# Microwave Tomography for Brain Imaging: Feasibility Assessment for Stroke Detection

Authors: Serguei Y. Semenov; Douglas R. Corfield
Journal: International Journal of Antennas and Propagation
 (2008)

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## Abstract

There is a need for a medical imaging technology, that supplements current
clinical brain imaging techniques, for the near-patient and mobile assessment of cerebral
vascular disease. Microwave tomography (MWT) is a novel imaging modality that has
this potential. The aim of the study was to assess the feasibility, and potential
performance characteristics, of MWT for brain imaging with particular focus on stroke
detection. The study was conducted using MWT computer simulations and 2D head
model with stroke. A nonlinear Newton reconstruction approach was used.
The MWT imaging of deep brain tissues presents a significant challenge, as the
brain is an object of interest that is located inside a high dielectric contrast shield,
comprising the skull and CSF. However, high performance, nonlinear MWT inversion
methods produced biologically meaningful images of the brain including images of
stroke. It is suggested that multifrequency MWT has the potential to significantly
improve imaging results.


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## Body

## 1. Introduction

A healthy brain  requires an adequate blood supply. A
stroke or “brain attack” compromises cerebral blood flow (CBF) leading to brain
injury. This brain injury can lead to
death or permanent loss of function and disability. Approximately 700 000 people, each year, will
experience a stroke in the US;
in 2004, stroke accounted for 1 in every 16 US deaths [1].The brain is
particularly vulnerable to disturbances in blood flow as it contains no
endogenous stores of energy; it is dependent upon a continuous and
sufficient level of blood flow for the constant replenishment of oxygen and
glucose and for the removal of waste products.
Therefore, CBF is tightly regulated to meet the brain’s metabolic needs;
local changes in cerebral metabolism are associated with local changes in
CBF. Indeed this close coupling of
metabolism and flow is the basis for functional brain imaging techniques such
asH2O15 positron emission
tomography (PET) and blood oxygen level dependent functional magnetic
resonance imaging (FMRI). In addition to the metabolic coupling of
CBF to metabolism, an intrinsic autoregulatory mechanism maintains
a constant level of blood flow despite fluctuations in arterial
blood pressure across a wide physiological ranges;
this protects the cerebral circulation from potentially harmful
changes in perfusion pressure. Thus, the normal
regulation of cerebral perfusion depends on a complex interaction of
metabolism, circulation, and respiration which is perturbed by
pathologies such as stroke.Acute ischemic strokes account for about 85% of all strokes;
each begins with a blood clot (thrombus) forming in the circulation
at a site distant from the brain. The clot breaks away from this distant
site forming an embolus which then travels through the circulation;
on reaching the brain, the embolus lodges in the small vessels
interrupting blood flow to a portion of brain tissue. With this
reduction in blood flow, tissue damage quickly ensues.
Clinical management of stroke has been enhanced by the use of
thrombolytics (clot busters) combined with the application of brain
imaging techniques that reveal the pathophysiological changes in
brain tissue that result from the stroke. In particular, the clinical
decision, to use a thrombolytic, must be made within 3 hours of
the onset of symptoms and requires a firm diagnosis of an ischemic
stroke [2]. This clinical decision
relies on imaging methods such as computed tomography (CT) and MRI
to reliably determine ischemic perfusion changes.
Subsequent management of the stroke is enhanced by imaging the extent of the area of brain tissue
with compromised blood flow [3]. Current clinical
imaging methods, including CT, PET, and MRI each offers useful information on
tissue properties related to perfusion, ischemia, and infarction
[3]. Whilst
each of these methods has its own advantages, none currently offers a rapid or
cost effective imaging solution that can be made widely available at the
“bedside” in the emergency department or to first response paramedical services.
Microwave tomography (MWT) might present a
safe, portable, and cost-effective supplement to current imaging modalities for
acute and chronic assessment of cerebral vascular diseases including stroke.With microwave
imaging, tissues are imaged based on differences in their dielectric
properties. It has been demonstrated that tissue malignancies, blood supply,
hypoxia, acute ischemia, and chronic infarction
[4–9] change tissue dielectric
properties. Therefore, MW imaging offers the potential for the diagnosis of
functional and pathological tissue conditions, including perfusion and
perfusion-related injuries. MW imaging of breast malignancies has been
demonstrated [8, 10–12].
Perfusion-related tissue injuries have been imaged using MWT in excised
canine hearts [13] and in simulated extremities
[9]. MWT of biological objects
possesses very complicated problem of so-called diffraction tomography
[14]. A high dielectric contrast between
tissues with high water content (e.g., muscle tissue) and low water content
(e.g., bone) presents an additional complication for MWT imaging. Various
approaches in two-dimensional (2D) and three-dimensional (3D) geometries,
using scalar and vector approximations, have been developed recently
[15–25]. We have shown that experimental MWT imaging of
high dielectric contrast objects is possible using nonlinear Newton
and multiplicative regularised
contrast source inversion (MR-CSI) methods [24].
MWT imaging of the brain presents a significant challenge, as the brain
is an object of interest that is located inside a high dielectric contrast shield,
comprising the skull (with low dielectric contrast
(ε∼10–15)
and cerebral spinal fluid (with high
ε∼55–60).
The aims of this project are: (i) to determine the
optimal technical characteristics of an MWT brain imaging device and (ii) to
assess the feasibility and potential performance characteristics of MWT for
brain imaging with a particular focus on stroke detection. The methods and
modeling approaches are described in Section 2; the results are presented and
discussed in Section 3.


## 2. Methods

The aims of the study were accomplished using computer simulations of MWT
imaging of a 2D head model. The model is presented in Figure1. The dielectric properties of the
regions of normal head model, taken from published data
[26–29], are summarised
in Table 1. In further developing this model,
to incorporate a region of acutely simulated stroke injury, we used previously
obtained tissue perfusion data [4, 5, 9].
The acute stroke injury was simulated as −10 contrast (to
white matter) circle with diameter
1, 2, or 4 cm. Further simulations were conducted using
two 2D models of a head, first, with normal brain blood flow and, second, with
compromised blood flow due to simulated stroke (see Figure
1).Table 1
Dielectric
properties of the head model at 1 GHz (see Figure1).
RegionMatching solutionSkinSkullCSFGrey matterWhite matterStroke areaThickness5 mm7 mm3 mmNo.1234567Dielectric properties40+j1340+j1113+j257+j2650+j1840+j1536+j13Figure 1
Simulated 2D model
of a head inside of the MWT imaging chamber with a radius of 11 cm.Transmitters and
receivers (positioned equidistantly) were located on the outer ring of the
working chamber with a radius of 11 cm. The overall number of
transmitters (Ntr)
and receivers (Nrec) was 32×32 or 64×64. In general cases, the more sources/receivers that are used,
the better quality of reconstructed images is
expected. However, an increased number of antennas will add additional
technical obstacles, such as an increase of data acquisition time, problem
related to the construction of small, efficient antennas for 0.5–2.0 GHz,
and so forth. See discussion following Table 3
for further details. To simulate an MWT imaging procedure, the object under the
study was irradiated from jth transmitter and scattered electromagnetic (EM) field was measured on Nrec/2 opposite receivers. This was
continued for each transmitter from 1 to Ntr. In some series of
simulations, a random noise was added to received complex EM signal. The
sources of EM radiation were simulated as unlimited strings over the main axis
(z-axis) of the 2D model. Of course, this source model together with an overall
2D approach has limited practical application. However, the model does allow
assessing the feasibility of the technology. In practical cases, we proved that
a dipole model is a good approximation of ceramic loaded waveguide antennas
used in our previously built systems [30–32]. The direct problem was solved on
a polar grid system with uniform mesh (512 over angle ×256 over radius) using
an approach presented elsewhere [15].Image
reconstruction was performed using the Newton
approach, presented elsewhere [15]. Within
this approach, we used a polar mesh with 256 (angle) ∗ 128 (radius)
grids for solution of the direct problem and a Cartesian mesh with
64∗64
grids for inverse problem, with various regularisation
parameters. Regularisation parameters were chosen by a trial method.
Two reconstruction schemes were used: single
frequency and multi (dual)-frequencies. Within single frequency schemes,
the image reconstruction was started with a homogeneous background medium of
matching solution, therefore, no a priori information taken into account.
Within the multifrequency schemes there was a sequential chain of
reconstructions at each frequency. An initial reconstruction was started from a
homogeneous background medium using scattered EM fields obtained at the 1st
frequency, while, at the sequential step(s) we used different frequencies (with
corresponding scattered EM fields obtained at that frequencies) and
started from the results of
reconstruction obtained at previous step(s). This procedure was performed using
different frequencies from 0.5 GHz to 2.0 GHz
At this stage, the frequency dispersion of
dielectric properties of the various tissues was not taken into account. The
potential impact of this assumption is discussed in the next section.


## 3. Results and Discussion

The technical
performance of MWT brain imaging approach was initially assessed over a
frequency range from 0.5 GHz to 2.5 GHz using the model and
direct problem solver. The ultimate goal is to develop microwave tomographic
technology with the best sensitivity and specificity, and with high temporal
and spatial resolution, for the noninvasive assessment of brain tissues.
The best spatial resolution can be achieved at high frequencies. However,
the attenuation of EM radiation in biological media is in inverse ratio with
the frequency, with decreasing signal-to-noise ratio (SNR) at high frequencies.
Therefore, the strategy is to find the highest possible frequency at which
receivers will still be able to detect signal with reliable SNR and will not
compromise temporal resolution. Using our MWT simulation approach, together
with the model of the head (see Figure1),
we estimated an overall signal attenuation summarised
in Table 2. The results should be taken
as a guidance or initial estimation, which does not take into account
dispersion of tissue dielectric properties, any particulars of head geometry, and
so forth.Table 2
Projected signal
attenuation within tomographic imaging procedure of human head.
Frequency [GHz]0.51.02.0Attenuation [dB]−58−100−156Table 3
Characteristics and projected
performance of an initial MWT system for brain imaging.
MW frequencies0.7–1.0 GHzImaging chamber3D “Helmet” like,
withR∼11cmNumber of antennas per a 2D “slice” of an imaging chamber32–64Type of antennasceramic (ε∼60–90) loaded waveguidesEach antenna works as transmitter and receiverYesMeasured attenuations [dB]60–110S/N ratio [dB]40–60IFBW [kHz]1–10Output power on antenna+20 dBmAs can be seen, the
attenuation is very high at frequencies above 1 GHz-2 GHz
range. As it is highly desirable: (i) to achieve a good SNR ratio (within a range of 40–60 dB)
for biological detection reasons, such as sensitivity, specificity, and resolution
and (ii) to not increase data acquisition time for measuring highly
attenuated signals, which compromises an expected very attractive
time resolution (within msec range) in order to detect circulated
gated tissue changes, we suggest that frequencies
within 0.5 to 1.0 GHz might be an optimal for brain imaging.
An additional expected advantage, of using this low portion of
microwave spectrum, is that acute perfusion related changes in tissue
dielectric properties are more pronounced at low frequencies
[4, 5]. This choice might unfavorably affect
spatial resolution in its classical, far-EM-field sense. However, there is
potential to improve spatial resolution, even to obtain a super-resolution in
near-EM-field using nonlinear inversion [33, 34].We further assessed
the potential resolution of the technology to detect acute “stroke-like” areas
with−10%
contrast in dielectric properties. It has to be noted here that this
is different from the classical spatial resolution definition, which is defined
as a minimal distance (using Raleigh or half-height criteria) at
which two small similar inhomogeneities can be distinguished between
each other. Previously, we conducted such studies and experimentally
achieved a 7–9 mm spatial resolution at 0.9 GHz
[35]. In this study, the aim was to understand
what was the smallest size of brain inhomogeneity, with a particular dielectric
contrast, that could potentially be detected. In our previous MWT imaging
studies, we suggested that changes in about 1% in amplitude and about 1 degree
in phase of the received EM signal could be confidently detected and
corresponding alterations in dielectric properties could be successfully
reconstructed. We simulated MWT data acquisition for brain models with and
without stroke areas of different size and then averaged differences in
received EM signals over all receivers for each of transmitter position. The
averaged differences in EM signals at 1 GHz for normal brain,
and brain with stroke, were about: 3.8% in amplitude and 5.5° in phase for
4 cm diameter stroke, 1.2% and 2°
for 2 cm and 0.3% and 0.2° for
1 cm correspondingly.
Therefore, it is suggested that, at this level of the development of MWT
imaging technology, the smallest imaginable area of acute stroke is estimated
to be about 2 cm in diameter. This resolution
might not compare with the one achieved by other imaging modalities, such as MRI or CT. However, all
performance factors should be considered together. Excellent temporal
resolution will add a novel diagnostic dimension. Cost efficiency, mobility,
and safety are other significant factors which suggest potential advantages of
MWT for brain imaging.An MWT imaging cycle was simulated as described in the method section. The results of the first series of imaging
experiments are presented in Figure2 for 1 GHz
frequency for the  32×32
transmitters × receivers case. The absolute values of the reconstructed
dielectric properties of (a) the normal brain image can be compared with (b) those
reconstructed properties for the stroke case. The reconstructed profile through
the stroke area of a radius 2 cm located at X=−4 cm
and Y=0 cm is presented in
(c) as % difference in reconstructed values between normal and stroke cases. The
shadow of the stroke area can be easy appreciated from the reconstructed image
(b). Furthermore, the reconstructed differential profile (c) clearly indicates
an area of dielectric inhomogeneity (stroke) in terms of both the geometrical
position and the absolute values of the reconstructed dielectric properties, as
evidenced by the proximity of the reconstructed profile (line in c) to the
expected simulated profile (dots in c).Figure 2
Reconstructed MWT
images of simulated brain model: (a) normal and (b) with a stroke injury with
radius 2 cm centered at{−4,0}, (c) the
reconstructed differential profile [% difference] through the stroke
area. Noiseless case. Frequency 1 GHz.Next, we focused on
the MWT imaging performance at different frequencies with 1% noise.
This noise figure does require a good performance of both MWT
imaging hardware and the overall MWT imaging reconstruction protocol but is
achievable in practice. We used the brain model, with the stroke area of a radius
2 cm located at{−4;0}.
The imaging results are presented in
Figure 3 for frequencies (a) 0.5 GHz,
(b) 1.0 GHz, and (c) 2.0 GHz. The area with
suspected stroke injury is circled in white. The stroke injury area
failed to be reconstructed when a high frequency (2 GHz)
is used alone. This unsuccessful
imaging result might be attributed to (i) a very high attenuation of EM field
at this frequency (see Table 2) and/or to (ii) weaknesses of used imaging
approach. However, the used imaging algorithm based on the Newton
approach has previously shown a good
imaging performance, which is comparable with other powerful, recently
developed nonlinear methods of MWT, such as gradient method and contrast source
inversion method [15, 24].
An area of stroke injury was reconstructed when MWT
imaging was performed at lower frequencies (0.5–1.0 GHz),
with more pronouncing detection at
1 GHz (b).Reconstructed MWT
images of simulated brain model with a stroke injury with radius 2 cm
located at{−4,0}
obtained at frequencies (a) 0.5 GHz, (b) 1.0 GHz,
and (c) 2.0 GHz. 1% noise.
Area with suspected stroke injury is circled in white.
(a)(b)(c)A multifrequency
approach further improved the imaging results. Images, obtained at 0.5 GHz
and 2.0 GHz, were used as a starting point (initial guess)
for further data inversion at 1 GHz. Corresponding scattered
EM fields, obtained at individual frequencies, were used. At this
stage, the frequency dispersion of dielectric properties of various
tissues was not taken into account, that is, we used the
same dielectric parameters of the model at 0.5 GHz and 2.0 GHz
as we did at 1 GHz
(see Table1). The dielectric
properties of biological tissues at this frequency
band show significant dispersion. For example, for an averaged brain
tissue
they vary from 48.5+j22.7  at 0.5 GHz
to 43.2 + j11.8 at 2.0 GHz
[29]. These
variations can be incorporated into a multifrequency reconstruction
approach later
on, using well-developed models of tissue dielectric properties, such as the
Cole-Cole model or the multicomponent Schwan approach. The aim here was to
assess if multifrequency MWT imaging has the potential to improve
brain imaging. This is demonstrated in Figure
4, when two multifrequency approaches
were used. The first one (a) uses an initial imaging procedure at
0.5 GHz continuing at 1 GHz; the second one uses an
initial inversion at 2.0 GHz
continuing at 1 GHz. Both approaches demonstrate significant
image improvement
as compared with the 1st phase of reconstruction (see Figure
3). The area of
stroke injury (circled in white) has been reconstructed using
both approaches. There is room for improvement and optimisation of multifrequency
MWT imaging, which should be a focus of further simulation and experimental
studies. The most interesting and technically important question, at
the moment, is how distant should frequencies be? If the frequency gap can be
narrowed, then (i) tissue dispersion might not be taken into account and (ii) narrow band
efficient antennas may be used instead of wide band, or a family of narrow
bands, antennas.Reconstructed MWT images of simulated brain model with
a stroke injury with radius 2 cm located at{−4,0}
obtained using multifrequency reconstruction: (a) 0.5 GHz
and 1.0 GHz and (b) 2.0 GHz
and 1.0 GHz. 1% noise. Area with suspected stroke
injury is white circled.
(a)(b)Presented imaging results are not perfect. However, they indicate
that MWT has the potential to determine perfusion related changes
in the human brain and that MWT could be developed as a useful
new imaging modality for stroke management. There is room for
further images improvement at both stages: during reconstruction
and at post-processing afterwards.The projected
characteristics of an initial practical MWT system for brain imaging are summarised
in Table3. We intend to use
ceramic loaded (with ε∼60–90) waveguide antennas as previously
successfully used EM sources within MWT imaging chamber
[30–32].
The typical dimensions of the antenna tip facing the imaging chamber for frequency of
interest are 21 mm × 7 mm. This gives a maximal number of antennas per a 2D “slice”
of an imaging chamber of about 32 to 90, depending on an antenna rotation and,
consequentially, an imaging approach used (2D, 3D scalar or 3D vector).


## 4. Conclusions

(1)
The MWT imaging
of deep brain tissues and stroke detection presents a significant challenge,
being an object of interest located inside of a high dielectric contrast
shield, comprising the skull and cerebral spinal fluid.(2)
High performance,
nonlinear MWT inversion methods were able to produce biologically meaningful
images including images of stroke. At this level of the development of MWT
imaging technology, the smallest imaginable area of acute stroke is estimated
to be about 2 cm.(3)
Suggested
multifrequency MWT has potentials for significant improvement of imaging
results.



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