photothermal spectroscopy as a method for noninvasive early detection of cancerous tumors
DESCRIPTION
PHOTOTHERMAL SPECTROSCOPY AS A METHOD FOR NONINVASIVE EARLY DETECTION OF CANCEROUS TUMORS. 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. - PowerPoint PPT PresentationTRANSCRIPT
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Michal TepperUnder the supervision of Prof. Israel Gannot
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IntroductionSpectroscopy 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.
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Previous Photothermal ResearchPhotothermal 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
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The MethodThe 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.
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The Method
COHERENT WAVEGUIDE BUNDLE
TISSUE
LASER
THERMAL
CAMERA
ENDOSCOPE
OPTICAL FIBER
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The GoalOne 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 adjustmentHypoxia detection
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Research StagesCreating a theoretical modelDeveloping an algorithm suitable
for different types of tissueTissue-like-phantoms experimentsTissue engineered phantoms
experimentsIn-vivo experiments
WE ARE HERE
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The Theoretical Model
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
• Simulating temperature rise in the tissue as a result of laser illumination:
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Skin Tissue Model
ThicknessngH2O%Blood%
stratum corneum201.50.860.052.1*10-4
epidermis801.340.80.22.1*10-4
papillary dermis1501.40.90.50.02
upper blood net dermis801.390.950.60.3
reticular dermis15001.40.80.70.04
deep blood net dermis801.380.950.70.1
hypodermis60901.440.750.70.05
A seven layer skin tissue model was selected.
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Results Monte-Carlo
Melanin absorption in epidermis
Hemoglobin absorption in dermis
Baseline absorption
J/cm3
r [cm]
z [cm]
Illumination
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Results COMSOL
r [cm]
z [cm]
T [K]
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Thermal Image SimulationT [K]
x [cm]
y [
cm]
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Preliminary ResultsSelection of excitation wavelengths:
saturation evaluation is limited by skin color
5% melanin
25% melanin
15% melanin
Wavelength [nm] Wavelength [nm]
T [
K]
T [
K]
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Hemoglobin Optical Absorption
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LimitationsSolving 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.
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IntuitionExamining the shape of the temperature
function and not the values.
Wavelength [nm]
Wavelength [nm]
T [
K]
µa
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The SolutionThe 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.
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Temperature Function
T1=f1()A1
T2=f2(A1 ,)A2
T3=f3(A1 , A2 ,)A3
The absorption of each layer is affected by the absorption of upper layers
A1=Σ µi·ci
Effective absorption of layer 1
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Temperature FunctionThe temperature rise is the sum of
effective contributions of all the layers:
Each layer affects deeper layers:
The functions fi can be approximated using Taylor approximation:
( ) ( 1) ( 2) ( 3)T T layer T layer T layer
0 1 1 2 1 2 3 1 2 3( ) ,T T f A f A A f A A A
2' ''2 1 2 2 1 2 1
10 0 0
2f A f f A f A
22 1 1 2 1 3 1f A b b A b A
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Temperature FunctionComparing computational results to the
theoretical equations enables us to estimate some of the coefficients:
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For skin tissue (containing melanin):
For “internal” tissue (skin tissue without melanin):
0 1 2 3 4
1
Melanin Baseline Melanin Baseline Hemoglobin
Hemoglobin HbO Hb
T T a a a a
S S
20 1 2
2 2 23 4 5
Baseline Baseline
Baseline Hemoglobin Baseline Hemoglobin Hemoglobin
T T a a
a a a
Temperature Function
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Results of the initial guess algorithm for skin tissue with 7.5-10% melanin:
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
True saturation
Est
imat
ed s
atur
atio
nEst
imate
d s
atu
rati
on
True saturation
Results
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Results of the saturation estimation algorithm for the tissue:
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
True saturation
Est
imat
ed s
atur
atio
nEst
imate
d s
atu
rati
on
True saturation
Results
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The results of the algorithm demonstrated considerable agreement with the model’s actual oxygenation values.
RMS of the error is reasonable. Hemoglobin:9g/l10.5g/l12g/l13.5g/l15g/lTotal
2.5% melanin8%7.6%6.8%7.7%8.1%7.7%
5% melanin8.7%5.1%6.3%5.4%6.8%6.6%
7.5% melanin5.2%6.4%5.9%6.4%8.1%6.5%
10% melanin9.1%6.4%7.1%8.4%5.7%7.5%
Results
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Tumor Oxygenation ValuesTissueMedian satuationReference value
Spleen92.796
Subcutis8596-97
Gastric mucosa82.697
Uterine cervix6997
Liver42.798
Cervix cancer3-3297-98
Adenocarcinomas9-1396-97
Squamous cell carcinomas1996-98
Breast cancers2496-98
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Results of the initial guess algorithm for skin tissue without melanin, representing internal tissue:
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
True saturation
Estim
ate
d s
atu
ration
Est
imate
d s
atu
rati
on
True saturation
Results
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Results of the saturation estimation algorithm the tissue:
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
True saturation
Est
imat
ed s
atur
atio
nEst
imate
d s
atu
rati
on
True saturation
Results
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Results for skin tissue without melanin.
RMS of the error is relatively small.
Hemoglobin:9g/l10.5g/l12g/l13.5g/l15g/lTotal
0% melanin5.3%4.8%4.2%5.3%5.2%5%
Results
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The phantoms were created using various types of absorbers.
Experimental Setup
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The agar used in the phantoms simulates the thermal properties of the skin.
Experimental Setup
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Absorption spectraThe selected absorbers were
Methylene Blue, Indocyanine Green (ICG) and ink.
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Experimental SetupThe phantoms are excited by 3900s
tunable laser, pumped by Millenia Vs Laser.
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The relative intensity of the illumination is measured using an integration sphere.
Experimental Setup
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The temperature is measured by thermoVision A40 IR camera.
The experiments can be monitored using MicroMax CCD camera.
Experimental Setup
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The setup can be further simplified by using diodes and thermocouples.
Experimental Setup
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Temperature measurement
0 500 1000 1500292.6
292.8
293
293.2
293.4
293.6
293.8
294
294.2
294.4
294.6
time [sec]
T [
K]
Calibration drift
Max temperature not reached
Noisy measurements
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Temperature measurementThe temperature is estimated using a
curve fitting algorithm.
0 100 200 300 400 500 600 700292
292.2
292.4
292.6
292.8
293
293.2
293.4
150 200 250 300 350292.2
292.4
292.6
292.8
293
293.2
293.4
fit_ys vs. fit_xs
fit 1
T0
Tsat
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Intensity CalibrationCalculated using measurements with the
integration sphere
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Calibrated Measurement ResultsTemperature increase, normalized according
to intensity
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Estimated temperature function
0 1 2
(1 )B G
T T I a a
S S
01 2
T TT a a
I
a1, a2 and S are unknowns and will be estimated using the curve fitting algorithm. a1 and a2 are a function
of the materials thermal and physical properties and concentrations. S is the saturation. (ratio between ICG and Methylene Blue)
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Experimental StagesPreliminary 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
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ResultsPreliminary measurements: Five agar
models containing two materials.For each sample there are 5 measurements
and 3 unknowns.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.2
0.4
0.6
0.8
1
Real ratio
Est
imat
ed r
atio
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ResultsThe adjusted procedures were used to
measure 11 phantoms.
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ResultsPreliminary measurements of phantoms
with upper absorbing layer (simulating the epidermal layer).
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Future ResearchLayered agar phantoms with increasing
complexityAdjusting the algorithmsTissue engineered phantomsFiber bundle experimentsIn-vivo experiments
Collaboration with Rabin Medical Center
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Fiber Bundle ExperimentsInfrared imaging bundles can be used to detect tumors
in internal organs.
The bundles can be integrated to a commercially available endoscope.
900 fibers HGW
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Fiber Bundle ExperimentsA preliminary experiment with 1mm fiber
bundle was performed on an agar model.
Results are satisfying for a first experiment:
The measured signal is clearly reduced
Reference value: 100%
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Thank you..