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

interdisciplinary research involving
Interdisciplinary research involving

Medical doctors



Computer Science

Electronic Engineers



What is microwaves?

Why microwaves useful?

What microwaves used for?

use of microwaves,

applications in medical

.Research at Other Universities

Research at BAMU

why are microwaves useful
Why are microwaves useful?

They can Travel Through Various Types of Media

why are microwaves useful4
Why are microwaves useful?

Earth Observation: Radio Detection and Ranging (RADAR)

why are microwaves useful5
Why are microwaves useful?

Earth Observation: Radio Detection and Ranging (RADAR)

information from interstellar medium
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

why are microwaves useful6
Why are microwaves useful?

Telecommunications: Mobile Phones

microwave applications in medicine
Microwave Applications In Medicine

Why Use Microwaves?

Sometimes they can travel through the body

Sometimes they can heat the body

microwave applications in medicine5
Microwave Applications In Medicine

Example: Brain Temperature Monitoring

microwave applications in medicine7
Microwave Applications In Medicine

Before After adding Microwave

microwave applications in medicine8
Microwave Applications In Medicine

Example: Microwave Cancer Detection

microwave applications in medicine9
Microwave Applications In Medicine

Example: Microwave Cancer Detection

microwave breast tumor detection
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
breast tissue electrical properties
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

breast tissue dielectric spectroscopy
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
results normal breast tissue
Results: normal breast tissue

Source: Drs.Hagness & Okoniewski

radar based detection historical
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

radar based detection basic
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
utrecht hyperthermia system
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

principle of tdr
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

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


Software for TDR

Interface To Laptop


Fig.1a:Instruments and Set up to acquire data from TDR


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)


The LDA were used to classify above five known cases. The clustering obtained are shown below.