Background
Coronary artery disease is the leading cause of death in the United States, claiming more than 480,000 lives annually. However, the most critical information for determining the clinical severity of a given individual’s disease is not accessible via any available diagnostic tool.
Several new techniques are emerging which show great promise for identifying the vulnerable atherosclerotic plaques that are responsible for more than 50% of fatal myocardial infarctions.
Included in these are contrast enhanced and intravascular magnetic resonance imaging (MRI), optical coherence tomography (OCT), electron-beam computed tomography (EBCT), fluorescence spectroscopy, angioscopy, elastography, and intravascular ultrasound (IVUS). The majority of these techniques are capable of providing morphological or structural information about the arterial wall. However, none (with the possible exception of fluorescence spectroscopy) are able to provide information about the chemical composition of the arterial wall that is now recognized as a major determinant of plaque stability.
Raman spectroscopy, a technique utilizing laser irradiation, can be used to provide specific chemical information about biological tissue without requiring a biopsy and without causing any damage. Utilizing near-infrared (830 nm) laser light, we are able to examine coronary arteries and classify them as either non-atherosclerotic, non-calcified atherosclerotic, or calcified atherosclerotic with >94% accuracy. We have developed several spectroscopic models that each provides unique information and allow us to make this diagnosis. The first is a statistical model that is based on Principal Component Analysis [7]. This model provides a proof-of-principal demonstration that Raman spectroscopy can be a useful tool for studying atherosclerosis, however it provides little physical insight about the disease. Therefore, we have taken a unique approach to studying spectra of coronary arteries and developed two additional models in order to gain access to the large amounts of information contained in Raman spectra.
The first model is based on the Raman spectra of individual chemical components that compose the arterial wall [9,10]. The chemical model provides an accurate quantitative analysis of the free cholesterol, cholesterol ester, triglyceride, phospholipid, and calcium salt content of the arteries. This model has also been extended to provide an accurate analysis of peripheral arteries to study peripheral vascular disease [8].
The final, and most recently developed model is based on the morphological structures that compose normal and diseased arteries [1,2]. This model was developed by acquiring basis spectra of the individual morphological structures (Figure 1), such as collagen and elastin fibers, foam cells, necrotic core, cholesterol crystals, calcifications, smooth muscle cells, etc., with a confocal Raman microspectrometer. These basis spectra are then linearly combined to reconstruct the spectra acquired from intact biopsy samples of coronary arteries (Figure 2). The
Raman morphometric analysis (Figure 3) is then compared to standard histopathologic diagnosis and a decision algorithm is generated with logistic regression (Figure 4).
Thus, with the application of these three models Raman spectroscopy is able to provide a morphological/structural analysis similar to other emerging techniques, as well as a quantitative

chemical analysis and accurate disease diagnosis. Proper interpretation of this wealth of information should provide valuable information about the stability of atherosclerotic plaques.
Current Work
Having developed several successful in vitro models of atherosclerosis, we are currently developing a clinical Raman spectroscopy system that is able to take in vivo data. Collection of clinical information requires the use of catheters that allow for transluminal access to the coronary arteries and permit accurate, efficient transmission of Raman photons without interference from background signals generated in the optical fibers themselves. We are designing and constructing such catheters to allow this technique to be taken from bench to bedside. Clinical implementation of this technique would allow for substantial progress in the practice of interventional cardiology, and would furthermore allow a means to evaluate the efficacy of various medications, such as lipid lowering drugs, in longitudinal studies.
Additional studies are also being conducted in our laboratory to study the degradation product ceroid that is found in atherosclerotic and atheromatous arteries. Ceroid deposits are initially identified through the use of fluorescence microscopy; the fluorescence emission being a specific defining characteristic of ceroid. The deposits are then studied in more detail with
Raman microscopy. The high degree of chemical specificity for the Raman effect has allowed a detailed analysis of these compounds and may lead to a greater understanding of the atherosclerotic disease process.
Current Research Group
 Core Investigator: Michael S. Feld Ph.D.
 Graduate Students: Jason T. Motz
 Research Engineer: Luis H. Galindo
 Collaborators: John R. Kramer M.D.
*
Maryann Fitzmaurice M.D., Ph.D.
Angheloiu M.D.
*
§
, Arnold Miller M.D.
, Barry D. Kuban
, Martin A. Hunter Ph.D.
# , Charles Paniszyn M.D.
#
* , Joseph Arendt Ph.D.
* , George
,
* The Cleveland Clinic Foundation
# MetroWest Hospital, Leonard Morse Campus
§
University Hospitals Cleveland/Case Western Reserve University
Recent Publications
1.
“ Diagnosis of Human Coronary Atherosclerosis by Morphology-based Raman Spectroscopy”
Buschman HPJ, Motz JT, Deinum G, Römer TJ, Fitzmaurice M, Kramer JR, van der Laarse
A, Bruschke AV, Feld MS. Cardiovascular Pathology 10 (2): 69-82 (2001).

2.
“Raman Microspectroscopy of Human Coronary Atherosclerosis: Biochemical Assessment of in situ Morphological Structures” Buschman HP, Deinum G, Motz J, Fitzmaurice M,
Kramer JR, van der Laarse A, Bruschke AV, Feld MS. Cardiovascular Pathology 10 (2): 69-
82 (2001).
3.
“Raman Diagnosis of Atherosclerosis: A Morphological/Histochemical Approach” Motz JT,
Kramer JR, Feld MS. In Cardiovascular Application of Laser , Medical and Engineering
Publishers, Inc., Washington, D.C. in press (2001).
4.
“Prospects of Laser Spectroscopy to Detect Vulnerable Plaque” van de Poll SWE, Motz JT,
Kramer JR, Feld MS. In Cardiovascular Plaque Rupture (ed: Brown D) Marcel Dekker,
New York NY, in press (2001).
5.
“The Raman Spectrum of Atherosclerosis: A Review of Newly Developed Modeling
Techniques” Motz JT, Buschman HPJ, van de Poll S, Kramer JR, Dasari RR, Feld MS.
Research Advances in Applied Spectroscopy 1 : 49-67 (2000).
6.
“Prospects for in vivo Raman Spectroscopy” Hanlon EB, Manoharan R, Koo T-W, Shafer
KE, Motz JT, Fitzmaurice M, Kramer JR, Itzkan I, Dasari RR, Feld MS. Physics in
Medicine and Biology 45 (2): R1-R59 (2000).
7.
“Histological Classification of Raman Spectra of Human Coronary Artery Atherosclerosis using Principal Component Analysis” Deinum G, Rodriguez D, Römer TJ, Fitzmaurice M,
Kramer JR, Feld MS. Applied Spectroscopy 53 (8): 938-942 (1999).
8.
"Biochemical Composition of Human Peripheral Arteries Using Near Infrared Raman
Spectroscopy” Salenius JP, Brennan JF, Miller A, Wang Y, Aretz T, Sacks B, Dasari RR,
Feld MS. Journal of Vascular Surgery , 27 (4): 710-719 (1998).
9.
“Histopathology of Human Coronary Artery by Quantifying its Chemical Composition with
Raman Spectroscopy” Römer TJ, Brennan III, JF, Fitzmaurice M, Feldstein ML, Deinum, G,
Miles, JL Kramer JR, Lees RS, Feld MS. Circulation 97 (9): 878-885 (1998).
10.
“Determination of Human Coronary Artery Composition by Raman Spectroscopy” Brennan
JF, Römer TJ, Lees RS, Tercyak AM, Kramer JR, Feld MS. Circulation 96 (1): 2843-2849
(1997).
11.
“Near Infrared Spectrometer Systems for Human Tissue Studies” Brennan JF, Wang Y,
Dasari RR, Feld MS. Applied Spectroscopy 51 (2): 201-208 (1997).
12.
“Compound Parabolic Concentrator Probe for Efficient Light Collection in Spectroscopy of
Biological Tissue” Tanaka K, Pacheco MTT, Brennan JF, Itzkan I, Berger AJ, Dasari RR,
Feld MS. Applied Optics 35 (4): 758-763 (1996).
13.
“PdSi Focal Plane Array Detectors for Short-Wave Infrared Raman Spectroscopy of
Biological Tissue: A Feasibility Study”, Brennan JF, Beattie ME, Wang Y, Cantella MJ,
Tsaur B-Y, Dasari RR, Feld MS. Applied Optics 35 (28): 5736-5739 (1996).

Smooth Muscle Cell
 -Carotene Crystal
Foam Cell/Core
Adventitial Fat
Elastic Lamina
Calcification
Cholesterol Crystal
Collagen
800 1000 1200 1400 1600 1800
Raman Shift (cm
-1
)
Figure 1. Basis spectra of the morphological model collected with the confocal
Raman microspectrometer from unstained/unfixed thin tissue sections.

Raman shift (cm -1 )
Microscopic
Model Fit
Raman shift (cm -1 )
Figure 2. Fits of the morphological model (black) to spectra acquired from intact arteries (green). The residual (data-fit), which indicates the accuracy of the model, is shown below in blue on the same scale.

as tic
c el l l tit al
f at et aca ro te e cr ys l l lla g l l es te el l /
n ec ro
c or ca lc ifi ca tio n
Figure 3.
Raman morphometric analysis showing the mean±SEM contributions of each morphological structure to the three classes of arteries. Features to note include decreased smooth muscle cell contribution along with increased cholesterol and foam cell/necrotic core presence in diseased tissue; the predominance of calcification in the calcified lesions; the decreased collagen content of the non-calcified plaque, a finding that may indicate the potential of Raman spectroscopy to identify vulnerable plaques.

1.0
0.8
0.6
Normal Artery
Non-Calcified Plaque
Calcified Plaque
Punctate Calcification
0.4
0.2
3  error zone
0.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Cholesterol + Lipid Core
NCR NCR
Figure 4.
Diagnosis scheme for the morphological model. The most significant contributors for diagnosis are calcification and a combination of cholesterol with the lipid core (foam cell/necrotic core) contribution. NCR=non-calcified region, these contributions have been normalized without the contribution of the calcifications to clearly demonstrate the separation in the data. Punctate calcifications are extremely small mineralizations and the mis-classification of these samples is likely due to a sampling error between spectroscopy and pathology.