Antennas Design and Image Reconstruction for Microwave

Imaging Systems

Andre Filipe Lourenco Martinsandre.filipe.lourenco.martins@tecnico.ulisboa.pt

Instituto Superior Tecnico, Lisboa, Portugal

April 2018

Abstract

This paper presents the design of a Palm Tree Vivaldi antenna (onwards PTVA) suitable for Microwave Imaging Systems and proposes an Image Reconstruction Algorithm with calibration techniques, based on the Delay and Sum (DAS) algorithm, which aims at detecting intracranial haemorrhages. The prototyped PTVA is a small, compact and thin stucture, offering Ultra Wideband (UWB) characteristics in the 1.61-3.02 GHz frequency band, directive radiation pattern and small reflected pulse distortion. The PTVA features edge slot resonators on its radiators, which are a novel feature used to miniaturize Vivaldi antennas dimensions by confining the electric field, thus creating an increase of the surface current distributions in the resonators, leading to an increased electric length of the antenna. A description of the DAS algorithm is presented and it illustrates the DAS image reconstruction capabilities in the air. By implementing the DAS algorithm, its extention to a more complex scenario is performed, by proposing calibration techniques, the implementation of Fermats Principle and an integration window for a human head model with an haemorrhage. The paper is concluded by performing measurements with the prototyped PTVA and applying the implemented DAS algorithm aiming at detecting a water anomaly, emulating an haemorrhage, inside a human head phantom, with permittivity close to the one found in the human brain.Keywords: Vivaldi antenna, Delay and Sum Algorithm, Image Reconstruction, Antenna Design, Microwave Imaging Systems

1. Introduction and Motivation

Microwave imaging systems are expected to be af-fordable, portable and simple to operate [1]. Thesesystems shall be useful in scenarios where a signifi-cant dielectric constrast exists, namely in the detec-tion of tumors and haemorrhages. In routine check-up procedures and precocious diagnosis, some of thetraditional imaging techniques pose harm or dis-comfort to patients. For instance, in breast cancerdetection women are typically exposed to high dosesof ionizing radiation for each mammography, butthis procedure is also painful and uncomfortable.A microwave imaging system would allow to per-form the examination without pressing the breastsand without using ionizing radiation, since low en-ergy microwave frequencies are harmless to humans.The portable aspect of microwave imaging systemsalso allows that these systems are deployed in emer-gency vehicles. In an accident situation, where atraumatic brain injury occurs, intracranial haem-orrhage may appear and, the sooner it is detectedand treated, the greater the chances of minimizingthe issue. Therefore, by equipping emergency ve-

hicles with microwave imaging systems, an exami-nation may be performed when the transportationfrom the accident location to the hospital is takingplace. This way, it is possible to know beforehand ifany action is needed towards an intracranial haem-orrhage situation upon reaching the hospital or not.

2. State of the Art

The core concept behind a microwave imaging sys-tem is a monostatic radar [2]. An antenna emits asignal at a close distance to a target, a human heador breasts for instance, and detects the reflected sig-nals by its different dielectric components. By per-forming this scan at multiple positions around thetarget, a set of signals can be obtained and thenprocessed by an imaging algorithm. An image canthen be obtained, which highlights the situationswhere high dielectric contrasts are present, whichcan be used to detect tumors or hemorrhages. Todevelop such systems, several obstacles have to beovercomed. While having a simple concept, thereare several propagation media, with different per-mittivity values, εr. These values depend on thefrequency, for instance, at f = 2.45 GHz, values of

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11.35, 48.83 and 58.18 have been used for bone,brain and blood, respectively, in [3]. The exis-tence of different dielectric boundaries and medianot only causes several reflections of the emittedsignal, leading to pulse distortion, but also to atten-uation in those media, thus making an anomaly de-tection a difficult process, since the maximum emit-ted power is also limited [4]. Therefore, developinga suitable antenna is needed in order to correctlydetect the anomaly attenuated reflections. While,futurely, this technique is expected to be used in hu-mans, the investigation conducted nowadays mustbe done using phantoms. A phantom is an objectwhich emulates the electric properties of several tis-sues of the human body [5], and producing the mix-tures needed to do so is also a challenge. Finally,an imaging algorithm developed for this task is nottrivial, since it attempts to solve an inverse prob-lem. These problems aim at determining the causalfactors that originated a set of observations. In amicrowave imaging system problem, the goal of thealgorithm is to detect the anomaly which causedthe measured reflected signals at the antenna.

2.1. Antennas Design

Since the use of milimiter waves does not pose thedangers of ionizing radiations, their use is desiredfor medical applications. In particular, for a hu-man head, the microwave spectrum band of 1-3GHz is suggested, since it offers enough penetra-tion into the tissue . Ideally, the emitted signalshould use most of this band, therefore providingthe Ultra Wideband (UWB) characteristic, in or-der to improve signal resolution [2]. As stated pre-viously, a microwave imaging system is expected tobe small, portable and cheap, therefore, the anten-nas must also possess these characteristcs. It is,however, a challenge to develop small antennas forthe lower frequencies of the 1-3 GHz band due tothe relation between operating frequency and theradiating elements size. While, for breast cancerdetection an immersion medium for the scanningmay be used to reduce the antennas size [6], thesame methods are not applicable for intracrianaltumor or hemorrhage detection. Quasi-Yagi anten-nas are the most suitable ones to be developed, dueto their easy fabrication process, low cost, compactand lightweight structure [7]. The suggestions pre-sented in the literature vary from electromagnetichorns to monopoles, planar and bowtie antennas,Substrate Integrated Waveguide (SIW) printed an-tennas [7], dipoles [8] [9] and Vivaldi antennas [10][6]. Vivaldi antennas are characterised by having acurvilinear geometry from the feeding point to theaperture, on the other end [11]. These antennasare usually designed taking into account the low-est desired frequency of operation, which, in this

case, results in a quite large antenna, around thedesired wavelength [12]. There are, however, noveldesigns that allow Vivaldi antennas to be more com-pact, such as the removal of substrate parts [13], thefolding of the metallic layers to the other face of theantenna [14] or even cutting the metallic layer fol-lowing certain geometries [15]. These strategies aimat lengthening the electrical dimensions of the Vi-valdi antenna, while maintaining, or even reducing,its physical size by changing the surface currentsdistribution behaviour.

2.2. Phantoms

Phantoms are objects that emulate the electricproperties of living tissue and they are used to tunemedical imaging devices, measure mobile phonesSpecific Absortion Rate (SAR), among other pur-poses. Depending on the complexity required,phantoms may be either simple models or moreaccurate 3D printed ones [5] filled with liquid,semisolid or solid mixtures. In order to determinethe permittivities of human tissue, the Cole-Colemodel, which is an extension to the simpler DebyeRelaxation Model, is used [3], after which the differ-ent mixtures may be produced, using materials suchas glucose, sodium chloridre, diacetin or di-etyhleneglycol buthyl ether (DGBE).

2.3. Image Reconstruction Algorithms

Sound Navigation And Ranging (SONAR) and Ra-dio Detection And Ranging (RADAR) techniqueshave widely used after the II World War, and itsapplications range from military purposes, to airand maritime navigation and meteorology. Altoughthese techniques are widely used, an algorithm ap-plied to microwave imaging must be simpler and re-quire less computational resources. So, algorithmssuch as the Delay And Sum (DAS) [16], Delay Mul-tiply And Sum (DMAS) [17] and Kirchoff Migration[18] are preferred to when solving the microwave im-age reconstruction inverse problem. While the Kir-choff Migration algorithm operates in the frequencydomain, the DAS and DMAS are time domain al-gorithms. The concept behind the DAS and DMASis the summation of received signals, after a certaindelay is introduced, so that they add constructively,resulting in signals with small amplitudes (such asthe reflection of an anomaly, for instance) to be dis-tinguishable from background noise and higher am-pltidude signals. In order to implement these algo-rithms, a propagation model for the medium has tobe developed [19], based on Fermats Principle [1],which states that light travelling time has to be theminimum between a set of two points, taking intoconsideration the different media, their permittivi-ties and antenna introduced delays.

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3. Palm Tree Vivaldi Antenna

Following the works of [10] and [15], a ”Palm TreeVivaldi” antenna (PTVA) was designed in CST.The designed PTVA can be seen in Figures 1 and 2and its dimensions are seen in Table 1.

Figure 1: Designed PTVA front view.

Figure 2: Designed PTVA back view.

Parameter Value [mm]w 110l 95wm1 1wm2 1.5g 3.6

Table 1: PTVA parameters

The designed PTVA was designed on a RogersRT/Duroid 5880 substrate (εr = 2.2) and thicknessof 0.75 mm. The PTVA presents three edge slotresonators which aim to confine the electric field inthese areas (figure 3), thus increasing the surfacecurrent distribution there (figure 4). This leads toan increased electric length of the antenna, whilekeeping its physical dimensions, thus being a usefulminiaturization process.

Figure 3: Electric field confinement near the edgeslot resonators.

Figure 4: Surface current distribution magnitude.

As referred in the State of the Art, the designedPTVA will ideally operate in the 1-3 GHz frequencyband. The S11 parameter of the antenna was sim-ulated on CST Studio Suite and, as seen in figure5, the band of 1.58-2.89 GHz is below the -10 dBthreshold, meaning the designed PTVA providesUWB in this frequency range, as intended.

Figure 5: Designed PTVA simulated S11 parameter.

While the PTVA is not intended to operate in thefarfield region, its radiation pattern is also simu-lated, in order to illustrate the antenna’s capabilityto illuminate a focused area while scanning a tar-get. The farfield radiation pattern is seen in figure6.

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Figure 6: Designed PTVA simulated farfield radia-tion pattern.

Finally, the designed PTVA pulse distortion mustbe assessed, since the correct detection of the re-flected pulse shape is crucial for image reconstruc-tion algorithms. Therefore, by measuring the S11

parameter of the antenna in front of a metal plane,placed at a fixed distance, it is possible to synthesizethe reflected signal. By performing this operation,in figure 7 the emitted and received pulse are seen.

Figure 7: Designed PTVA pulse distortion assess-ment.

The PTVA introduces small signal distortion, al-though the pulse shape is easily detectable, makingthe antenna suitable for a microwave imaging sys-tem. The designed PTVA was prototyped with thedimensions presented in Table 1. Its front and backview can be seen in figures 8 and 9.

Figure 8: Prototyped PTVA front view.

Figure 9: Prototyped PTVA back view.

The prototyped PTVA was connected to a VNAand its S11 parameter was measured in free spaceconditions. As seen in figure 10, the PTVA pro-vides UWB characteristics in the 1.61-3.02 GHz fre-quency range, with the S11 parameter curves follow-ing the same behaviour.

Figure 10: Prototyped PTVA measured S11 param-eter.

The pulse distortion was also evaluated, by ar-ranging the same measurement setup than the oneused in the simulations. As occurred previously,the PTVA introduces small signal distortion, whilepreserving the pulse shape, making its detection ac-curate, as seen in figure 11.

Figure 11: Prototyped PTVA pulse distortion as-sessment.

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4. Image Reconstruction Algorithm

The Delay And Sum (DAS) algorithm was imple-mented in Matlab in order to perform the ImageReconstruction of a scanning region. The DAS isa time domain algorithm and the computation ofthe propagation times is fundamental for it to workproperly. The DAS, in free space, uses a monos-tatic radar approach [2] to obtain the reflected sig-nals from a target, with the propagation time beinggiven by equation 1

tp =(d1 + d2)×√εr + dant

c(1)

with d1 = d2 = d being the distance from the an-tenna to every point in the scanning region and dantthe propagation delay introduced by the antenna.The distance, d, from the antenna to all points inthe scanning region is computed according to ex-pression 2

d =√

(x− ai)2 + (y − bj)2 (2)

and, for a CST Studio Suite simulation set-up whichaims at determining the position of a metal target,in the air, for instance the one seen in figure 12,

Figure 12: CST Studio Suite simulation model.

in which only one antenna is considered, but 8target positions are evaluated, a procedure whichproduces the same effect as considering a circulararray of 8 antennas around the target. The re-flected signals for the 8 hypothetical antenna posi-tions were synthesized using the simulated S11 pa-rameter values, and are seen in figure 13

Figure 13: Reflected signals for each antenna posi-tion.

and, by applying expressions 1 and 2 to computethe propagation time, it is possible to describe theDAS algorithm with equation 3

I(x, y) =

N∑pos=1

g(pos) (3)

with g(pos) being defined as

g(pos) =

a1b1 a1b2 ... a1bja2b1 ... ... ...... ... ... ai−1bjaib1 ... aibj−1 aibj

and each aibj defined with expression 4

aibj = yr(t) (4)

with yr(t) being the reflected signal on each antennaand t computed according to expression 5

t =dant + 2

√(x− ai)2 + (y − bj)2

c(5)

which produces the image, by representing thesquared value of the summed amplitude signals seenin figure 14,

Figure 14: DAS image reconstruction for a coppertarget, in the air, with squared values of the ampli-tudes.

which shows a clear detection of the target, in itscorrect position, as seen by the darker red colours.By extending the propagation model in free spaceto the one of a human head, two additional prop-agation media must be considered, the bone (εr =11) and brain (εr = 49). A model of a human head,with a water anomaly emulating an haemorrhage,was designed in CST Studio Suite, as seen in figure15

Figure 15: Simulation set-up, as seen in CST StudioSuite, with Human Head model and water anomaly.

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The model was, has it was done previously, ro-tated in 8 positions to emulate the existence of 8antennas. The reflected signals were synthesized af-ter simulating the S11 parameter for each position,as seen in figure 16

Figure 16: Reflected signals for each antenna posi-tion.

in which is clear the existence of a strong reflec-tion from the air/bone dielectric boundary. Thisreflection has to be eliminated in order to allowthe anomaly reflection to be detectable. For this tobe feasible, the human skull symmetries have to beused, with a definition of the furthest signals fromthe anomaly being used, in which all other signalswill be aligned with. This calibration is describedmathematically as

ucal(t) = y∗r (t−∆t) (6)

with ucal(t) being the calibration signal, which isthe furthest signal from the anomaly, y∗r (t − ∆t)aligned to the closest,

ycal(t) = yr(t)− ucal(t) (7)

and ycal(t) the signal without the reflection fromthe air/bone interface. These signals are then cali-brated in respect to their symmetric ones, followingexpression 8

yi(t) =

{yi(t)− yi+N/2(t) i ≤ N/2yi(t)− yi−N/2(t) otherwise

(8)

resulting in the detection of the anomaly reflection,as seen in figure 17

Figure 17: Anomaly reflection detection, after cali-bration, in antenna in position 6.

and then, in order to eliminate the appearence ofa signal in phase oposition, the signal with highestpeak amplitude is considered the signal closer to theanomaly, while the other is chopped with zeros untilthe peak occurence time added with the impulsewidth, a process described by expression 9

y∗(t) =

{0 t ≤ tp + τy∗(t) t > tp + τ

(9)

In order to accurately represent the human head,a cubic spline interpolation is used, by computingthe distances travelled in free space by the reflectedsignals seen in figure 16, to define a delimiting curveof the air/bone interface. Since it was considered afixed value for the bone thickness, in figure 18 it ispossible to see the bone/brain delimiting curve.

Figure 18: Delimiting curve of the bone/brain di-electric boundary.

Then, by having all points of this curve, it is pos-sible to introduce Fermat’s Principle into the imagereconstruction algorithm, by considering more thanpossible path from each antenna to every point inthe scanning region, using the one that computesthe mininum distance,

dbrain = dmin = min√

(αi − ai)2 + (βj − bj)2

(10)and, using expression 10 and the known permittiv-ity of the brain, it is possible to compute the totalpropagation time,

tbrain =2dbrain

√εr

c(11)

t = tair + tbone + tbrain (12)

It is then possible to repeat the summation pro-cess described by equation 3 to produce the recon-structed image from figure 19, in which the anomalyis detected by the darker colours.

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Figure 19: DAS image reconstruction for a wateranomaly, inside a human head model, with squaredvalues of the amplitudes.

In order to aid in the detection of the anomalydue to resolution problems with microwaves, an in-tegration window was implemented, described byexpression 13

aibj =

∫ tp+∆t

tp−∆t

y(t)dt (13)

which highligths the detection, allowing a moreclear visualization of the anomaly, as seen in figure20

Figure 20: DAS image reconstruction for a wateranomaly, inside a human head model, applying theintegration window, with squared values of the am-plitude.

The developed DAS algorithm was tested usinga human head phantom, with a liquid phantom be-ing produced using 68% distilled water, 32% TritonX-100 and 4.4 g/L of sodium chloridre, accordingto [20], resulting in a liquid with measured per-mittivity εr = 45.45 at f = 2.3 GHz. A plas-tic tube containing tap water was inserted into areal sized human skull model, made of polyurethanewith measured permittivity εr = 2.75. The pro-totyped PTVA was placed in the same horizontalplane as the water anomaly, 1.7 cm below the ref-erence line and 1 cm away from the head phantomin their closest point, which was filled with liquiduntil the orange dashed line, as seen in figure 21

Figure 21: Reference line, position of the antenna1.7 cm below it and maximum liquid phantom level.

Using this measurement set-up the S11 param-eter was measured for 8 positions of the antenna,with the reflected signals being used to sucessfullyrecreate the air/bone delimiting curve, as seen infigure 22

Figure 22: Delimiting curve of the air/bone dielec-tric boundary.

although failing to calibrate successfully the an-tennas, leaving air/bone reflections which outshinethe water anomaly detection, as seen in figure 23

Figure 23: DAS image reconstruction for the humanhead phantom, with faulty calibration.

In order to detect the haemorrhage emulatingstructure, a calibration consisting in the subtractionof the S11 parameters measured with and withoutthe anomaly was used, resulting in a clear detec-tion, as seen in figure 24, altough some symmetriesappear in respect to the y = 0 axis, which may be

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due to multipath phenomena and reflections on theair/liquid phantom dielectric boundary and due tothe implemented DAS only considering a 2D scan-ning region.

Figure 24: DAS image reconstruction for the hu-man head phantom, using an alternative calibrationmethod.

5. Conclusionsfollowing the literature review which provided thefoundaments and knowledge to carry this task,the ”Palm Tree Vivaldi” antenna was prototyped.Starting with the requirements desired for an an-tenna suitable for a microwave imaging system, abasic antipodal Vivaldi antenna and novel featuresin Vivaldi antennas, the PTVA was designed. Byadjusting the Base Vivaldi antenna dimensions andfeeding structure, most of the desired frequencyband of operation was achieved, with the antennaexhibiting a directive radiation pattern and smallpulse distortion. The introduction of the edgeslot resonators in the Vivaldi antenna (now calledPTVA) radiators allowed to confine the electricfield, thus increasing the surface current distribu-tion in these locations, leading to an increase ofthe electric lenght of the antenna. This increaseallowed the PTVA operating frequency band lowerlimit to decrease, therefore occupying more of thesought 1-3 GHz frequency band. Not only this wasachieved, but also the electric lenght increase of theantenna allows its miniaturization, since it is possi-ble to maintain the performance with the introduc-tion of the resonators and reduce the antenna’s size.With this in mind, the designed PTVA antenna ex-hibits the desired UWB characteristic in the bandof 1.58-2.89 GHz band, low pulse distortion and di-rective properties. This is achieved in a miniatur-ized design, in a compact and thin structure. ThePTVA was then prototyped, with the fabricated an-tenna showing the same S11 behaviour as the sim-ulations, achieving UWB in the 1.61-3.02 GHz, oc-cupying most of the 1-3 GHz band, and preserv-ing the desired properties observed in the simula-tions. The antenna was successfully prototyped andtested, and fulfils the requirements needed for a Mi-crowave Imaging System. Following the antennadesign chapter of this dissertation, the imaging al-

gorithm is adressed. After a conceptual illustrationof the Delay and Sum algorithm, simulations usingCST Studio Suite are conducted, using the designedPTVA model. After a successful demonstrationof the DAS reconstructing a scanning environmentcomposed of a metal target in the air, a propaga-tion model for the human head is proposed and de-signed in CST Studio Suite. By simulating the de-veloped model, calibration strategies are proposedto overcome the air/bone dielectric boundary anddetect the signal reflected from a water anomaly,which emulates an haemorrhage. Following theproposed calibration description, Fermat’s Princi-ple was taken into account in the DAS algorithmpropagation times computation. Finally, after cor-rectly detecting the anomaly, an integration windowwas implemented to further improve its highlight,improving image resolution. All this was achievedwith the DAS algorithm running in an acceptablecomputation time. With the simulations results be-ing positive, it was decided to build a phantom andperform measurements with the prototyped PTVA.Using a polyurethane human skull model as con-tainer, producing a liquid phantom and fabricatinga water anomaly, which emulates an haemorrhage,measurements were perfomed in laboratory. Notonly the S11 parameter measurements, required forthe image reconstruction process, were conducted,but also measurements to determine solid and liq-uid materials permittivity accurately. While, us-ing the proposed calibration methods, the anomalycould not be detected, the use of different calibra-tion method allowed to perform its detection. Thiscalibration consisted in measuring the S11 param-eter with the anomaly and then subtracting theS11 parameter without it. The described proceduremade the removal of the air/bone reflections possi-ble and allowed to demonstrate the DAS detectioncapabilities, highlighting the haemorrhage emulat-ing structure in its location. As future work, sev-eral topics are left open for reasearch work. Forinstance, the aditional miniaturization of Vivaldiantennas, while keeping the working band in the1-3 GHz interval, is desired. The effect of dif-ferent edge slot resonator shapes and orientationson the electric lenght increase of the antenna andits miniaturization possibilities is crucial to havesmaller antennas deployed in a real world scenario.Moreover, aditional miniaturization strategies aresuggested to be researched. The biggest challengein microwave image reconstruction is, undoubtedly,the artifact removal calibrations. In this work, theproposed calibration failed when accounting labora-tory measurements, and investigation on overcom-ing the biggest reflections from the first dielectricboundary are suggested to take place. In fact, cali-bration techniques or signal processing methods in-

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vestigation is suggested in order to solve the hard-est challenge in microwave imaging systems. Theextension of the DAS algorithm to a 3D scanningenvironment is also a path of investigation, since itshould allow more accurate propagation times esti-mations, due to more paths to the anomalies beingconsidered. Further improvements on the existingalgorithms, either in the time or frequency domain,are also suggested, aiming at improving image res-olution and accuracy.

AcknowledgementsI would like to thank Prof. Antonio Topa forinspiring me and guiding through the Telecom-munications area, Mr. Carlos Brito and Mr.Antonio Almeida for providing the technical exper-tise needed during this work, Joao Felıcio for allthe patience and time spent explaining new con-cepts and providing ideas and solutions to overcomeseveral problems that appeared. To Prof. Car-los Fernandes and Prof. Antonio Moreira I expressmy most sincere gratitude for challenging me withthis Thesis, for the friendly relation we shared dur-ing this time and for all the fantastic feedback andwords of wisdom and advice that made this The-sis possible. To all my dear friends who were al-ways there for me. To Anaısa, my girlfriend, for allthe hours spent by my side, cheering for me, for allthe love and support, giving everything humanlypossible to make these months of work definitelybetter. Finally, I would like to thank my caringparents and grandparents. Even with the distanceand by spending so much time far away from them,their kind words of wisdom and support came to meeveryday. They made this journey possible, theymade it a unique experience, they made me who Iam, and I can’t ever thank them enough for all thesacrifices made in order for me to pursue this goalof my life.

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