1. Michal Tepper Under the supervision of Prof. Israel Gannot

2. 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.

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

4. 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

5. 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.

6. The Method COHERENT WAVEGUIDE BUNDLE TISSUE LASER THERMAL CAMERA ENDOSCOPE OPTICAL FIBER

7. 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

8. 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

9. 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:

10. Skin Tissue Model Blood%H2O%gnThickness 2.1*10 -4 0.050.861.520stratum corneum 2.1*10 -4 0.20.81.3480epidermis 0.020.50.91.4150papillary dermis 0.30.60.951.3980upper blood net dermis 0.040.70.81.41500reticular dermis 0.10.70.951.3880deep blood net dermis 0.050.70.751.446090hypodermis A seven layer skin tissue model was selected.

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

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

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

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

15. Hemoglobin Optical Absorption

16. 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.

17. Intuition Examining the shape of the temperature function and not the values. Wavelength [nm] T [K] µaµa

18. 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.

19. 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

20. 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:

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

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

23. Results of the initial guess algorithm for skin tissue with 7.5-10% melanin: Estimated saturation True saturation Results

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

25. 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

26. Tumor Oxygenation Values TissueMedian satuationReference value Spleen 92.796 Subcutis 8596-97 Gastric mucosa 82.697 Uterine cervix 6997 Liver 42.798 Cervix cancer 3-3297-98 Adenocarcinomas 9-1396-97 Squamous cell carcinomas 1996-98 Breast cancers 2496-98

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

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

29. 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

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

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

32. Absorption spectra The selected absorbers were Methylene Blue, Indocyanine Green (ICG) and ink.

33. Experimental Setup The phantoms are excited by 3900s tunable laser, pumped by Millenia Vs Laser.

34. The relative intensity of the illumination is measured using an integration sphere. Experimental Setup

35. The temperature is measured by thermoVision A40 IR camera. The experiments can be monitored using MicroMax CCD camera. Experimental Setup

36. The setup can be further simplified by using diodes and thermocouples. Experimental Setup

37. Temperature measurement Calibration drift Max temperature not reached Noisy measurements

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

39. Intensity Calibration Calculated using measurements with the integration sphere

40. Calibrated Measurement Results Temperature increase, normalized according to intensity

41. 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)

42. 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

43. Results Preliminary measurements: Five agar models containing two materials. For each sample there are 5 measurements and 3 unknowns.

44. Results The adjusted procedures were used to measure 11 phantoms.

45. Results Preliminary measurements of phantoms with upper absorbing layer (simulating the epidermal layer).

46. 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

47. 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

48. 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%

49. Thank you..