Michal Tepper
Under the supervision of Prof. Israel Gannot
Introduction
 Spectroscopy of biological tissues is a powerful tool for
evaluation of tissue composition and functionality.
 Photothermal spectroscopy is a method for
performing tissue spectroscopy, based on measuring
tissue thermal changes due to light excitation.
Previous Photothermal Research
 Photothermal spectroscopy was shown to be
valuable for surface measurements (Milner, 1998)
 Single particles can be detected (Zharov, 2003)
 Measurements through fiber bundles are a new
field and offer new possibilities
The Method
 The
temperature increase depends on tissue
composition, its optical properties and the exciting
laser wavelength.
 Using several wavelengths for the excitation will allow
us to estimate tissue composition.
 The method can be applied to internal cavities using a
commercially available endoscope.
The Method
LASER
THERMAL
CAMERA
OPTICAL
FIBER
COHERENT WAVEGUIDE BUNDLE
ENDOSCOPE
TISSUE
The Goal
 One promising application is the determination of the
oxygenation of a tissue, a widely researched subject
due to its clinical importance:
 Tumor detection (90% of human cancers arise from
epithelial cells)
 Cancer treatment adjustment
 Hypoxia detection
Research Stages
 Creating a theoretical model
 Developing an algorithm suitable for
different types of tissue
WE ARE HERE
 Tissue-like-phantoms experiments
 Tissue engineered phantoms experiments
 In-vivo experiments
The Theoretical Model
• Simulating temperature rise in the
tissue as a result of laser illumination:
Defining material concentration (water, melanin, hemoglobin)
Calculating optical properties of the tissue’s layers
Calculating absorption using MCML
Calculating tissue temperature distribution using COMSOL
Calculating the thermal image seen by the camera
Skin Tissue Model
A seven layer skin tissue model was selected.
Thickness
n
g
H2O%
Blood%
stratum corneum
20
1.5
0.86
0.05
2.1*10-4
epidermis
80
1.34
0.8
0.2
2.1*10-4
150
1.4
0.9
0.5
0.02
80
1.39
0.95
0.6
0.3
1500
1.4
0.8
0.7
0.04
80
1.38
0.95
0.7
0.1
6090
1.44
0.75
0.7
0.05
papillary dermis
upper blood net dermis
reticular dermis
deep blood net dermis
hypodermis
Monte-Carlo Results
J/cm3
z [cm]
Baseline absorption
Hemoglobin absorption
in dermis
Melanin absorption
in epidermis
Illumination
r [cm]
COMSOL Results
T [K]
r [cm]
z [cm]
Thermal Image Simulation
y [cm]
T [K]
x [cm]
Preliminary Results
T [K]
T [K]
 Selection of excitation wavelengths:
25% melanin
15% melanin
5% melanin
Wavelength [nm]
Wavelength [nm]
saturation evaluation is limited by skin color
Hemoglobin Optical
Absorption
Limitations
 Solving the equation system is inaccurate
because of measurement errors.
 The model might be inaccurate and parameters
might change between people and between
different locations.
We want to develop a generic algorithm suitable
for different tissues and wavelengths.
Intuition
 Examining the shape of the temperature function and
T [K]
µa
not the values.
Wavelength [nm]
Wavelength [nm]
The Solution
 The measured temperature is a function of
several unknowns, including the saturation.
 The unknowns can be estimated using a simple
curve fitting algorithm.
 The curve fitting algorithm depends on the initial
guess for each of the unknowns. Therefore, an
initial guess algorithm for the saturation was also
developed.
Temperature Function
T1=f1()A1
A1=Σ µi·ci
Effective absorption
of layer 1
T2=f2(A1 ,)A2
T3=f3(A1 , A2 ,)A3
The absorption of each layer is affected by the absorption of upper layers
Temperature Function
 The temperature rise is the sum of effective
contributions of all the layers:
T ( )  T (layer1)  T (layer 2)  T (layer 3) 
 Each layer affects deeper layers:
T ()  T0  f1  A1  f2  A1   A2  f3  A1, A2   A3 
 The functions f i can be approximated using Taylor
approximation:
f 2  A1   f 2  0   f 2'  0   A1 
1 ''
2
f 2  0    A1 
2
f 2  A1   b1  b2  A1  b3   A1 
2
Temperature Function
 Comparing computational results to the
theoretical equations enables us to estimate some
of the coefficients:
319.7
319.65
with water
319.6
319.55
Temperature [K°]
Temperature [K°]
319.6
y = 0.0225x + 319.18
R² = 0.9981
319.5
319.45
Calculation
319.4
Linear (Calculation)
without water
319.5
319.4
319.3
319.2
319.1
319
319.35
318.9
9
12
15
Hemoglobin concentration [g/dl]
18
410
415
420
425
430
Wavelength [nm]
435
440
Temperature Function
 For skin tissue (containing melanin):
T  T0  a1  Melanin  a2   Baseline  a3  Melanin   Baseline  a4   Hemoglobin
 Hemoglobin  S   HbO  1  S    Hb
 For “internal” tissue (skin tissue without melanin):
T  T0  a1  Baseline  a2   Baseline 
2
 a3  Baseline  Hemoglobin  a4   Baseline  Hemoglobin  a5  Hemoglobin
2
2
2
Results
 Results of the initial guess algorithm for skin
tissue with 7.5-10% melanin:
1
Estimated
Estimated saturation
saturation
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
True saturation
True saturation
0.7
0.8
0.9
1
Results
 Results of the saturation estimation algorithm
for the tissue:
1
saturation
Estimated
Estimated saturation
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
TrueTrue
saturation
saturation
0.7
0.8
0.9
1
Results
 The results of the algorithm demonstrated
considerable agreement with the model’s
actual oxygenation values.
 RMS of the error is reasonable.
Hemoglobin:
9g/l
10.5g/l
12g/l
13.5g/l
15g/l
Total
2.5% melanin
8%
7.6%
6.8%
7.7%
8.1%
7.7%
melanin
8.7%
5.1%
6.3%
5.4%
6.8%
6.6%
7.5% melanin
5.2%
6.4%
5.9%
6.4%
8.1%
6.5%
10% melanin
9.1%
6.4%
7.1%
8.4%
5.7%
7.5%
5%
Tumor Oxygenation Values
Tissue
Median satuation
Reference value
Spleen
92.7
96
85
96-97
Gastric mucosa
82.6
97
Uterine cervix
69
97
Liver
42.7
98
Cervix cancer
3-32
97-98
Adenocarcinomas
9-13
96-97
19
96-98
Subcutis
Squamous cell carcinomas
Results
 Results of the initial guess algorithm for skin
tissue without melanin, representing internal
tissue:
1
Estimated
Estimated saturation
saturation
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
TrueTruesaturation
saturation
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Results
 Results of the saturation estimation algorithm
the tissue:
1
0.9
saturation
Estimated
Estimated saturation
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
True saturation
0.7
True saturation
0.8
0.9
1
Results
 Results for skin tissue without melanin.
 RMS of the error is relatively small.
Hemoglobin:
9g/l
10.5g/l
0% melanin
5.3%
4.8%
12g/l 13.5g/l 15g/l
4.2%
5.3%
5.2%
Total
5%
Experimental Setup
 The phantoms were created using various types
of absorbers.
Experimental Setup
 The agar used in the phantoms simulates
the thermal properties of the skin.
Absorption spectra
 The selected absorbers were Methylene
Blue, Indocyanine Green (ICG) and ink.
Experimental Setup
 The phantoms are excited by 3900s tunable
laser, pumped by Millenia Vs Laser.
Experimental Setup
 The relative intensity of the illumination is
measured using an integration sphere.
Experimental Setup
 The temperature is measured by
thermoVision A40 IR camera.
 The experiments can be monitored
using MicroMax CCD camera.
Experimental Setup
 The
setup can be
further simplified by
using
diodes
and
thermocouples.
Temperature measurement
294.6
Max temperature
not reached
294.4
294.2
294
T [K]
293.8
293.6
293.4
Noisy measurements
293.2
293
Calibration drift
292.8
292.6
0
500
1000
time [sec]
1500
Temperature measurement
 The temperature is estimated using a curve fitting
algorithm.
293.4
293.4
293.2
Tsat
fit_ys vs. fit_xs
fit 1
293.2
293
293
T0
292.8
292.8
292.6
292.6
292.4
292.4
292.2
292.2
292
0
100
200
300
400
500
600
700
150
200
250
300
350
Intensity Calibration
 Calculated using measurements with the integration
sphere
Calibrated Measurement Results
 Temperature increase, normalized according to
intensity
Estimated temperature function
a1, a2 and S are unknowns and will be estimated
using the curve fitting algorithm.
T  T0  I  a1  a2   
  S   B  (1  S )  G
T  T0
T 
 a1  a2  
I
a1 and a2 are a function of
the materials thermal and
physical
properties
and
concentrations.
S is the saturation.
(ratio between ICG
Methylene Blue)
and
Experimental Stages
 Preliminary measurements: Used to fine-tune
experimental procedures and algorithms and
to adjust material concentrations.
 Repeating measurements with a larger
number of phantoms
 Validating the algorithms
Results
 Preliminary measurements: Five agar models
containing two materials.
 For each sample there are 5 measurements and 3
unknowns.
1
Estimated ratio
0.8
0.6
0.4
0.2
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Real ratio
0.7
0.8
0.9
1
Results
 The adjusted procedures were used to measure 11
phantoms.
Results
 Preliminary measurements of phantoms with upper
absorbing layer (simulating the epidermal layer).
Future Research
 Layered agar phantoms with increasing complexity
 Adjusting the algorithms
 Tissue engineered phantoms
 Fiber bundle experiments
 In-vivo experiments
 Collaboration with Rabin Medical Center
Fiber Bundle Experiments
 Infrared imaging bundles can be used to detect
tumors in internal organs.
 The bundles can be integrated to a commercially
available endoscope.
900 fibers HGW
Fiber Bundle Experiments
 A preliminary experiment with 1mm fiber bundle was
performed on an agar model.
 The measured signal is clearly reduced
 Results are satisfying for a first experiment:
Reference value:
100%
Thank you..