Near-infrared (NIR) spectroscopic measurement of blood and tissue chemistry often requires a large set of subject data
for training a prediction model. We have previously developed the principal component analysis loading correction
(PCALC) method to correct for subject related spectral variations. In this study we tested the concept of developing
PCALC factors from simulated spectra. Thirty, two-layer solid phantoms were made with 5 ink concentrations (0.004%-
0.02%), 2 μs' levels, and 3 fat thicknesses. Spectra were collected in reflectance mode and converted to absorbance by
referencing to a 99% reflectance standard. Spectra (5733) were simulated using Kienle's two-layer turbid media model
encompassing the range of parameters used in the phantoms. PCALC factors were generated from the simulated spectra
at one ink concentration. Simulated spectra were corrected with the PCALC factors and a PLS model was developed to
predict ink concentration from spectra. The best-matched simulated spectrum was identified for each measured phantom
spectrum. These best-matched simulated spectra were corrected with the PCALC factors derived from the simulated
spectra set, and they were used in the PLS model to predict ink concentrations. The ink concentrations were predicted
with an R2=0.897, and an estimated error (RMSEP) of 0.0037%. This study demonstrated the feasibility of using
simulated spectra to correct for inter-subject spectral differences and accurately determine analyte concentrations in
turbid media.
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.
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.
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).
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 present a nonflowing laser light scattering method for automatically counting and classifying blood cells. A linear charge-coupled device (CCD) and a silicon photoelectric cell (which is placed behind a pinhole plate on the CCD) form a double-detector structure: the CCD is used to detect the scattered light intensity distribution of the blood cells and the silicon photoelectric cell to complete the focusing process. An isotropic sphere, with relative refractivity near 1, is used to model the blood cell. Mie theory is used to describe the scattering of white blood cells and platelets, and anomalous diffraction, red blood cells. To obtain the size distribution of blood cells from their scattered light intensity distribution, the nonnegative constraint least-squares (NNLS) method combined with the Powell method and the precision punishment method are used. Both numerical simulation and experimental results are presented. This method can be used not only to measure the mean and the distribution of red blood cell size, but also to divide the white blood cells into three classes: lymphocytes, middle-sized cells, and neutrocytes. The experimental results show a linear relationship between the blood cell (both white and red blood cells) concentration and the scattered light intensity, and therefore, the number of blood cells in a unit volume can be determined from this relationship
A portable transceiver for indoor wireless link that employs a transmitter of eye-safe infrared light-emitting diodes and a receiver of photodiode arrays with multichannel transimpedance-summer architecture is presented. The transmitter can attain a wide field of view (up to 55 deg half-angle) and high speed (up to 35 MHz) to support different intensity modulation schemes. The receiver can receive signals with a bit error rate (BER) of 10–4 at a plane of 2 m away from the transmitter, even at a point ±50 deg off the transmitter's vertical axis. The bit rate of the transceiver can achieve up to 40 Mbit/s in an indoor nondirected infrared wireless link, and can be extended to 100 Mbit/s when light-emitting diodes (LEDs) with higher cut frequency are used. The system is able to transmit real-time uncompressed video 320×240 in frame size.
This paper describes a software system currently being developed at Clemson University in which a client provides recently obtained data to a remote server running a compute-intensive algorithm. To improve performance and speed up delivery of the results, the server distributes the data among multiple sub-server processors and assembles partial output from each processor into a coherent whole before sending the final results to the client. To demonstrate the
capabilities of the system, a specific application is presented in this paper: a fluorescence image reconstruction system for breast cancer detection. An experimental instrument optically scans the patient’s breast and generates some files of experimental data which are then sent to the server via the web. The data is processed by the numerical finite-element based algorithm running in parallel on a server and several sub-servers at Clemson. The algorithm is based on a set of coupled diffusion equations which are used to describe the propagation of excitation and fluorescent emission light in multiply scattering media (such as a breast). The algorithm reconstructs the fluorescence image of the breast in parallel. The resulting fluorescence lifetime and quantum yield mapping data can be sent back to the doctor for image display and analysis. This paper describes the numerical algorithm briefly and the software system which uses Java servlets to collect the data from the client and remote method invocation (Java RMI) to distribute the data to multiple processors.
The output of the numerical algorithm, combined with the corresponding finite element mesh information, are input into
a mathematical software package called Matlab which is used to produce the final images. Experiments are performed using indocyanine green (ICG) dye and tissue-like phantoms in both single- and multi-target configurations. Phantom experimental results of both lifetime and quantum yield are shown in this paper. Future work includes a refinement of the algorithm to incorporate adaptive mesh techniques. The expectation is that such techniques will improve the accuracy of the reconstructed images.
The reconstruction of fluorescence lifetime distributions in heterogeneous turbid media and tumor-bearing animals are experimentally demonstrated by frequency-domain measurements. A set of coupled diffusion equations are used to describe the propagation of excitation and fluorescent emission light in multiply scattering media. A finite element based reconstruction algorithm combined with Marquardt and Tikhonov regularization methods are used to obtain the fluorescence images. The experimental set-up is an automatic multi-channel frequency-domain system. 16 sources and 16 detectors are used. Experiments are performed using indocyanine green (ICG) and 3,3'-diethylthiatricarbocyanine iodide (DTTCI) in tissue-like phantoms of both single- and multi-target configurations with considerations of perfect and imperfect uptake of fluorescence dyes in the scattering media. ICG are used in tumor-bearing animal studies. Our results show that the fluorescence lifetime image of the heterogeneities within a circular surrounding medium and in-vivo tissue can be reconstructed successfully.
A pilot clinical study on ten female volunteers is reported using our multi-channel frequency-domain optical imager. Seven of these patients were previously identified with tumors or microcalcifications by x-ray and/or ultrasound mammography. These tumors had a size between 5 and 38 mm. Using our optical imager and reconstruction methods we were able to detect all tumors that had been identified by x-ray and/or ultrasound mammography. The quantification of our improved scattering images shows a clear differentiation between benign and malignant tumors. A comparison between our results and x-ray/ultrasound results is also given.
A new non-flowing laser light scattering method for counting and classifying blood cells is presented. A linear charge- coupled device with 1024 elements is used to detect the scattered light intensity distribution of the blood cells. A pinhole plate is combined with the CCD to compete the focusing of the measurement system. An isotropic sphere is used to simulate the blood cell. Mie theory is used to describe the scattering of blood cells. In order to inverse the size distribution of blood cells from their scattered light intensity distribution, Powell method combined with precision punishment method is used as a dependent model method for measurement red blood cells and blood plates. Non-negative constraint least square method combined with Powell method and precision punishment method is used as an independent model for measuring white blood cells. The size distributions of white blood cells and red blood cells, and the mean diameter of red blood cells are measured by this method. White blood cells can be divided into three classes: lymphocytes, middle-sized cells and neutrocytes according to their sizes. And the number of blood cells in unit volume can also be measured by the linear dependence of blood cells concentration on scattered light intensity.
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