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Interdisciplinary research involving

Medical Applications of Microwaves Suresh C. Mehrotra UGC-BSR Faculty Fellow Dr.Babasaheb Ambedkar Marathwada University, Aurangabad. Interdisciplinary research involving. Medical doctors Physics Chemistry Computer Science Electronic Engineers. Outline. What is microwaves?

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Interdisciplinary research involving

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  1. Medical Applications of MicrowavesSuresh C. MehrotraUGC-BSR Faculty FellowDr.BabasahebAmbedkarMarathwada University, Aurangabad

  2. Interdisciplinary research involving Medical doctors Physics Chemistry Computer Science Electronic Engineers

  3. Outline What is microwaves? Why microwaves useful? What microwaves used for? use of microwaves, applications in medical .Research at Other Universities Research at BAMU

  4. What is Microwaves Ocean Waves

  5. What is Microwave

  6. Why are microwaves useful? They can Travel Through Various Types of Media

  7. Why are microwaves useful?

  8. Why are microwaves useful?

  9. Why are microwaves useful?

  10. Why are microwaves useful? Earth Observation: Radio Detection and Ranging (RADAR)

  11. Why are microwaves useful? Earth Observation: Radio Detection and Ranging (RADAR)

  12. Information from Interstellar Medium Microwaves received from far space gives information regarding types of molecules there H, He, Water , formaldehyde etc and also their temperayures

  13. Why are microwaves useful? Telecommunications: Mobile Phones

  14. Microwave Applications In Medicine Why Use Microwaves? Sometimes they can travel through the body Sometimes they can heat the body

  15. Microwave Applications In Medicine Why Use Microwaves?

  16. Microwave Applications In Medicine Why Use Microwaves?

  17. Microwave Applications In Medicine Example

  18. Microwave Applications In Medicine Example Cont..

  19. Microwave Applications In Medicine Example: Brain Temperature Monitoring

  20. Microwave Applications In Medicine Example:

  21. Microwave Applications In Medicine Before After adding Microwave

  22. Microwave Applications In Medicine Example: Microwave Cancer Detection

  23. Microwave Applications In Medicine Example: Microwave Cancer Detection

  24. Microwave breast tumordetection • Microwave tomography – Inverse scattering, non-linear relationship between the acquired data and imagined pattern, non-unique solution. – Early solutions - linear approximation, more recent accurate solutions based on optimization. • Ultra-wideband microwave radar techniques • Hybrid microwave – acoustic imaging

  25. Breast tissue electrical properties • Early (before 2000) published data – Are not all in agreement – Limited sample sizes and frequency ranges – Do not consistently distinguish between different normal tissue types

  26. Breast tissue Dielectric Spectroscopy • Comprehensive study to characterize malignant, benign, and normal breast tissues • U. Wisconsin-Madison (S. C. Hagness) and • U. Calgary, Canada (M. Okoniewski) • Frequencies 0.5 - 20 GHz • Total number of patients 93, samples 490; ages 17-65 • Tissue composition determined by pathologists • Normal breasts: percentage adipose, fibrous connective, and glandular

  27. Breast tissue dielectric spectroscopy

  28. Results: normal breast tissue Source: Drs.Hagness & Okoniewski

  29. Results: normal breast tissue

  30. radar-based detection - historical • 1998/1999: S. C. Hagness, A. Taflove & J. Bridges (Northwestern U.): concept proposed and demonstrated with FDTD models of planar antenna array system • 2000: E.C. Fear & M.A. Stuchly (U. Victoria): cylindrical system, skin subtraction - FDTD • Today: two main groups pursue simulations & experiments – Susan C. Hagness, U. Wisconsin – Elise C. Fear, U. Calgary – Other groups

  31. Radar-based detection - basic • Ultra-wideband pulse: modulated Gaussian or frequency contents optimized (1 - 10 GHz) • Small broadband antennas • Signal processing – Calibration: removal of the antenna artifacts – Skin surface identification and artifact removal: reduce dominant reflection from skin - various algorithms – Compensation: of frequency dependent propagation effects – Tumor detection • Basic algorithm: compute time delays from antennas to focal • point, add together corresponding signals, scan focal point • through volume • Additional complex signal processing

  32. Time space adaptive radar (TSAR):3-D localization

  33. University of Wisconsin: Results 2D

  34. University of Wisconsin: 2D Results

  35. Hyperthermia & MRI System at ZIB Berlin

  36. MRI & Sensor-measured temperature

  37. Utrecht Hyperthermia System • 3 T MRI system, RF = 128 MHz • Radio frequency within the range optimal for regional hyperthermia of abdomen • Efficient 3T MRI requires tuned antenna array instead of traditional coils • The same antenna array for hyperthermia and MRI monitoring • Water (de-ionized) bolus – Optimal power coupling & surface cooling of the patient – Shorter antennas (more elements): better control of focus and uniformity of B field in imaging – No significant effect on S/N in imaging

  38. Cancer Detection Research At BAMU using TDR

  39. Principle of TDR A fast rising (20 ps) pulse is transmitted in the sample of interest. The sample is placed in transmission line The reflected pulse is recorded Fourier Transform is used to extract the information. Experiments have been perfoemed in vitro as well as in vivo

  40. Values of permittivity, conductivity & relaxation time for the control and oral squamous cell carcinoma groups

  41. Values of permittivity, conductivity & relaxation time for the control and oral squamous cell carcinoma groups

  42. Statistical Analysis

  43. The mean permittivity and conductivity values were higher in the OSCC group as compared to the control group. The mean relaxation time value was higher in the control group as compared to the OSCC group. Statistically significant correlation was not observed between values of dielectric parameters and the different clinical stages of OSCC. The mean values of permittivity and conductivity were higher in histopathological grade II as compared to grade I. Grade I had a higher relaxation time compared to grade II. Thus, the values of dielectric parameters correlated well with the histopathological grades of OSCC and the difference was found to be extremely statistically significant (p<0.0001)

  44. The TDR Set Up

  45. Software for TDR Interface To Laptop PROBE Fig.1a:Instruments and Set up to acquire data from TDR

  46. The feature vectors p are extracted for each set of measurements. These feature vectors are used as inputs to Linear Discriminate Analysis (LDA). The measurements have been classified in three categories as follows: Category 1. Subjects with no tobacco eating habits Category 2: Subjects with tobacco eating habits Category 3: Subjects with known cases of cancer (grade -1) Category 4: Subjects with known cases of cancer (grade -2) Category 5: Subjects with known cases of cancer (grade -3)

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