SHAPE Society

1. Design and Performance of a Localized Fiber Optic,
Spectroscopic Prototype Device for the Detection of the
Metabolic Status of “Vulnerable Plaque”:
in-vitro Investigation of Human Carotid Plaque

2. 2
OUTLINE
• INTRODUCTION
– Problem identification, objectives, specific aims, hypotheses, and
background review
• OPTICAL DESIGN
– ~ 1 mm3
tissue volume interrogation achieved with optical probe
• METHODS
– Laboratory setup
– Data collection
– Calibration model development
• RESULTS
• DISCUSSION/CONCLUSIONS
– Limitations
– Future work

3. 3
PROBLEM IDENTIFICATION
• Atherosclerotic cardiovascular disease 6.3 M deaths / yr
worldwide
• Cardiovascular disease #1 killer in the U.S.
• 1.5 M myocardial infarctions (MI) / yr in the U.S.
• 250,000 / yr sudden cardiac deaths
• $111.8 billion / yr health care costs (direct/indirect)
• Major risk factors
– Smoking
– High blood cholesterol (LDL/HDL ratio)
– Physical inactivity
– Overweight/Obesity
– Diabetes mellitus
Source: American Heart Association. 2002 Heart and Stroke Statistical Update.
2001. http://www.americanheart.org

4. 4
Everybody has atherosclerosis, the question is who has
vulnerable plaque
Sudden Cardiac Death
Acute MI
Vulnerable
Plaque(s)

5. 5
Unknown Diagnosis – Vulnerable Plaque
• The “precursor” that ultimately ends in acute
thrombi (clots) of sudden death MI
• Inflammatory cells found preferentially in
vulnerable plaque
• Activity sustained through anaerobic metabolism
and lactate production

6. 6
Morphology vs. Activity Imaging
Inactive and
non-
inflamed
plaque
Active
and
inflamed
plaque
Appear Similar in
IVUS OCT MRI
w/o
CM
Morphology
Show
Different
Activity
Thermography, Spectroscopy,
immunoscintigraphy, MRI with
targeted contrast media…

7. 7
HISTOLOGY
LIPID CORETHROMBUS
FIBRO-CALCIFIC
J Am Coll Cardiol. 2001 Sep;38(3):718-23.Am J Pathol. 2000 Oct;157(4):1259-68.
Courtesy of Texas Heart Institute

8. 8
LONG-TERM OBJECTIVES
• Develop an optical spectroscopy catheter system to
determine the metabolic status of atherosclerotic vessels
– No exogenous dyes
– No ionizing radiation
– Low cost addition to existing cardiac catheterization laboratory
• Locate and identify vulnerable plaque based on metabolic
status with optical spectroscopy

9. 9
SHORT-TERM GOALS
• Demonstrate feasibility in-vitro of optical
spectroscopy to accurately determine
metabolic status
– Tissue lactate concentration
– Tissue pH

10. 10
Specification Tissue pH Tissue Lactate
Concentration
[µmoles lactate /
gram tissue]
in-vitro
Temperature
(°C)
in-vitro Experimental
Stability
< 0.03
change/hr
- <0.4°C change/hr
Optical Calibration
Range
6.80 – 7.60 2 – 20 32.0 – 38.0
Optical Calibration
Accuracy/
Prediction Error
(R2
/ SECV)
≥0.75 / 0.08
pH units
≥0.75 / 1.0
umoles/ gram
tissue
-
Maximum # of Factors
in Optical Calibration
Model
5 to 6
independent
samples per
factor
5 to 6
independent
samples per
factor
-

11. 11
SPECIFIC AIMS
1) Design a reflectance-based fiber optic probe that uses
visible to near-infrared light optimally to interrogate a
small volume of tissue.
2) Estimate the depth penetration of fiber optic probe,
based on theory and experiments.
3) Identify major interferents to the optical spectra and
tissue reference measurements collection.
4) Collect and analyze fresh tissue from human carotid
endarterectomies to create large optical calibration
training set while maintaining tissue in a viable
physiological state in-vitro.

12. 12
HYPOTHESES
1) A small fiber optic prototype can make optical
measurements in-vitro for the assessment of
metabolically active plaque in a defined region of
tissue (< 1 mm3
volume).
2) in-vitro experimental factors can be assessed to their
importance in affecting the optical calibration accuracy.
The tissue temperature, experiment time, and gross
pathology are identified a priori.
3) Mathematical models can be developed which relate
the corresponding optical spectra to the individually
measured metabolic parameters (tissue pH and lactate
concentration) in the presence of inherent pathological
variability.

13. 13
SPECTROSCOPY BASICS
In general, spectroscopy is the use of the
electromagnetic spectrum to perform physical or
chemical analysis

14. 14
PREVIOUS WORK
• Optical spectroscopy proposed by Lodder (UKY), Feld
(MIT) and Jaross (Germany) to characterize
morphological properties of atherosclerotic plaques such
as thin fibrous cap, large lipid core

15. 15
LACTATE AND PLAQUE
• Metabolite produced by activated macrophages
• Studies show lactate is present in plaque (Kirk, Zemplenyi)
Anaerobic glycolysis: LDH
Pyruvate + NADH → Lactate + NAD+
Overall anaerobic process:
Glucose + 2ADP + 2Pi → 2 Lactate + 2ATP +2H20 + 2H+

16. 16
NIR Spectrum
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1700 1800 1900 2000 2100 2200 2300 2400 2500
wavelength (nm)
arbitraryunits(absorbance)
Near infrared absorbance of lactic acid

17. 17
PLAQUE pH
Large scale, ex-vivo study on carotid plaques demonstrated metabolic
heterogeneity in grossly pathological areas (Grascu, 1999)
Inflamed regions of plaque are lower in pH in the atherosclerotic Watanabe
rabbit and human carotid plaques; plaque pH heterogeneity demonstrated
(Naghavi, 2002)

18. 18
DR. SOLLER’S LAB
• Tissue pH can be measured by NIR spectroscopy in heart muscle
(Soller, Zhang 1998)
• Lessons learned: volume of optical measurement >> volume of
reference measurement
• Heterogeneity in a “large” tissue volume may be solved with
smaller optical probe

19. OPTICAL DESIGN

20. 20
DESIGN PROCESS
• Define of optical probe requirements
• Theoretical considerations of tissue optical
properties
• Monte Carlo simulations interpretation
• Building and testing several optical probes
• Depth penetration assessment

21. 21
Optical Catheter
System Diagram
• Optical fibers carry light to tissue
• Light is reflected and/or backscattered
toward fibers that return light to
spectrometer and tissue absorbance
calculated
• Catheter geometry and optical
coupling important
• Small source-receiver separations:
light penetrates tissue while restricting
volume interrogated
~2 mm
Light in
To spectrometer
wavelength
Absorbance
tissue
interface

22. 22
THEORY
• Monte Carlo Simulations
– Estimate light paths in complex absorbing and
scattering medium
– Random events: reflection, absorption, scattering, or
transmission
– Define grid geometry, specify tissue optical properties
Probe – tissue
interface
n1=n2
dr= ∆25 µm
dz=∆5
µm
Source
Diffuse
Reflectance(r)

23. 23
0.00E+00
5.00E+01
1.00E+02
1.50E+02
2.00E+02
2.50E+02
3.00E+02
3.50E+02
0.000 0.005 0.010 0.015 0.020 0.025
radius (cm)
diffusereflectance[1/cm2]
514 nm 633 nm 1064 nm
0.00E+00
1.00E+02
2.00E+02
3.00E+02
4.00E+02
5.00E+02
6.00E+02
7.00E+02
0.000 0.005 0.010 0.015 0.020 0.025
radius (cm)
diffusereflectance(1/cm2)
470 nm 633 nm 514 nm 1050 nm 1064 nm
a) b)
normal atherosclerotic
Diffuse Reflectance(radius)
Theoretical Depth Penetration
Wavelength
(nm)
Normal Aorta
(microns)
Atherosclerotic
Aorta (microns)
470 -- 417.5
514 1063 722
633 1338 1197
1050 1333 1187
1064 -- 927

24. 24
OPTICAL EXPERIMENTS
• Compared signal-to-noise ratios (SNR)
for several fiber types / configurations
– Different core sizes / number of fibers
– With or without optical windows
– Source-receiver separations

25. 25
PROBE GEOMETRY
Large
OD
Probe
360 degree
illumination
w/ optical
window
Forward illumination
No optical window
Final probe
W/ optical window

26. 26
TISSUE PENETRATION STUDY
1) Reference spectra collected for each optical configuration (50, 500 micron
separations).
2) Absorbance spectra collected with n-th slice of 50 micron tissue.
3) Second absorbance spectrum collected with n-th slice plus diffuse
reflector. Both absorption and scattering attenuate tissue signal.
Optical
Probe
1) 2) 3)
To spectrometer
Diffuse Reflector
~50 um slices
Aortic tissue
Light source
(source fibers)
Diffuse Reflector

27. 27
0
200
400
600
800
1000
420 550 760 970 1450 1750 2250
wavelength (nm)
penetrationdepth(um)
S-D separation = 0.05 mm S-D separation = 0.5 mm

28. 28
FINAL PROBE DESIGN
• Using a source-receiver separation of 50 microns, adequate depth
resolution could be achieved in plaque in both the visible and near-
infrared
• Increasing the collection fiber core diameter size to 200 microns
with improved transmission out to 2400 nm, higher signal-to-noise
ratio is achieved by improving the fiber collection area by 4 times
and collection efficiency
• Using a 0.5 mm thick quartz optical window fused on the common
end, with forward-viewing optics, the signal-to-noise ratio would be
further improved across all wavelengths

29. 29
Fiber optic probe used for all optical
determinations in this study.
1 cm

30. METHODS

31. 31
Heating pad
Balance
Fiber optic probe
Gas line
Micro pH and reference junction
electrodes
Thermistor
37°C, >85% RH
Spectralon 50% reflectance
standard
Tissue bath
Laboratory setup for all studies.

32. 32
Humidified Incubator maintained at 37°C.

33. 33
in-vitro Plaque Validation Study
• Minimum Eagle’s Medium (MEM), pH 7.4, 5.6 mM glucose, 26.2 mM
NaHCO3, with non-essential amino acids was used (Invitrogen).
• Media equilibrated with 75% O2 / 5% CO2 gas mixture prior to plaque
addition.
• Seven human carotid plaques (UMass Memorial IRB Approval
#10041) were collected and placed immediately in 37°C media
enclosed in a humidified incubator at 37°C.
• Two plaques that were not placed in the liquid media, only in the
humidified air of the incubator, served as controls.
• Measurements were taken with a 0.5 mm OD multi-parameter sensor
placed in the tissue (Diametrics, MN).
• Changes in tissue pH, temperature, PO2 and PCO2 over time were
analyzed.

34. 34
Control plaque with multi-parameter sensor in place.

35. 35
Box-whisker plots for
∆pH / hour and
∆temperature / hour (top
and bottom, respectively)
for the control and test
plaques. The change in pH
and temperature over time
is greater in the controls
than the test plaques.

36. 36
STABILITY REVIEW
• Experiment time fixed max. 4 hrs
• in-vitro experimental stability criteria met
– <0.03 pH units/hr and < 0.4°C /hr
• Tissue temperature in media > 32°C to ensure tissue
viability
• Tissue values stable and different from media, controls
• Plaques in oxygenated media had higher pO2 readings
versus control plaques
• Thickness of plaque affected magnitude of pO2 readings
• Unable to measure calcified areas over time

37. 37
OPTICAL CALIBRATION
• ~24 additional human carotid plaques were collected and placed in-vitro.
• Absorbance spectra (667 – 2500 nm) of each area were taken using
Nicolet FTIR 670 spectrometer with fiber optic probe (Remspec, MA) for
optical lactate determination.
• Tissue biopsies of the same area were taken using a 4-mm punch
biopsy and immediately frozen in liquid nitrogen.
• Reference tissue lactate (LA) assayed using micro-enzymatic methods.
Values reported as micromole LA per gram wet tissue.
• Matching spectra and reference values modeled by multivariate
calibration techniques. R2
and the standard error of cross-validation
(SECV) used to assess model accuracy.

38. 38
OPTICAL CALIBRATION
• Absorbance spectra (400-1100 nm) were collected for a smaller subset
of 14 plaques using a Control Development spectrometer and same
optical probe for optical tissue pH determination.
• Reference tissue pH was measured using 750 um diameter micro-pH
electrodes.
• Matching spectra and reference values modeled by multivariate
calibration techniques. R2
and the standard error of cross-validation
(SECV) used to assess model accuracy.

39. 39
MODEL DEVELOPMENT
• Partial least-squares, factor analysis
– Create calibration with as many points as possible
• Cluster analysis
– Investigate (in)homogeneity of spectra

40. RESULTS

41. 41
SPECTRA COLLECTION: Lactate
• 82 raw absorbance spectra shown below (667-2400 nm)
• Key features are water (~970, 1450 and 2000 nm), cholesterol and its
esters (~1750 nm).
“Thrombus/Red”
n=22
“Fatty/Yellow”
n=41
“Calcified/White”
n=19

42. 42
SPECTRA COLLECTION: Tissue pH
• 48 raw absorbance spectra shown below (400-1100 nm)
• Key features are hemoglobin (550 – 575 nm) and water (~970 nm)
absorption
400 500 600 700 800 900 1000
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
wavelength (nm)
Absorbance(arbitraryunits)
400 500 600 700 800 900 1000
0
0.5
1
1.5
2
2.5
3
wavelength (nm)
Absorbance(arbitraryunits)
400 500 600 700 800 900 1000
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
wavelength (nm)
Absorbance(arbitraryunits)
“Thrombus/Red”
n=11
“Fatty/Yellow”
n=23
“Calcified/White”
n=14

43. 43
Histogram of lactate reference measurements
3.2 ± 2.7 (mean ± SD)
n=82
0
5
10
15
20
25
30
35
0 0.2 0.5 1 2 5 8 10 More
lactate concentration (micromoles/gram tissue)
frequency

44. 44
Histogram of tissue pH reference measurements
7.33 ± 0.21 (mean ± SD)
n=48
0
2
4
6
8
10
12
14
6.70 6.80 6.90 7.00 7.10 7.20 7.30 7.40 7.50 7.60 7.70 More
pH units
frequency

45. 45
REFERENCE MEASUREMENTS
• No spurious correlations between measured variables
• Tissue temperature, experiment times within validated
experiment parameters
• Pathology subjective
Variables Correlation Coefficient
between data
Tissue Lactate – Tissue
Temperature
0.03 (n=82)
Tissue pH – Tissue
Temperature
0.00004 (n=48)
Tissue Lactate – Tissue pH 0.0008 (n=48)

46. 46
RESULTS: Tissue Lactate
• 6-Factor model from 17 points
• Wavelength regions
contributing to model:
– 2030 – 2330 nm
• The R2
of the determination for
optical lactate (LA) calibration
= 0.83.
• Estimated accuracy ~ 1.4
micromoles LA/gram tissue.

47. 47
2000 2050 2100 2150 2200 2250 2300 2350
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
2000 2050 2100 2150 2200 2250 2300 2350
1.35
1.4
1.45
1.5
1.55
1.6
1.65
1.7
1.75
1.8
1.85
2000 2050 2100 2150 2200 2250 2300 2350
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
A
B
C
Clustering solution for 82 spectra for the optical determination of
lactate. Cluster A – 45 spectra, B – 31 spectra, and C – 6 spectra.
Cluster A contained the first 21 calibration spectra collected.

48. 48
RESULTS: Tissue pH
• 3-Factor model from 17 points
• Wavelength regions contributing
to model:
– 1: 400 – 615 nm
– 2: 925 – 1890 nm
– 3: 2044 – 2342 nm
• The R2
of the determination for
optical tissue pH calibration =
0.75.
• Estimated accuracy ~ 0.09 pH
units.
Region 1 Region 2 Region 3
6.80
7.00
7.20
7.40
7.60
7.80
6.80 7.00 7.20 7.40 7.60 7.80
electrode pH
NIRpH

49. 49
400 500 600 700 800 900 1000 1100
0
0.5
1
1.5
2
2.5
400 500 600 700 800 900 1000 1100
0
0.2
0.4
0.6
0.8
1
A
B
Clustering solution for optical determination of tissue pH. Two
clusters: A contains 39 spectra, B contains 9 spectra. The
underlying pathology in cluster B was identified as all thrombotic
points.

50. 50
DISCUSSION
• Lactate model on portion of entire data set
– Further factor analysis showed spectra weakly
associated with theoretical lactate peaks
– Number of factors in model too high for # of samples
used; need more samples
• Tissue pH model on portion of entire data set
– Further factor analysis showed spectra associated
with Hb and water peaks, evidence of pH-induced
shift
– Number of factors acceptable for # of samples used

51. 51
CONCLUSIONS
1) A small fiber optic prototype can make optical measurements
in-vitro for the assessment of metabolically active plaque in a
defined region of tissue (< 1 mm3
volume).
• Hypothesis accepted
1) in-vitro experimental factors can be assessed to their
importance in affecting the optical calibration accuracy. The
tissue temperature, experiment time, and gross pathology are
identified a priori.
• Hypothesis accepted

52. 52
CONCLUSIONS
3) Mathematical models can be developed which relate the
corresponding optical spectra to the individually measured
metabolic parameters (tissue pH and lactate concentration) in
the presence of inherent pathological variability.
• Hypothesis rejected for large n; pending work
– Limited feasibility of models generated
– Pathological variability large
– Unmodeled tissue variability
– Lactate reference method precision
– Optical tissue volume >> real tissue pH heterogeneity
– Long-term spectrometer drift could not be ruled out

53. 53
FUTURE WORK
• Considerable in-vitro work needs to continue
• Other clustering algorithms, pre-processing methods
• Reference lactate measurement precision
• Reduce unmodeled variability, better tissue model
• Larger data sets
• Spectrometer stability
• Rigorous acceptance criteria must be met before use
in-vivo animals or humans

54. 54
ACKNOWLEDGEMENTS
Texas Heart Institute, Center for Vulnerable Plaque Research/UT Houston
Dr. S.W. Casscells
Dr. Silvio Litovsky
Dr. Morteza Naghavi
Department of Surgery, University of Massachusetts Medical School
Vascular Surgeons: Dr. P. Nelson, Dr. B. Cutler, Dr. A. Fox and Dr. M. Rohrer
Dr. Babs R. Soller
Dr. Patrick Idwasi
This work was supported by US Army DREASM Grant