The fluorescent tracer agent 3,6-diamino-2,5-bisN-[(1R)-1-carboxy-2-hydroxyethyl]carbamoylpyrazine, designated MB-102, is cleared from the body solely by the kidneys. A prototype noninvasive fluorescence detection device has been developed for monitoring transdermal fluorescence after bolus intravenous injection of MB-102 in order to measure kidney function. A mathematical model of the detected fluorescence signal was created for evaluation of observed variations in agent kinetics across body locations and for analysis of candidate instrument geometries. The model comprises pharmacokinetics of agent distribution within body compartments, local diffusion of the agent within the skin, Monte Carlo photon transport through tissue, and ray tracing of the instrument optics. Data from eight human subjects with normal renal function and a range of skin colors shows good agreement with simulated data. Body site dependence of equilibration kinetics was explored using the model to find the local vasculature-to-interstitial diffusion time constant, blood volume fraction, and interstitial volume fraction. Finally, candidate instrument geometries were evaluated using the model. While an increase in source-detector separation was found to increase sensitivity to tissue optical properties, it reduced the relative intensity of the background signal with minimal effect on the measured equilibration kinetics.
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.
Radiation dose is particularly a concern in pediatric cardiac fluoroscopy procedures, which account for 7% of
all cardiac procedures performed. The Scanning-Beam Digital X-ray (SBDX) fluoroscopy system has already
demonstrated reduced dose in adult patients owing to its high-DQE photon-counting detector, reduced detected
scatter, and the elimination of the anti-scatter grid. Here we show that the unique flexible illumination platform
of the SBDX system will enable further dose area product reduction, which we are currently developing for
pediatric patients, but which will ultimately benefit all patients. The SBDX system has a small-area detector
array and a large-area X-ray source with up to 9,000 individually-controlled X-ray focal spots. Each focal spot
illuminates a small fraction of the full field of view. To acquire a frame, each focal spot is activated for a fixed
number of 1-microsecond periods. Dose reduction is made possible by reducing the number of activations of
some of the X-ray focal spots during each frame time. This can be done dynamically to reduce the exposure
in areas of low patient attenuation, such as the lung field. This spatially-adaptive illumination also reduces the
dynamic range in the full image, which is visually pleasing. Dose can also be reduced by the user selecting a
region of interest (ROI) where full image quality is to be maintained. Outside the ROI, the number of activations
of each X-ray focal spot is reduced and the image gain is correspondingly increased to maintain consistent image
brightness. Dose reduction is dependent on the size of the ROI and the desired image quality outside the ROI.
We have developed simulation software that is based on real data and can simulate the performance of the
equalization and ROI filtration. This software represents a first step toward real-time implementation of these
dose-reduction methods. Our simulations have shown that dose area product reductions of 40% are possible
using equalization, and dose savings as high as 74% are possible with the ROI approach. The dose reduction
achieved in clinical use will depend on patient anatomy.
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.
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.
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 Cramér-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.
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