Journal of Cancer Therapy, 2009, 1, 1-8 1
Published Online September 2009 in SciRes (www.SciRP.org/journal/cancer)
Tissue Sensing Adaptive Radar for Breast Cancer
Detection—Experimental Investigation of Simple
Tumor Models
ABSTRACT
Microwave breast cancer detection is based on differences in electrical properties between healthy and malignant tis-
sues. Tissue sensing adaptive radar (TSAR) has been proposed as a method of microwave breast imaging for early tu-
mor detection.TSARsenses all tissues in the volume of interest and adapts accordingly. Simulation results have shown
the feasibility of this system for detecting tumors of 4mmin diameter. In this paper, the second generation experimental
system for TSAR is presented. Materials with electrical properties similar to those in the breast are used for the breast
model. A resistively loaded Wu–King monopole antenna is fabricated, and reflections from the breast model over the
frequency range of 1–10 GHz are recorded. The reflected signals are processed with the TSAR algorithm, which in-
cludes improved skin subtraction and TSAR focusing algorithms. Various tumormodels are examined; specifically, a
1-cm tumor is detected with a signal-to-clutter ratio of 10.41 dB. Tumor detection with the experimental systemis
evaluated and compared to simulation results.
Keywords: breast cancer detection, experimental verification, microwave imaging, tissue sensing adaptiveradar.
1. Introduction
BREAST cancer is a significant health issue for women
and affects one in every seven women [1]. The current
method of detection is mammography, which involves
X-ray imaging of a compressed breast. X-raymammo-
graphy creates images of the density of breast tissues and
the images are used to locate suspicious areas. Although
mammography is the gold standard, concerns related to
the false-positive and false-negative rates exist [2]. There
is need for a complementary, safe, and reasonably priced
method [3]. Microwave breast cancer detection has been
introduced as a complementary method for breast cancer
detection.
Microwave breast cancer detection relies on differ-
ences in electrical properties between malignant and fatty
tissues as summarized in [4]. Microwave breast imaging
methods include hybrid, passive, and active approaches.
Hybrid methods include thermoacoustic tomography,
which uses microwaves to selectively heat tumors and
ultrasound approaches to create images [5–7]. One pas-
sive approach, microwave radiometry, measures the in-
creased temperature of the tumor compared to the normal
tissue [7–9]. Active microwave approaches include to-
mography and radar-based imaging. Microwave tomo-
graphy records the transmission of waves through the
breast and creates an electrical property map of the region
of interest [7]. Radar-based approaches, first presented by
Hagness et al. [10], involve focusing reflections from the
breast in order to determine the location of significant
scatterers (i.e., tumors). Radar-based imaging systems
include microwave imaging viaspace time (MIST)
beamforming [11,12] and tissue sensing adaptive radar
(TSAR) [13,14]. The MIST system features a woman
lying supine with the antennas scanned over the naturally
flattened breast. In the TSAR system, the woman lies
prone, the breast falls through a hole in the examination
table, and antennas are scanned around the breast. The
MIST system uses advanced clutter reduction algorithms
to create an image. Simulations have shown that the
MIST system can detect a 2-mm tumor in a two-dimensi-
onal (2-D) breast model derived from magnetic resonance
imaging [11]. The TSAR algorithm uses simple clutter
reduction methods; however, tumors of 4 mm have been
detected in a three-dimensional (3-D) cylindrical breast
model [15].
The experimental verification of each imaging system
is the next step before their clinical application. In a real-
istic system, practical issues such as antenna fabrication,
the electrical properties of breast tissues, and breast shape
must be considered. The first experimental system for
testing radar-based breast cancer detection was presented
in [16]. This system was designed for preliminary method
verification and included a polyvinyl chloride (PVC) pipe,
wood, and air to represent the breast, tumor, and fatty
tissues, respectively. The materials used had similar con-
trasts in electrical properties to those expected in the
breast. Detection of a 3-mm-diameter wooden dowel was

2 Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models
possible in a 2-D experiment. A quasi-3-D system was
presented in [17] and showed detection of a 3-D tumor in
a 2-D model. Experimental verification was presented in
[12] for the MIST system. The breast model consisted of
a printed circuit board (skin), diacetin-water solution (tu-
mor), and soybean oil (fatty tissue). The materials were
chosen based on availability, cost, toxicity, and stability.
Because the soybean oil has lower dielectric properties
than the actual fatty tissue, the materials selected for the
skin and tumor were correspondingly lower in properties.
Tumors of 4-mm diameter were detected in a 6 cm 6 cm
breast model scanned at 49 antenna locations. While this
system is more complex than the first-generation experi-
mental TSAR system, the electrical properties are again
based on the property contrasts. Therefore, the goal of the
second-generation experimental TSAR system is to use
materials with electrical properties similar to realistic
breast tissues.
This paper reports the experiments and results with the
second-generation TSAR prototype. Specifically, it ex-
pands on the preliminary results presented in [18], where
a 1-cm tumor immersed in canola oil was detected. The
aims of this paper are to test and characterize an antenna,
implement an improved TSAR algorithm, and detect tu-
mors in a realistic breast model. Section II discusses the
experimental system, including the materials used for the
breast model and antenna fabrication. The improved sig-
nal processing for the skin subtraction algorithm and the
TSAR focusing algorithm is presented in Section III. In
Section IV, the tumor detection results are presented for
various breast models. Section V draws conclusions
based on the results and outlines future work with the
experimental TSAR system.
2. Experimental System
This section describes the TSAR experimental system.
The experimental setup, equipment used, and data acqui-
sition techniques are outlined. As well, the electrical
properties of each material in the breast model are dis-
cussed. Finally, a description of the antenna fabrication is
presented.
2.1 Experimental Setup
The experimental system is shown in Figure 1. The sys-
tem is composed of a Plexiglas tank, immersion liquid,
ground plane, antenna, and breast phantom. The top of
the tank is a ground plane, which is implemented to sim-
plify the antenna design. This places limitations on the
imaging capabilities of the system; however, this setup is
similar to preliminary simulations in [19]. Therefore, it is
acceptable for preliminary experimental tests. Holes are
placed in the ground plane for placement of the antenna,
breast model, and tumors. The entire tank, with the ex-
ception of the ground plane, is fabricated without metal,
thus reducing reflections from the tank. The dimensions
are shown in Figure 2.
Figure 1. TSAR experimental system. The tank is shown on
the left and theVNA is on the right. The coaxial cable is held
ith stands to reduce flex and movement
Figure 2. Test setup with a monopole antenna, immersion
liquid, and breast phantom. (a) Top-down view. (b) Side
view
In all experiments, reflections from the antenna （S11）
are recorded with an 8719ES vector network analyzer
(VNA) (Agilent Technologies, Palo Alto, CA) connected
to a 50-Ώ coaxial cable. Data are recorded at 1601 fre-
quency points and 16 samples are averaged at each fre-
quency. The frequency range over which data are ac-
quired is from 1 to 10 GHz. In the experiments, the
phantom plate (Figure 2) is rotated in increments of 22.5°
or 45°. These rotations are performed to simulate scan-
ning the antenna around the tumor or breast model.
Reflections are recorded after each rotation.
2.2. Phantom Materials
The breast model is represented by a cylinder with a di-
ameter of 10 cm and a height of 30 cm. A hemispherical
tumor is attached to the ground plane and placed in the
breast model, as shown in Figure 2. Here, the length of
the breast model is selected such that the antenna does
not detect the end of the model (i.e., via method of im-

Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models 3
ages, this represents a 2-D model). However, the tumor is
3-D in order to provide a more challenging detection task,
as the imaging task involves detecting the 3-D tumor in a
plane perpendicular to the cylinder axis and containing
the tumor. The breast model is composed of materials
with similar electrical properties to skin, fatty tissue, and
tumors. The properties of each material are measured
using an open-ended borosilicate dielectric probe [20].
The results are summarized in Figure. 3 and Table I.
The skin is composed of a flexible silicone sheet
loaded with dielectric fillers named LDF-32 (Eccosorb)
(Emerson and CummingMicrowave Products,Randolph,
MA). The electrical properties of the skin are shown in
Figure. 3. The sheet of material is formed into a cylinder
by joining the sheet with TP-50 epoxy (Eccobond TP-50)
(Emerson and Cumming Microwave Products). This ep-
oxy has electrical properties of 4


and 0.04


S/m at 4 GHz; however, this value may vary as air mi-
crobubbles are dispersed throughout the material. The
fatty tissue is created from flour, canola oil, and 0.9%
saline [21] in a ratio by weight of 500 : 225 : 25. The
fatty tissue mixture is a dough that is packed in the inte-
rior of the skin cylinder. The electrical properties of the
fat dough (Figure 3) were monitored for a three-week
period. The electrical properties decreased by 10% as the
water evaporated. As a first approximation, the electrical
properties are selected to represent mainly the fatty tissue
inside the breast and hence have lower permittivity than
the average properties used in [10].
Tumors are fabricated using Alginate powder (Algin-
max) (Major Proditti Dentari S.P.A., Moncalieri, Italy),
water, and salt in a ratio by weight of 115:250:14 [22].
The tumors are covered with a thin layer of epoxy (Ec-
costock HiK cement) (Emerson and Cumming Micro-
wave Products). The epoxy (and S/m) creates a layer be-
tween the oil and the tumor to prevent diffusion of the
tumor in the oil. Furthermore, this conserves the electri-
cal properties (Figure 3) of the tumor. Over a three-week
period, measurements demonstrated minimal change in
properties. The thickness of the epoxy layer is difficult to
control; however, the size of the tumor is measured based
on the tumor and epoxy size combined. Therefore, as
investigated in [18], the epoxy layer reduces reflections
from the tumor, which creates a more difficult tumor de-
tection scenario. As described in [18], the tumors are at-
tached to metal plugs and inserted into the ground plane
of the tank.
An immersion liquid is needed to improve the match
between interior and exterior of the breast. Therefore, the
tank is filled with an immersion liquid of canola oil
(2.5


, 0.04

 S/m). Canola oil is similar to the
liquid investigated in [14], which provides excellent tu-
mor detection and localization. Additionally, fewer an-
tennas are required to scan a given volume than with a
higher permittivity liquid [14]. Furthermore, canola oil is
minimally dispersive over the frequency range of interest
and has low loss.
The electrical properties of the breast model at 4 GHz
are listed in Table I. The materials have a relatively small
change in permittivity over the frequency range (Figure 3)
and the changes correspond to those observed in real tis-
sues (e.g., [23]). Furthermore, the materials used to rep-
resent the breast have similar electrical properties to
those of real tissues [7].
2.3. Antenna Fabrication
The antenna used to illuminate the breast model is a re-
sistively loaded Wu–King monopole [24, 25]. The resis-
tively loaded monopole is selected as it provides accept-
able performance over the ultrawideband frequency range
of interest. The monopole has length of 10.8 mm and is
designed in a lossless liquid similar to oil with 3.0



.
The design and characterization of the antenna are out-
Figure 3. Electrical properties of materials in the breast
model. All properties are measured with an open-ended
borosilicate dielectric probe [20]. (a) Relative permittivity
and (b) conductivity as a function of frequency

4 Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models
Table 1. Electrical properties used in the experimental sys-
tem. All materials are measured with an open-ended boro-
silicate dielectric probe [20] at 4 ghz
Object Permittivity, єr Conductivity,
σ(s/m)
Canola Oil 2.5 0.04
Fatty Tissue 4.2 0.16
Skin 34.3 4.25
Tumor 43.7 6.94
Figure 4. Fabricated Wu–King monopole antenna soldered
to an SMA connector and attached to a metal plug
lined in [14] and [18]. The antenna is fabricated
using high-frequency chip resistors (Vishay 0603HF)
(Rogers RO3203 series) (Rogers Corporation, Chandler,
AZ). The substrate (3.02 

 and 0.001

 S/m) has
electrical properties similar to those of the canola oil.
The antenna is soldered to a subminiature A (SMA)
connector and attached to a metal plug. The fabricated
antenna (Figure 4) is inserted into the ground plane of the
tank.
3. Signal Processing
The signal processing includes converting signals from f-
requency to time, and the TSAR image formation algorit-
hm. Theinitial signal processing step is converting the fr-
equency-domain data to the time domain for use in the i-
mage formation algorithms. As in [16], the measured data
are weighted with a differentiated Gaussian signal with c-
enter frequency of 4 GHz and full-width half maximum
extent from 1.3 to 7.6 GHz. The data are transformed wit-
h an inverse chirp z transform to produce thetime-domain
signal.
The TSAR image formation algorithm is similar to that
of [16]; however, improvements to the skin subtraction
and focusing step are implemented. The first step is cali-
bration, which involves subtracting the reflections re-
corded without an object present. This removes clutter in
the signals, such as reflections from the Plexiglas tank.
The remaining signal contains antenna mismatch, skin
reflection, and tumor reflection. The next steps in the
TSAR algorithm are skin subtraction and focusing, dis-
cussed in this section.
3.1. Skin Subtraction
The skin subtraction algorithm previously implemented
was an adaptive correlation method named Woody aver-
aging [13,26]. This method provided effective skin su-
btraction; however, a residual skin response remained.
Therefore, an improved skin subtraction algorithm is de-
sired.
The proposed skin subtraction algorithm is based on
the recursive least squares (RLS) algorithm, which is an
adaptive filtering method. The method is adapted from a
beamformer approach in [27]. A single signal is selected
as the target signal and the remaining signals are
weighted and summed to approximate the target signal.
The desired signal is defined as or d, a
r
u1N

vector
where is the sample at time . The input signal or
remaining ignals can be defined as
()di
]T Q
i
[1
r
, 2
r
uu u,
··· r u

and is a QN

matrix where is the
number of input signals. The weight vector at time is
defined as a-
Q
()
r
Qn
n

), T w() rr wnw 1(),n w 2(n
nd is a 1Q

vector. The approximation to the desired
signal at time is defined as i
()()( ) ()
T
diyiw nui

(1)
and the error in the approximation is calculated as
()() ()eid idi

(2)
At time , the sum of the squared error is defined as
1
2
1
() ()
n
n
i
J
n 


ei
(3)
where is the forgetting factor and is the current sample n-
umber. Expanding to include the definition of results in
1
21
11
1
1
()() 2()()()
( )()()( )
n
nn
TnT
ii
n
TnT
i
J
ndiwnuid
wn uiuiwn













i
(4)
Defining
1
2
1
() ()
n
n
i
Dnd i 


 (5)
1
1
( )()()
n
nT
i
F
nuiu 


i (6)
1
1
( )()()
n
nT
i
znuidi 


 (7)
permits us to write ()
J
n as
()()2()()()() ()
TT
J
nDn wnznwnFnwn  (8)
Minimization of the mean squared error with respect to
results in the basic Wiener–Holf equation
() ()()
F
nwn zn (9)
Solving for recursively using the standard brute
force approach equates
()wn

Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models 5
()(1)()()
T
F
nFn wnun   (10)
and
()(1)() ()
T
znznwndn   (11)
However, to solve (9) requires a matrix inversion.
Therefore, the matrix inversion lemma is used to solve
for 1()
F
n
 as in[27].
This method differs from the MIST skin subtraction
approach presented in [11], as the weight vectors are up-
dated recursively after each time step. In contrast, the
method proposed in [11] has a constant weight vector,
which is shifted through the selected window.
When applied to TSAR signals, the RLS algorithm and
Woody averaging methods are combined. The RLS algo-
rithm estimates the skin response and is therefore applied
from the start of the signal to a point corresponding to the
interior of the breast. This is obtained from the skin loca-
tion and thickness estimates [28]. The Woody averaging
algorithm is applied from the interior of the breast to the
remaining portion of the signal. Therefore, signals esti-
mated with the RLS algorithm and Woody averaging are
combined, creating a total estimated signal. The total
estimated signal is subtracted from the target signal. This
process is repeated with the signal received at each anten-
na as the target signal.
Figure 5. Experimental, simulated, and theoretical VSWR
calculated from the reflection coefficient [18]
Figure 6. Transmission between two antennas (S21 )
3.2. Focusing
The skin-subtracted signals are integrated and focused
similar to [28]. The focusing is performed by identifying
a focal point inside the region bounded by the synthetic
antenna array and calculating the travel time from each
antenna to the focal point. The selected contribution from
each signal is summed and the process is repeated as the
focal point is scanned through the focusing region. The
resulting image indicates the location of significantly
scattering objects as reflections from these objectsadd
coherently [16]. To improve selectivity of focus, several
additions are made to the algorithm [29]. Mathematically,
pixel intensity at location can be described by
2
(,,) (,,,)(,,,)(,,,)
n
pi jksi jknwi jknQijkn



 (12)
The contribution from antenna ,n(,,,)
s
ijkn, is identi-
fied using the travel time between the antenna and the pi-
xel location. Weighting gives more weight
to the antennas closest to the current focal point. Signal c-
(,,,)wijk n
ompensation includes variable compensati-
(, , ,)Qi jkn
on and in-breast compensation
((,,,))vi jkn((,
tissue
dij,
,))kn (
,,,) (,,,)(,,,)
tissue
Qi jknvi jkndi jkn

(13)
In-breast compensation is the distance
traveled in breast tissue and is determined using the esti-
mated skin location. Variable compensation
has a value of zero if the pixel location is outside of the
skin, one if the location is between the skin and the array
center, and decreases as
(, , ,)
tissue
dijkn
1/
(, , ,)vi jkn

if the location is beyond
the array center, where

is the distance from the an-
tenna. The skin location is calculated as in [28]. The im-
ages are evaluated using the signal-to-clutter ratio. The
signal-to-clutter ratio is calculated as the maximum tumor
response compared to the maximum response in the same
image with the tumor response removed [28].
4. Results and Discussion
4.1. Antenna
Three antennas are fabricated with the same profile and
compared to simulations to confirm correct operation.
The impedance of the simulated antenna in [14] is con-
verted to represent a monopole. The theoretical impedan-
ce is calculated from [30]. The voltage standing wave ra-
tios (VSWRs) for the fabricated, simulated, and theoretc-
al antennas are shown in Figure 5. The results demon-
strate a good match between all fabricated antennas and
the simulated antenna. The VSWR is below 2 between
7–10 GHz. The poor VSWRs at lower frequencies are
expected as matching to 50 Ώ was not a design goal.
However, the input impedance of the antenna is relatively
constant over the frequency range of interest and the de-
sign of an impedance transformer is feasible.
Transmission between two antennas is measured by c-
onnecting an antenna to each port of the VNA and meas-
uring . The antennas are placed in the immersion
21
S

6 Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models
liquid and separated by 7 cm as this is sufficiently in the
far field of the antennas. Transmission is measured
using the same reference antenna, which is similar to the
antennas in Figure 5. The results demonstrate a transmis-
sion of approximately 38 dB at 4 GHz as shown in Figure
6. This low transmission can be attributed to the poor
VSWR and the resistive profile of the antenna. The de-
crease in transmission at higher frequencies may be in
part due to the variation in resistors at higher frequencies.
21
S
7.0
The efficiency of the antenna is calculated using defi-
nitions in [31] and finite-difference time-domain (FDTD)
simulations. Specifically, we compute the power radiated
through a closed surface surrounding the antenna and
divide this by the input power. The efficiency ranges
from 1.9% to 15.8% over the frequency range from 2 to 8
GHz. At maximum power of 5 dBm from the VNA, the
total power radiated at 4 GHz is -6.2 dBm. Although this
antenna has poor performance, it is still implemented for
tumor detection due to simplicity. Furthermore, if tumor
detection is possible with an inefficient antenna, im-
proved detection capabilities are expected with an im-
proved antenna.
4.2. Tumor Detection and Skin Subtraction
In a preliminary experiment, a tumor was placed in the
immersion liquid and rotated around the center of the
tank. Reflections were recorded at eight antenna positions
and imaged with the focusing algorithm [18]. The tumor
was clearly detected with maximum response located at
cm and cm while the actual physical
location was cm and cm. The sig-
nal-to-clutter ratios were 8.21 and 7.19 dB for the 2- and
1-cm tumors, respectively. The signal-to-clutter ratio
decreased with tumor size, as expected. The results were
promising as tumor detectionwas possible with very little
clutter [18].
7.4x6.9y
7.5 xy 
The next experiment includes the skin and the interior
of the skin filled with canola oil. The two skin subtraction
algorithms, Woody averaging and the RLS–Woody com-
bination, are applied to the data and the skin response is
compared before and after skin subtraction. Figure 7
demonstrates the effectiveness of the RLS algorithm
compared to the Woody averaging results. The
peak-to-peak of the skin response is calculated prior to
and after skin subtraction, and the ratio of these quantities
is calculated.
Here, the peak-to-peak results are -26.33 and -107.87
dB for Woody averaging and the RLS algorithm, respec-
tively. The skin-subtracted data recorded at 15 antenna
positions are focused using the TSAR focusing algo-
rithm.
The signal-to-clutter ratios for a 1-cm tumor are calcu-
lated to be 6.63 dB forWoody averaging and 14.37 dB for
the RLS–Woody combination. These results further
demonstrate the effectiveness of the RLS–Woody com-
bination, which is selected as the skin subtraction method
for the remainder of this paper.
Figure 7. Plot of a single skin-subtracted response using the
Woody averaging method and the RLS algorithm. This
shows the effectiveness of the RLS skin subtraction method
Figure 8. Image of 1-cm tumor in the complete breast model.
The white circle is a postprocessing step to illustrate the
actual location of the skin cylinder. The exterior line repre-
sents the antenna locations. Strongly scattering objects are
indicated with lighter intensities while darker pixels indicate
areas of weak scattering
4.3. TSAR Images
The final experiment is the most complex and includes
the skin, tumor, and fatty tissue. Reflections are recorded
and the TSAR algorithm is applied to the data. The re-
sul-ts are plotted in Fig. 8 for a 1-cm tumor. The tumor is
detected with maximum response located at 8.1x

cm
and 6.9y

cm, while the physical location is
8.0x

cm and 7.0y

cm. The signal-toclutter ratios
are 13.74 and 10.41 dB for the 2- and 1-cm tumors, re-
spectively. To confirm and compare the results, simula-
tions are performed with the FDTD method [32]. The
simulation setup is as in [14]; however, the breast interior
is homogeneous, and material properties are the same as
discussed in Section 2. The recorded reflections from the
simulations are focused, and the signal-to-clutter ratio for
the 1-cm tumor is 23.38 dB. As expected, the sig-
nal-to-clutter ratio is higher for simulations than experi-

Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models 7
Table 2. Signal-to-clutter ratios for each breast model
Figure 9. Signal-to-clutter ratio received when changing the
permittivity in the TSAR focusing algorithm. The highest
signal-to-clutter ratios occur when the permittivity value
used for calculation is the same as the measured value
ments. Simulations provide a relatively noise-free envi-
ronment and a homogeneous breast interior. In addition,
oscillations are introduced in the measured signal when
converting from the frequency to the time domain, as the
weighting function is band limited.
The signal-to-clutter ratios for each of the three ex-
periments are shown in Table II. As expected, the sig-
nal-to-clutter ratio decreases as the tumor size decreases.
The tumor-only simulations have low signal-to-clutter
ratios compared to the skin tumor case as only eight ante-
nna positions are used to create an image. Finally, as the
complexity of the system increases, the signal-to-clutter
ratios decrease.
These results are promising, as tumor detection is pos-
sible using an inefficient antenna to record the reflections.
Therefore, with an improved antenna, improved tumor
detection capability is anticipated.
Finally, the robustness of the algorithm to changes in
breast permittivity is tested. The permittivity of the fatty
tissue in the interior of the breast is varied from 3 to 6 in
the postprocessing algorithm. This variation changes the
travel time from the antenna to focal point. The sig-
nal-to-clutter ratios are calculated as the permittivity val-
ues are changed in 0.1 increments and shown in Figure 9.
The results indicate that, for small tumors, the largest
signal-to-clutter ratio is obtained when the electrical
properties are similar to the measured value of the fatty
tissue. This demonstrates the necessity of prior informa-
tion on the electrical properties inside the breast to
achieve the highest signal-to-clutter ratio. In the case of
inhomogeneous breast tissue, an average permittivity
value is necessary to compute the travel time through the
breast tissue. Algorithms for estimating the average per-
mittivity in the breast have been introduced in [33]. With
an estimate of the average properties, the focusing algo-
rithms described here have detected tumors in simple
breast models containing variations in properties of 10%
[14].
5. Conclusions
This paper presents the second-generation TSAR ex-
perimental system. The materials used to represent the
breast and the corresponding electrical properties were
presented. The RLS algorithm was introduced as an im-
proved skin subtraction algorithm compared to the pre-
vious method of Woody averaging. Tumor detection and
localization were possible in three breast phantoms of
increasing complexity. These results are promising as the
materials used to represent the breast have similar elec-
trical properties to those in a realistic breast. Furthermore,
this is the most complex breast model currently under
investigation for radar-based tumor detection. The tumors
examined here are larger in diameter than the 4-mm tu-
mors detected with a 3-D model reported in [14]. Testing
detection of smaller tumors is planned with a 3-D system.
Although promising results are obtained with this
simpl system, experiments in 3-D are not performed.
Currently,the antenna is the limiting factor for 3-D ex-
periments. An improved antenna is under development,
and improved performance and directivity are expected.
This should increase the tumor detection capabilities of
the TSAR system. Another challenge is the development
of a 3-D breast model, including more realistic shape and
inhomogeneities. The materials and methods reported
here may be used to develop this realistic model. Finally,
a scanning system must be developed to scan the im-
proved antenna around the new model. This may involve
a vertical scan in addition to the rotation of the model
reported here. Therefore, this paper with a simple model
provides a foundation for expansion to a 3-D system.
6. Acknowledgments
The authors would like to acknowledge the technical
support of S. Foster, I. Choi, F. Hickli, and J. Shelley, all
of the University of Calgary, Calgary, AB, Canada.
REFERENCES
[1] American Cancer Society, “Cancer facts and figures
2005,” Amer. Cancer Soc,, Atlanta, GA, 2005.
[2] Mammography and Beyond: Developing Technologies
for the Early Detection of Breast Cancer. Washington, DC:
Inst. Med., Nat. Academy Press, 2001.
[3] Saving Women’s Lives: Strategies for Improving Breast
Cancer Detection and Diagnosis. Washington, DC: Inst.
Med., Nat. Academy Press,2004.
[4] E. C. Fear, “Microwave imaging of the breast,” Technol.
Cancer Res. Treat., vol. 4, no. 1, pp. 69–82, Feb. 2005.
Tumor
Size
(cm)
Tumor
Only
(dB)
Skin-Tumor
(dB)
Skin-Fat-Tumor
(dB)
2
1
8.21
7.19
14.51
14.37
13.74
10.41CCCC

8 Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models
[5] R. A. Kruger, K. K. Kpoecky, A. M. Aisen, D. R.
Reinecke, G. A. Kruger, and W. L. Kiser Jr., “Ther-
moacoustic CT with radio waves: A medical imaging
paradigm,” Radiology, vol. 211, no. 1, pp. 275–278, 1999.
[6] L. V. Wang, X. Zhao, H. Sun, and G. Ku, “Micro-
wave-induced acoustic imaging of biological tissues,”
Rev. Sci. Instrum., vol. 70, no. 9, pp. 3744–3748, 1999.
[7] E. C. Fear, S. C. Hagness, P. M. Meaney, M. Okoniewski,
and M. A. Stuchly, “Enhancing breast tumor detection
with near-field imaging,” IEEE Micro, vol. 3, no. 1, pp.
48–56, Mar. 2002.
[8] S. Mouty, B. Bocquet, R. Ringot, N. Rocourt, and P. De-
vos, “Microwave radiometric imaging for the characteri-
zation of breast tumors,” Eur. Phys. J.: Appl. Phys., vol.
38, pp. 73–78, 2000.
[9] K. L. Carr, P. Cevasco, P. Dunlea, and J. Shaeffer, “Ra-
diometric sensing: An adjuvant to mammography to de-
termine breast biopsy,” in IEEE MTT-S Int. Microwave
Symp. Dig., Boston, MA, Jun. 2000, pp. 929–932.
[10] S. C. Hagness, A. Taflove, and J. E. Bridges,
“Two-dimensional FDTD analysis of a pulsed microwave
confocal system for breast cancer detection: Fixed-focus
and antenna-array sensors,” IEEE Trans. Biomed. Eng.,
vol. 45, no. 12, pp. 1470–1479, Dec. 1998.
[11] E. J. Bond, X. Li, S. C. Hagness, and B. D. Van Veen,
“Microwave imaging via space time beamforming for
early detection of breast cancer,” IEEE Trans. Antennas
Propag., vol. 51, no. 8, pp. 1690–1705, Aug. 2003.
[12] X. Li, S. K. Davis, S. C. Hagness, D. W. van der Weide,
and B. D. Van Veen, “Microwave imaging via space time
beamforming: Experimentalinvestigation of tumour de-
tection in multilayer breast phantoms,” IEEE Trans. Mi-
crow. Theory Tech., vol. 52, no. 8, pp. 1856–1865, Aug.
2004.
[13] E. C. Fear and J. Sill, “Preliminary investigations of tissue
sensing adaptive radar for breast tumour detection,” in
Proc. Engineering Medicine and Biology Society, Cancun,
Mexico, Sep. 2003, pp. 3787–3790.
[14] J. M. Sill and E. C. Fear, “Tissue sensing adaptive radar
for breast cancer detection: A study of immersion liquid,”
Electron. Lett., vol. 41, no. 3, pp. 113–115, Feb. 2005.
[15] J. M. Sill, T. C.Williams, and E. C. Fear, “Tissue sensing
adaptive radar for breast tumour detection: Investigation
of issues for system implementation,” in Int. Zurich Elec-
tromagnetic Compatibility Symp., Zurich, Switzerland,
Feb. 2005, pp. 71–74.
[16] E. C. Fear, J. Sill, and M. A. Stuchly, “Experimental fea-
sibility study of confocal microwave imaging for breast
tumor detection,” IEEE Trans. Microw. Theory Tech., vol.
51, no. 3, pp. 887–892, Mar. 2003.
[17] , “Experimental feasibility of breast tumor detection and
localization,” in IEEE MTT-S Int. Microwave Symp. Dig.,
Philadelphia, PA, Jun. 2003, pp. 383–386.
[18] J. M. Sill and E. C. Fear, “Tissue sensing adaptive radar
for breast cancer detection: Preliminary experimental re-
sults,” in IEEE MTT-S Int. Microwave Symp. Dig., Long
Beach, CA, Jun. 2005. [CD ROM].
[19] E. C. Fear and M. A. Stuchly, “Microwave system for
breast tumor detection,” IEEE Microw. Guided Wave
Lett., vol. 9, no. 11, pp. 470–472, Nov. 1999.
[20] D. M. Hagl, D. Popovic, S. C. Hagness, J. H. Booske, and
M. Okoniewski, “Sensing volume of open-ended coaxial
probes for dielectric characterization of breast tissue at
microwave frequencies,” IEEE Trans. Microw. Theory
Tech., vol. 51, no. 4, pp. 1194–1206, Apr. 2003.
[21] J. J.W. Lagendijk and P. Nilsson, “Hyperthermia dough:
A fat and bone equivalent phantom to test micro-
wave/radio frequency hyperthermia heating systems,”
Phys. Med. Biol., vol. 30, no. 7, pp. 709–712, 1985.
[22] X. Yun, E. C. Fear, and R. Johnston, “Compact antenna
for radar-based breast cancer detection,” IEEE Trans. An-
tennas Propag., vol. 53, no. 8, pp. 2374–2380, Aug. 2005.
[23] K. R. Foster and H. P. Schwan, “Dielectric properties of
tissues and biological materials: A critical review,” Crit.
Rev. Biomed. Eng., vol. 17, no. 1, pp. 25–104, 1989.
[24] T.Wu and R. King, “The cylindrical antenna with nonre-
flecting resistive loading,” IEEE Trans. Antennas Propag.,
vol. AP-13, no. 3, pp. 369–373, May 1965.
[25] , “Corrections to ‘The cylindrical antenna with nonre-
flecting resistive loading’,” IEEE Trans. Antennas
Propag., vol. AP-13, no. 11, p. 998, Nov. 1965.
[26] C. D. Woody, “Characterization of an adaptive filter for
the analysis of variable latency neuroelectric signals,”
Med. Biol. Eng., vol. 5, no. 6, pp. 539–553, 1967.
[27] S. Haykin, Adaptive Filter Theory. Upper Saddle River,
NJ: Prentice- Hall, 1996.
[28] E. C. Fear, X. Li, S. C. Hagness, and M. A. Stuchly,
“Confocal microwave imaging for breast cancer detection:
Localization of tumors in three dimensions,” IEEE Trans.
Biomed. Eng., vol. 49, no. 8, pp. 812–822, Aug. 2002.
[29] J. M. Sill, “Second generation experimental system for
tissue sensing adaptive radar,” M.S. thesis, Univ. Calgary,
Calgary, AB, Canada, 2005.
[30] J. G. Maloney and G. S. Smith, “A study of transient ra-
diation from the Wu–King resistive monopole—FDTD
analysis and experimental measurements,” IEEE Trans.
Antennas Propag., vol. 41, no. 5, pp. 668–676,May 1993.
[31] C. A. Balanis, Antenna Theory: Analysis and Design.
New York: Wiley, 1997.
[32] A. Taflove and S. C. Hagness, Computational Electrody-
namics: The Finite Difference Time Domain Method.
Boston, MA: Artech House, 2000.
[33] D. W. Winters, E. J. Bond, S. C. Hagness, and B. D. Van
Veen, “Estimation of average breast tissue properties at
microwave frequencies using a time-domain inverse scat-
tering technique,” in Int. Zurich Electromagnetic Com-
patibility Symp., Zurich, Switzerland, Feb. 2005, pp.
59–64.