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.

Introduction Absorption Radiative relaxation Thermal relaxation Metastable state Chemical reaction Sample heating Temperature change Density change Pressure change Chemical change

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 COHERENT WAVEGUIDE BUNDLE TISSUE LASER THERMAL CAMERA ENDOSCOPE OPTICAL FIBER

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 Tissue-like-phantoms experiments Tissue engineered phantoms experiments In-vivo experiments WE ARE HERE

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:

Skin Tissue Model Blood%H2O%gnThickness 2.1* stratum corneum 2.1* epidermis papillary dermis upper blood net dermis reticular dermis deep blood net dermis hypodermis A seven layer skin tissue model was selected.

Results Monte-Carlo Melanin absorption in epidermis Hemoglobin absorption in dermis Baseline absorption J/cm 3 r [cm] z [cm] Illumination

Results COMSOL r [cm] z [cm] T [K]

Thermal Image Simulation T [K] x [cm] y [cm]

Preliminary Results Selection of excitation wavelengths: saturation evaluation is limited by skin color 5% melanin 25% melanin 15% melanin Wavelength [nm] T [K]

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 not the values. Wavelength [nm] T [K] µaµa

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  T 1 =f 1 (  )  A 1  T 2 =f 2 (A 1,  )  A 2  T 3 =f 3 (A 1, A 2,  )  A 3 The absorption of each layer is affected by the absorption of upper layers A 1 =Σ µ i ·c i Effective absorption of layer 1

Temperature Function The temperature rise is the sum of effective contributions of all the layers: Each layer affects deeper layers: The functions f i can be approximated using Taylor approximation:

Temperature Function Comparing computational results to the theoretical equations enables us to estimate some of the coefficients:

For skin tissue (containing melanin): For “internal” tissue (skin tissue without melanin): Temperature Function

Results of the initial guess algorithm for skin tissue with % melanin: Estimated saturation True saturation Results

Results of the saturation estimation algorithm for the tissue: Estimated saturation True saturation Results

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% melanin 8%7.6%6.8%7.7%8.1% 7.7% 5% 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% Results

Tumor Oxygenation Values TissueMedian satuationReference value Spleen Subcutis Gastric mucosa Uterine cervix 6997 Liver Cervix cancer Adenocarcinomas Squamous cell carcinomas Breast cancers

Results of the initial guess algorithm for skin tissue without melanin, representing internal tissue: Estimated saturation True saturation Results

Results of the saturation estimation algorithm the tissue: Estimated saturation True saturation Results

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

The phantoms were created using various types of absorbers. Experimental Setup

The agar used in the phantoms simulates the thermal properties of the skin. Experimental Setup

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.

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. Experimental Setup

Temperature measurement Calibration drift Max temperature not reached Noisy measurements

Temperature measurement The temperature is estimated using a curve fitting algorithm. T0T0 T sat

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. 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)

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.

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. Results are satisfying for a first experiment: The measured signal is clearly reduced Reference value: 100%

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