Noninvasive glucose sensing by transcutaneous Raman spectroscopy.

We present the development of a transcutaneous Raman spectroscopy system and analysis algorithm for noninvasive glucose sensing. The instrument and algorithm were tested in a preclinical study in which a dog model was used. To achieve a robust glucose test system, the blood levels were clamped for periods of up to 45 min. Glucose clamping and rise/fall patterns have been achieved by injecting glucose and insulin into the ear veins of the dog. Venous blood samples were drawn every 5 min and a plasma glucose concentration was obtained and used to maintain the clamps, to build the calibration model, and to evaluate the performance of the system. We evaluated the utility of the simultaneously acquired Raman spectra to be used to determine the plasma glucose values during the 8-h experiment. We obtained prediction errors in the range of ~1.5-2 mM. These were in-line with a best-case theoretical estimate considering the limitations of the signal-to-noise ratio estimates. As expected, the transition regions of the clamp study produced larger predictive errors than the stable regions. This is related to the divergence of the interstitial fluid (ISF) and plasma glucose values during those periods. Two key contributors to error beside the ISF/plasma difference were photobleaching and detector drift. The study demonstrated the potential of Raman spectroscopy in noninvasive applications and provides areas where the technology can be improved in future studies.

Predicting skin flap viability using a new intraoperative tissue oximetry sensor: a feasibility study in pigs.

BACKGROUND: Operations for soft-tissue reconstruction, orthopedic, vascular, and other types of surgery can be complicated by unexpected skin flap necrosis. At present, surgeons utilize subjective clinical judgment and physical findings to estimate the potential for tissue compromise. As the validity of these subjective methods is questionable, there is a need for objective, quantitative tools to determine the risk of flap necrosis during surgery. METHODS: Three 9-month-old Yorkshire pigs were used for the study. Four laterally based random pattern fasciocutaneous flaps were dissected on each animal. After the flaps were elevated, a prototype oximeter (ViOptix Inc., Fremont, CA), was used to measure tissue oxygenation (StO2) at 2 cm intervals along the flaps. Measurements were performed immediately after the flaps were dissected, and again at the same points after they were sutured. The animals were reevaluated 4 days later, and assessed for areas of tissue necrosis. RESULTS: For each flap, StO2 at the base was compared with StO2 at the more distal points. The median delta StO2, as measured immediately after dissection, was -3.9% points for tissue that remained viable and -34.0% points for tissue that became necrotic (p = 0.039). After the flaps were sutured back to the chest wall, the median delta StO2 for tissue that remained viable was -1.7% points versus -24.7% points for tissue that became necrotic (p = 0.006). CONCLUSIONS: This new handheld surface sensor can be used to measure StO2 of skin flaps and may potentially reduce complications associated with unexpected tissue necrosis.

Re-evaluation of model-based light-scattering spectroscopy for tissue spectroscopy.

Model-based light scattering spectroscopy (LSS) seemed a promising technique for in-vivo diagnosis of dysplasia in multiple organs. In the studies, the residual spectrum, the difference between the observed and modeled diffuse reflectance spectra, was attributed to single elastic light scattering from epithelial nuclei, and diagnostic information due to nuclear changes was extracted from it. We show that this picture is incorrect. The actual single scattering signal arising from epithelial nuclei is much smaller than the previously computed residual spectrum, and does not have the wavelength dependence characteristic of Mie scattering. Rather, the residual spectrum largely arises from assuming a uniform hemoglobin distribution. In fact, hemoglobin is packaged in blood vessels, which alters the reflectance. When we include vessel packaging, which accounts for an inhomogeneous hemoglobin distribution, in the diffuse reflectance model, the reflectance is modeled more accurately, greatly reducing the amplitude of the residual spectrum. These findings are verified via numerical estimates based on light propagation and Mie theory, tissue phantom experiments, and analysis of published data measured from Barrett's esophagus. In future studies, vessel packaging should be included in the model of diffuse reflectance and use of model-based LSS should be discontinued.

Intrinsic Raman spectroscopy for quantitative biological spectroscopy part I: theory and simulations.

We present a novel technique, intrinsic Raman spectroscopy (IRS), to correct turbidity-induced Raman spectral distortions, resulting in the intrinsic Raman spectrum that would be observed in the absence of scattering and absorption. We develop an expression relating the observed and intrinsic Raman spectra through diffuse reflectance using the photon migration depiction of light transport. Numerical simulations are employed to validate the theoretical results and study the dependence of this expression on sample size and elastic scattering anisotropy.

Intrinsic Raman spectroscopy for quantitative biological spectroscopy part II: experimental applications.

We demonstrate the effectiveness of intrinsic Raman spectroscopy (IRS) at reducing errors caused by absorption and scattering. Physical tissue models, solutions of varying absorption and scattering coefficients with known concentrations of Raman scatterers, are studied. We show significant improvement in prediction error by implementing IRS to predict concentrations of Raman scatterers using both ordinary least squares regression (OLS) and partial least squares regression (PLS). In particular, we show that IRS provides a robust calibration model that does not increase in error when applied to samples with optical properties outside the range of calibration.

Diagnosing breast cancer using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy.

Using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy, we have developed an algorithm that successfully classifies normal breast tissue, fibrocystic change, fibroadenoma, and infiltrating ductal carcinoma in terms of physically meaningful parameters. We acquire 202 spectra from 104 sites in freshly excised breast biopsies from 17 patients within 30 min of surgical excision. The broadband diffuse reflectance and fluorescence spectra are collected via a portable clinical spectrometer and specially designed optical fiber probe. The diffuse reflectance spectra are fit using modified diffusion theory to extract absorption and scattering tissue parameters. Intrinsic fluorescence spectra are extracted from the combined fluorescence and diffuse reflectance spectra and analyzed using multivariate curve resolution. Spectroscopy results are compared to pathology diagnoses, and diagnostic algorithms are developed based on parameters obtained via logistic regression with cross-validation. The sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy (total efficiency) of the algorithm are 100, 96, 69, 100, and 91%, respectively. All invasive breast cancer specimens are correctly diagnosed. The combination of diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy yields promising results for discrimination of breast cancer from benign breast lesions and warrants a prospective clinical study.

Constrained regularization: hybrid method for multivariate calibration.

We present a hybrid multivariate calibration method, constrained regularization (CR), and demonstrate its utility via numerical simulations and experimental Raman spectra. In this new method, multivariate calibration is treated as an inverse problem in which an optimal balance between model complexity and noise rejection is achieved with the inclusion of prior information in the form of a spectral constraint. A key feature is that the constraint is incorporated in a flexible manner, allowing the minimization algorithm to arrive at the optimal solution. We demonstrate that CR, when used with an appropriate constraint, is superior to methods without prior information, such as partial least-squares, and is less susceptible to spurious correlations. In addition, we show that CR is more robust than methods in which the constraint is rigidly incorporated, such as hybrid linear analysis, when the exact spectrum of the analyte of interest as it appears in the sample is not available. This situation can occur as a result of experimental or sample variations and often arises in complex or turbid samples such as biological tissues.

Determination of uncertainty in parameters extracted from single spectroscopic measurements.

The ability to quantify uncertainty in information extracted from spectroscopic measurements is important in numerous fields. The traditional approach of repetitive measurements may be impractical or impossible in some measurements scenarios, while chi-squared analysis does not provide insight into the sources of uncertainty. As such, a need exists for analytical expressions for estimating uncertainty and, by extension, minimum detectable concentrations or diagnostic parameters, that can be applied to a single noisy measurement. This work builds on established concepts from estimation theory, such as the Cramer-Rao lower bound on estimator covariance, to present an analytical formula for estimating uncertainty expressed as a simple function of measurement noise, signal strength, and spectral overlap. This formalism can be used to evaluate and improve instrument performance, particularly important for rapid-acquisition biomedical spectroscopy systems. We demonstrate the experimental utility of this expression in assessing concentration uncertainties from spectral measurements of aqueous solutions and diagnostic parameter uncertainties extracted from spectral measurements of human artery tissue. The measured uncertainty, calculated from many independent measurements, is found to be in good agreement with the analytical formula applied to a single spectrum. These results are intended to encourage the widespread use of uncertainty analysis in the biomedical optics community.

Moving beyond traditional UV-visible absorption detection: cavity ring-down spectroscopy for HPLC.

We describe the use of liquid-phase continuous-wave cavity ring-down spectroscopy for the detection of an HPLC separation. This technique builds on earlier work by Snyder and Zare using pulsed laser sources and improves upon commercially available UV-visible detectors by a factor of up to 50. The system employs a compact doubled-diode single-mode continuous-wave laser operating at 488 nm and a previously described Brewster's-angle flow cell. Ring-down time constants as long as 5.8 micros were observed with liquid samples in a 0.3-mm path length cell. The baseline noise during an HPLC separation was only 2 x 10(-7) absorbance units (AU) peak to peak, as compared to 1 x 10(-5) AU for a state-of-the-art commercial UV-visible detector.