Early detection and treatment of rupture-prone vulnerable atherosclerotic plaques is critical to reducing patient mortality associated with cardiovascular disease. The combination of reflectance, fluorescence, and Raman spectroscopy-termed multimodal spectroscopy (MMS)-provides detailed biochemical information about tissue and can detect vulnerable plaque features: thin fibrous cap (TFC), necrotic core (NC), superficial foam cells (SFC), and thrombus. Ex vivo MMS spectra are collected from 12 patients that underwent carotid endarterectomy or femoral bypass surgery. Data are collected by means of a unitary MMS optical fiber probe and a portable clinical instrument. Blinded histopathological analysis is used to assess the vulnerability of each spectrally evaluated artery lesion. Modeling of the ex vivo MMS spectra produce objective parameters that correlate with the presence of vulnerable plaque features: TFC with fluorescence parameters indicative of collagen presence; NC/SFC with a combination of diffuse reflectance β-carotene/ceroid absorption and the Raman spectral signature of lipids; and thrombus with its Raman signature. Using these parameters, suspected vulnerable plaques can be detected with a sensitivity of 96% and specificity of 72%. These encouraging results warrant the continued development of MMS as a catheter-based clinical diagnostic technique for early detection of vulnerable plaques.
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.
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.
Raman spectroscopy has been well established as a powerful in vitro method for studying biological tissue and diagnosing disease. The recent development of efficient, high-throughput, low-background optical fiber Raman probes provides, for the first time, the opportunity to obtain real-time performance in the clinic. We present an instrument for in vivo tissue analysis which is capable of collecting and processing Raman spectra in less than 2 s. This is the first demonstration that data acquisition, analysis, and diagnostics can be performed in clinically relevant times. The instrument is designed to work with the new Raman probes and includes custom written LabVIEW and Matlab programs to provide accurate spectral calibration, analysis, and diagnosis along with important safety features related to laser exposure. The real-time capabilities of the system were demonstrated in vivo during femoral bypass and breast lumpectomy surgeries. Such a system will greatly facilitate the adoption of Raman spectroscopy into clinical research and practice.
Vulnerable plaques, which are responsible for most acute ischemic events, are presently invisible to x-ray angiography. Their primary morphological features include a thin or ulcerated fibrous cap, a large necrotic core, superficial foam cells, and intraplaque hemorrhage. We present evidence that multimodal spectroscopy (MMS), a novel method that combines diffuse reflectance spectroscopy (DRS), intrinsic fluorescence spectroscopy (IFS), and Raman spectroscopy (RS), can detect these markers of plaque vulnerability. To test this concept, we perform an MMS feasibility study on 17 human carotid artery specimens. Following the acquisition of spectra, each specimen is histologically evaluated. Two parameters from DRS, hemoglobin concentration and a scattering parameter, are used to detect intraplaque hemorrhage and foam cells; an IFS parameter that relates to the amount of collagen in the topmost layers of the tissue is used to detect the presence of a thin fibrous cap; and an RS parameter related to the amount of cholesterol and necrotic material is used to detect necrotic core. Taken together, these spectral parameters can generally identify the vulnerable plaques. The results indicate that MMS provides depth-sensitive and complementary morphological information about plaque composition. A prospective in vivo study will be conducted to validate these findings.