1.

Introduction

Among the diseases of the industrialized world, atherosclerosis is overwhelmingly the prime disorder that leads to serious morbidity and death. Although any artery may be affected, the aorta and the coronary and cerebral systems are the prime targets, with myocardial infarcts and strokes being two main consequences of the disease. The progression and regression of atherosclerotic plaques appear to be related to the amount and type of lipids that accumulate in the intimae.^{1, 2} The detection of these plaques that are vulnerable to rupture may be aided by classifying and quantifying the amounts of lipids, such as cholesterol and cholesterol esters, within the arterial wall.^{3, 4}

Raman spectroscopy is a rapid nondestructive technique capable of assaying chemicals in human artery tissues and characterizing plaques in vivo.^{5, 6, 7, 8, 9, 10, 11} However, clinical applications through optical fiber-based catheters have been hindered by large background signals generated within the fibers. Catheters that have been used in vivo commonly manage the fiber background by utilizing dedicated delivery and collection fibers equipped with filters and optics at the end of the fiber bundle, often leading to a catheter that is too bulky for routine cardiovascular use.^{12, 13, 14}

Puppels and coworkers realized that the background signal generated from an optical fiber was reduced significantly in the high wavenumber (HWVN) Raman region $(\sim 2400{\mathrm{cm}}^{-1}\text{to}\sim 3800{\mathrm{cm}}^{-1})$ , and with proper selection of optical fibers, one could collect quality Raman spectra remotely via a single optical fiber with no additional filters or optics.^{15, 16, 17} These researchers pursued the technology and demonstrated that there were large differences between the HWVN Raman spectra of various structures and the components of the human coronary arterial wall.¹⁸

We have built upon this previous work by evaluating whether HWVN spectra can be used to quantify the relative concentrations of lipid components in the coronary artery. Although this study involved a limited sample population, the preliminary results are encouraging and warrant further investigation into this technique. We collected spectra of homogenized coronary artery tissue through a single optical fiber, developed a model of the spectra, and validated the model by comparing its estimated chemical concentrations to those measured by standard chemical assay techniques. The results verify that HWVN spectra measured through a single optical fiber can provide accurate compositional information of the human coronary artery and raise the possibility of determining this information in vivo via small-profile cardiovascular catheters.

2.

Materials and Methods

The approach of this study was to compare lipid concentrations in coronary artery tissue that were estimated with HWVN spectra to lipid concentrations determined by chemical assay techniques. We used homogenized preparations of coronary artery tissue as opposed to ones with intact arterial walls, since the minimum volume of material needed for the chemical assays was much larger than that measured through a single optical fiber (i.e. $<1{\mathrm{mm}}^3$ ).^{8, 19, 20} This section describes the spectroscopic instrumentation, tissue preparation procedures, and analytical techniques.

3.

Model Development

A model of the HWVN spectra was developed with a calibration data set consisting of spectra and assay values from 14 unique homogenized tissue samples. Example spectra from the calibration set spectra are shown in Fig. 1, which demonstrates how the spectral features varied among samples in the data set. The chemical assay results ranged for FC from $\sim 0\%$ to $\sim 22\%$ ; CE, $\sim 4\%$ to $\sim 29\%$ ; TG, $\sim 0\%$ to $\sim 30\%$ ; PL, $\sim 1\%$ to $\sim 8\%$ ; and PT, $\sim 40\%$ to $\sim 90\%$ . A matrix of the squared correlation coefficients ${\mathrm{R}}^2$ , which was used to check for the independence of the chemical components, is given in Table 1 . The spectra in the calibration set were decomposed with principle component analysis (PCA), and the first four dominant factors were retained, since the minimum standard error of calibration (SEC) occurred at that point for all components.^{23, 24}

Fig. 1

Spectra representing the variation within the calibration data.

Table 1

Correlation coefficients R2 between assay results for each chemical compound in the calibration data set.

Prediction vectors for FC, CE, TG, PL, and PT, which are shown in Fig. 2, were calculated with the four factors and the assay results. The prediction vectors provided an estimate for a given chemical concentration when a spectrum was projected onto them. An initial assessment of the predictive ability of this model was made by retrospectively examining the calibration data set. The SEC for FC was 3.0%; CE, 3.3%; TG, 2.1%; PL, 1.4%; and PT, 5.0%. Charts comparing these model estimates with the chemical assay results are shown in Fig. 3 . The hundreds of spectra taken from each sample were individually processed to assess the residual inhomogeneity in a given tissue preparation; the error bars in Fig. 3 indicate the standard deviation of the calculated values.

Fig. 2

Prediction vectors calculated using the four primary PCA vectors and chemical assay results.

Fig. 3

Retrospective comparison between the chemical assay (CA) results and the estimates made with the Raman spectra (RS) for each chemical component in the calibration data. The diagonal line is the 1:1 correlation line.

Other techniques for processing the spectra were performed, such as partial least squares and classical least squares, but were found to be more susceptible to errors with decreasing signal-to-noise ratios of the spectra.^{23, 24} All numerical analyses were preformed using Matlab® loaded with the Statistics Toolbox (The MathWorks, Inc., Natick, MA).²⁵

4.

Model Validation

We created a validation data set $(N=7)$ to assess the predictive ability of the HWVN spectra model. Tissue preparation and data collection were performed with the same protocol used for the calibration set. The relative component concentrations determined by chemical assay for the validation set ranged for FC from $\sim 0\%$ to $\sim 6\%$ ; CE, $\sim 1\%$ to $\sim 14\%$ ; TG, $\sim 2\%$ to $\sim 22\%$ ; PL, $\sim 1\%$ to $\sim 7\%$ ; and PT, $\sim 75\%$ to $\sim 87\%$ .

The spectra were processed and analyzed with the model to estimate chemical concentrations. Numerical comparisons between the model predictions and the chemical assay results are listed in Table 2, which details for each component the standard error of prediction (SEP) and linear regression analysis slopes $\left(m\right)$ , $y$ -axis intercepts $\left(y_0\right)$ , squared correlation coefficients $\left({\mathrm{R}}^2\right)$ , and P-values.^{23, 24} Graphical representations of the regression results are in Fig. 4 (left-side plot for each component), where the error bars indicate the standard deviation of the values calculated from all individual spectra taken from a given sample.

Fig. 4

Comparison between the chemical assay (CA) results and the estimates made with Raman spectra (RS) for each chemical component and the total cholesterol in the validation data. For each component, a linear regression with the comparison data set is plotted on the left side (solid line), while the right side displays the Bland-Altman analysis of the mean $\left(\overline{\Delta }\right)$ difference between the measurements (solid lines). The dashed lines indicate 95% confidence intervals.

Table 2

Comparison data between the standard assay and spectra model estimates with the validation data set.

We utilized Bland-Altman analysis²⁶ to compare the differences between the chemical assay and model estimates as shown in Fig. 4, (right-side plot for each component). The means $\left(\overline{\Delta }\right)$ and standard deviations $\left(s\right)$ of the comparison differences are also listed in Table 2 along with the 95% confidence intervals for each parameter.

The comparison data for all the cholesterols, FC and CE, were combined to form a total cholesterol $(\mathrm{TC}=\mathrm{FC}+\mathrm{CE})$ category, which was also examined for prediction accuracy with numerical values listed in Table 2 and a graphical comparison given in Fig. 4.

5.

Discussion

The Bland-Altman analysis of the validation data set shows that HWVN Raman spectra analysis can quantify the relative weights of cholesterols that are present in coronary artery tissue to within a maximum error of a few percentage points. These results were obtained over a range where TC was $\sim 20\%$ or less; a larger data set that covers a broader range may have a different outcome. The mean difference between the Raman and chemical assay techniques for the total cholesterol content TC was $\sim 3.3\%$ . For FC and CE, the differences were $<1\%$ and $\sim 2.6\%$ , respectively, with corresponding regression correlations of high statistical significance. Although the mean difference in PL was also very small at $\sim 0.5\%$ , a poor correlation was calculated between the two assay techniques. This was likely due to the narrow content range of PL in the validation set, making PL estimates unreliable. The estimates of TG content compared to the assay results were too low, as indicated by the strongly correlated regression slope of 0.4 and a mean difference of $4.6\pm 4.5\%$ . However, PT was accurately estimated with a $\sim 1.3\%$ mean error, a strong correlation, and a $\sim 1.0$ regression slope. The 95% confidence intervals of the PT linear regression parameters were large, which was likely due to the limited range covered by the data set. Strong trends are not apparent in the Bland-Altman graphs, although FC appears to be underestimated and TG overestimated with increasing concentration levels. These results are similar to those obtained in earlier work in the Raman fingerprint region.⁸ The salient addition of this current study is that these estimates of compound concentrations were made with spectra gathered through a single optical fiber.

Our technique for analyzing the HWVN Raman spectra was developed to quantify the content of specific lipids in the arterial wall. Other compounds, such as specific proteins, glycosaminoglycans, and DNA, are also present in arteries, and it may be possible to expand the current spectral model to quantify the concentrations of these compounds. In future work, it also may be possible to quantify macromolecules, such as lipoproteins, and identify more lipids, such as specific phospholipids and cholesterol esters.

6.

Study Limitations

Correlations exist in the calibration data between FC and CE, as well as between each cholesterol category and protein, which may have reduced the number of independent variables in the model development. Although these correlations are as expected when examining atherosclerotic tissue,^{3, 4} they may have limited the number of PCA factors in our study. A larger calibration data set may be needed, perhaps with added lipid extracts, to weaken internal correlations within the data set and refine the existing model.

Homogenized coronary arteries were used in this study because previous studies on intact (unground) coronary artery sections gave considerable spread to the Raman assay data as measured against chemical assays.²⁷ We concluded that point-to-point spatial variations in the structure of arterial lesions made it difficult to compare the small region examined with Raman spectroscopy with the much larger volume examined by conventional assay. Compounds that are present in large concentrations near the luminal surface of the artery but not significantly present at locations deeper than the artery intima will be diminished in concentration via the homogenization process and may become undetectable. Our ongoing studies compare HWVN Raman spectra with an intact arterial wall. Studies are also underway to assess the performance of this technology with spectra collected by sideways-looking probes in a blood field using cadaver arteries grafted onto living porcine hearts.²⁸ Initial results indicate that measurement durations of a few hundred milliseconds can provide spectra of sufficient quality for accurate composition estimates, but more work needs to be done to assess the effects of blood and collection times on the model estimates.

7.

Conclusions

In this exploratory study, we have demonstrated that the relative chemical composition of a coronary artery can be determined with HWVN Raman spectra collected through a single optical fiber, allowing the possibility of small-profile catheters for routine cardiovascular applications. The algorithms presented here are being integrated into a catheter-based system that can collect quality HWVN spectra from coronary arteries, allowing characterization of the coronary artery wall in vivo, and may act as a diagnostic tool for the detection of vulnerable plaques. Although this study involved a limited sample population, these preliminary results are encouraging and warrant further investigation into this technique.