We have evaluated a modified delay-and-sum (DAS) beamforming algorithm for breast cancer detection with a microwave radar-based system. The improved DAS algorithm uses an additional weight factor calculated at each focal point to improve image quality. These weights essentially represent the quality of preprocessing and coherent radar operation. Using a multistatic UWB radar system based on a hemispherical antenna array, we present experimental detection of 7 mm and 10 mm phantom tumours. We show that the new proposed DAS algorithm improves signal-to-clutter ratio in focused images by 2.65 dB for 10 mm tumour, and by 4.4 dB for 7 mm tumour.
X-ray mammography is currently the most common technique used in breast cancer screening. It employs ionising radiation, requires uncomfortable compression of the breast during the examination, and is of limited value for younger women. These limitations of mammography have resulted in research into alternative methods for imaging breast cancer.
Microwave radar-based imaging [
In radar-based imaging, the goal is to create a map of
microwave scattering, arising from the contrast in dielectric properties within
the breast. The radar approach originates from military and ground-penetrating
applications, and was proposed for breast cancer detection in the late nineties
independently by Benjamin in 1996 [
The University of Bristol team is working on multistatic ultrawideband (UWB) radar for breast cancer detection. Our radar system is based on a real (as opposed to synthetic) aperture antenna array. We have also developed a realistic 3D curved breast phantom with appropriate electrical properties. Moreover, our experimental system was built in such a way that it can be used directly with real breast cancer patients (clinical trials have been recently commenced).
We have developed a microwave radar for breast cancer
detection, based on a curved hemispherical antenna array. In this paper, we
present results obtained using a second-generation symmetrical antenna array.
The new symmetrical antenna array, shown in Figure
Symmetrical curved antenna array used in microwave radar for breast cancer detection: (a) CAD model, (b) photograph of the manufactured array. The array consists of sixteen UWB antennas populated on a section of hemisphere.
During laboratory experiments, the array is first filled
with a matching medium, the spherical skin phantom (2 mm thick) is placed in
the correct position, and then we attach a tank to the top of the antenna array
to finally fill it with a breast fat equivalent liquid [
The contrast between dielectric properties of breast
fat and tumour phantom materials is around 1 : 5. Recently published data in
[
Our radar system operates in the multistatic mode.
With sixteen antennas in the array, one hundred and twenty (120) independent
radar measurements are recorded for processing (the monostatic measurement is
not performed). Measurements are performed in the frequency domain between 3
and 10 GHz using a standard vector network analyser (VNA). All recorded radar
signals are transformed into the time domain for further signal processing
(described in Section
The first step of signal processing deals with the
extraction of the tumour response from the raw measured data. This process must
be performed before equalisation and beamforming algorithms will be applied.
When a monostatic synthetic aperture radar is used for breast cancer detection,
tumour extraction aims at removing strong skin reflection from measured data.
This is usually performed by simple subtraction from the averaged skin
reflection signal (see [
The approach we use to extract the tumour response is
different. In our multistatic real aperture array, the measured response
contains not only strong skin reflections, but also reflections from other
mechanical parts of the array as well as antenna coupling signals. All these
undesired signals are usually of greater amplitude than that of the tumour
response. To subtract all unwanted signals, we physically rotate the antenna array
around its center and perform a second radar measurement. This target
displacement method is commonly used in radar cross-section measurements
[
Rotation gives us two sets of measured data, in which
undesired signals such as antenna coupling or skin reflections are almost
identical and appear at the same time position; therefore they can be
eliminated. In contrast, a tumour response will appear at different time
position in these two measured sets (unless it is on the axis of rotation).
Applicability of this technique will depend on the homogeneity of the breast
within a given angle defined by rotation. We therefore assume that within the
angle of array rotation, (a) distance between antennas and skin remains
unchanged, (b) skin properties and thickness are the same, and (c) normal
breast tissue properties do not change. For more details about the performance
of this tumour extraction technique, please refer to [
Before applying the focusing algorithm, we have to
perform a preprocessing step. This process aims at the equalisation of
scattered tumour responses for different antenna pairs. Ideal preprocessing
would result in all received pulses being of the same shape and amplitude, and
perfectly time-aligned. In our preprocessing, the following steps are
performed: (1) extraction of the tumour response from measured data (see
[
Delay-and-sum (DAS) beamforming is a basic and
well-known method [
During the focusing, the focal point moves from one position to another within the breast, resulting in spatial beamforming. At each location, all time-shifted responses are coherently summed and integrated. Integration is performed on the windowed signal, and the length of the integration window is chosen according to the system bandwidth. A three-dimensional (3D) map of scattered energy is formed in this way. The main advantage of the DAS algorithm is its simplicity, robustness, and short computation time.
Essentially, the scattered energy at the given focal
point within the breast volume can be expressed as
The improved DAS algorithm uses an additional
weighting factor
Example of the curve of energy collection (measured data).
Next, the energy collection curve is rescaled by
normalising it to the standard deviation of energy,
In a last step, we estimate the coefficients of a
second-order polynomial (
In the following section, we will present the experimental results of phantom tumours detection, and discuss the new DAS algorithm.
This section presents the experimental results of
tumour detection using our curved antenna array and 3D breast phantom. Focusing
results for standard DAS algorithm are compared to those for the improved DAS,
and differences between both algorithms are discussed. Results are presented
for tumours of two different sizes located at different positions: (a) 10 mm
spherical tumour located at position
In Figure
Detection results of a 10 mm spherical phantom tumour:
(a) standard DAS, (b) improved DAS with
As we can see in Figure
2D focusing results for standard and improved DAS
algorithms, for different horizontal planes along the
Significantly better detection results were obtained
using improved DAS algorithm presented herein. 3D and 2D focusing results for
the improved DAS are presented in Figures
The same improved performance is observed in the 2D
results shown in Figures
Results described
above (Figures
After extraction of the tumour response from measured
data (by mechanical array rotation), resultant signals are being preprocessed
and time-aligned. This initial step is identical for standard and improved DAS
algorithms. Then, all pulsed signals (120 signals for our radar) are coherently
summed. During this process, the curve of coherent energy collection is
obtained, at each focal point within the focusing volume. This curve is
presented in Figures
Curves of energy collection at focal points
If we assume that the focused energy for tumour
location using standard DAS algorithm is equal to unity
We can see the rescaled (normalised) curves in Figures
As known in the ideal coherent summation of scattered
pulses, the energy collection curve should follow a parabola
This example presents the detection of a smaller 7 mm
spherical tumour phantom. In Figure
Detection results of a 7 mm spherical phantom tumour:
(a) standard DAS, (b) improved DAS with
As we can see in Figure
Looking at all 2D focused images (Figure
2D focusing results for standard and improved DAS
algorithms, for different horizontal planes along the
In Figure
Curves of energy collection at focal points
We observe that, after normalisation, the curves for
In this paper, we have presented a modified delay-and-sum (DAS) beamforming algorithm for breast cancer detection. The improved DAS algorithm uses an additional weight factor calculated at each focal point, to improve image quality. These weights essentially represent the quality of the preprocessing step and the coherent radar operation. Using measured data from a multistatic UWB radar system, we have presented experimental detection of 7 mm and 10 mm tumours in a phantom. We have shown that the proposed new DAS algorithm improves signal-to-clutter ratio in focused images by 2.65 dB for 10 mm tumour, and by 4.4 dB for 7 mm tumour.
Further, it may be noted that this
improvement in signal-to-clutter ratio is
comparable to that achieved [