Kate L. Bechtel

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.

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.

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.

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 Barretts esophagus. In future studies, vessel packaging should be included in the model of diffuse reflectance and use of model-based LSS should be discontinued.

Noninvasive glucose sensing by transcutaneous Raman spectroscopy

Abstract. 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.

Automated image curvature assessment and correction for high-throughput Raman spectroscopy and microscopy

Raman spectroscopy and microscopy can provide molecular information for complex materials such as biological tissue and cells. In these applications, light-collection throughput is essential for speedy acquisition of high-quality data. To im- prove throughput, two-dimensional detectors and high numerical aperture (NA) optical systems have been employed. However, owing to the out-of-plane diffraction in grating-based dispersive spectrograph, the entrance slit image formed at the detector plane is curved along the vertical direction. Direct vertical binning of individual detector rows without correcting the curvature results in degraded spectral resolution and peak misalignment. We evaluate two software approaches to remove the image cur- vature after high-throughput data acquisition, with the objective to retain instrument spectral resolution and peak accuracy as if a linear-array detector were used. Curvature correction and detection are achieved in two steps: calibration of the image curva- ture using a Raman active material and application of the correction to future curved images. This method has been employed for a high-NA, large CCD Raman spectroscopic system deigned for non-invasive glucose sensing, a medium-NA, medium-size CCD line-scan Raman microscope designed for high-throughput tissue and cellular imaging, and an active-illumination Raman microscope. We show that remarkable improvement in data fidelity can be obtained as assessed by peak misalignment, distri- bution of data variance, and the waveform of principal component spectra. High quality curvature correction is essential for quantitative analysis such as the multivariate calibration, spectral pattern recognition, and peak shift detection based techniques. The software approach is highly flexible for instrument modification.

Intrinsic Raman Spectroscopy Improves Analyte Concentration Measurements in Turbid Media

We present a new technique, intrinsic Raman spectroscopy, which utilizes diffuse reflectance to correct sample turbidity-induced distortions in Raman spectra. This technique allows for improved analyte concentration measurements in turbid media.