;bulky and complex instrumentation can be eliminated with the goal of producing a field-portable instrument.A candidate filter design for an rganic analyte,of military interest,and an interferent is evaluated.
Multivariate Optical Computing (MOC) devices have the potential of greatly simplifying as well as reducing the cost of applying the mathematics of multivariate regression to problems of chemical analysis in the real world. These devices utilize special optical interference coatings known as multivariate optical elements (MOEs) that are encoded with pre-determined spectroscopic patterns to selectively quantify a chemical species of interest in the presence of other interfering species. A T-format prototype of the first optical computing device is presented utilizing a multilayer MOE consisting of alternating layers of two metal oxide films (Nb2O5 and SiO2) on a BK-7 glass substrate. The device was tested by using it to quantify copper uroporphyrin in a quaternary mixture consisting of uroporphyrin (freebase), tin uroporphyrin, nickel uroporphyrin, and copper uroporphyrin. A standard error of prediction (SEP) of 0.86(mu) M was obtained for copper uroporphyrin.
A new algorithm for the design of optical computing filters for chemical analysis otherwise known as Multivariate Optical Elements (MOEs), is described. The approach is based on the nonlinear correlation of the MOE layer thicknesses to the standard error in sample prediction for the chemical species of interest using a modified version ofthe Gauss-Newton nonlinear optimization algorithm. The design algorithm can either be initialized by random layer thicknesses or by a pre-existing design. The algorithm has been successfully tested by using it to design a MOE for the determination of copper uroporphynn in a quaternary mixture of uroporphyrin (freebase), nickel uroporphyrin, copper uroporphynn, and tin uroporphyrin.
12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.
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