On the Opportunities and Challenges in Microwave Medical Sensing and Imaging.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TBME.2015.2432137, IEEE Transactions on Biomedical Engineering 1

On the Opportunities and Challenges in Microwave Medical Sensing and Imaging Rohit Chandra, Member, IEEE, Huiyuan Zhou, Ilangko Balasingham, Senior Member, IEEE, Ram M. Narayanan, Fellow, IEEE (Review Paper)

Abstract—Widely used medical imaging systems in clinics currently rely on X-rays, magnetic resonance imaging, ultrasound, computed tomography, and positron emission tomography. The aforementioned technologies provide clinical data with a variety of resolution, implementation cost, and use complexity, where some of them rely on ionizing radiation. Microwave sensing and imaging (MSI) is an alternative method based on non-ionizing electromagnetic (EM) signals operating over the frequency range covering hundreds of MHz to tens of GHz. The advantages of using EM signals are low health risk, low cost implementation, low operational cost, ease of use, and user friendliness. Advancements made in microelectronics, material science, and embedded systems make it possible for miniaturization and integration into portable, handheld, mobile devices with networking capability. MSI has been used for tumor detection, blood clot/stroke detection, heart imaging, bone imaging, cancer detection, and localization of in-body RF sources. The fundamental notion of MSI is that it exploits the tissue dependent dielectric contrast to reconstruct signals and images using radar-based or tomographic imaging techniques. This paper presents a comprehensive overview of the active MSI for various medical applications, for which the motivation, challenges, possible solutions, and future directions are discussed. Index Terms—Microwave Medical Imaging, Microwave Sensing, Microwave Tomography, Biomedical Engineering

I. I NTRODUCTION

M

EDICAL imaging is an important tool for visualizing the interior of the body for clinical analysis and identifying any abnormality usually in a noninvasive way. It can be used to investigate human anatomy and physiology for both healthy and diseased cases. Medical imaging has been widely used in all phases of cancer management [1], identifying bone fracture, and tumors, to name a few applications. Medical imaging systems currently used in medical care facilities usually rely on x-rays, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US), and positron emission tomography (PET). These different methods provide clinical data with a variety of resolution, implementation cost, and use complexity, where some of them rely on ionizing radiation. For example, MRI provides good resolution, but is an expensive procedure. Further, internal body movements Rohit Chandra is with the Department of Electronics and Telecommunication, Norwegian University of Science and Technology, Norway. Huiyuan Zhou and Ram M. Narayanan are with the Department of Electrical Engineering, the Pennsylvania State University, USA. Ilangko Balasingham is with the Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Intervention Center, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway. e-mail: [email protected]

such as heartbeat can produce measurement artifacts in an MRI image. CT has a good spatial resolution, but is less informative when it comes to imaging soft tissues [2]. Further, it uses Xrays which can ionize the tissues and hence poses a health risk. PET can provide good information on the soft tissues, but suffers from poor spatial resolution [2]. Moreover, there may also be a chance of a false alarm when using these imaging techniques for detection of tumor. For example, X-rays are used in mammography for breast tumor detection where a high false negative rate (4 − 34%) and high false positive rate (70%) have been reported [3]. Moreover, due to less variation between the contrast of a tumor tissue and healthy tissue at Xray frequencies, detection becomes challenging. Furthermore, the systems based on these imaging methods are usually not portable. Using non-ionizing electromagnetic (EM) waves at microwave frequencies for sensing and imaging for medical diagnosis is emerging as a low cost and low health risk alternative to the aforementioned imaging technologies. Microwave sensing and imaging (MSI) offers a noninvasive way for the diagnosis of functional and pathological tissue conditions [2], [4]. Sensing applications of microwave involve single point applications wherein microwave signals transmitted by an antenna are received by the same or a different antenna after reflection or scattering by tissues. In such a case, no two-dimensional (2-D) or three-dimensional (3-D) images are formed. Examples of such cases can be heartbeat detection or changes in the microwave signal caused because of the pathological accumulation of water in the brain called cerebral edema [5]. In the imaging applications, a map or an image either in 2-D or 3-D is formed, that shows different tissue electrical properties or the location of a strong scatterer that is usually a tumor inside the body. MSI systems are usually portable and low-cost and hence, can offer the initial diagnosis of various life threatening conditions like brain-strokes while patients are still being on the way to a hospital in an ambulance [6]. The hardware of an MSI system generally consists of a microwave signal transmitter and a receiver such as a vector network analyzer, an antenna array, and a radiofrequency switch that switches between different antennas [7]. As mentioned by a commercial MSI company EMTensor [8], such a hardware for MSI can be produced at a fraction of the price of other diagnostic equipment, resulting in a low cost of the MSI system. Low cost of the MSI system can facilitate the diagnosis of patients in developing economies, where other costly imaging modalities are out of reach of large masses.

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Feasibility studies and proof-of-concept of MSI for various medical applications have been reported in the literature. Some of the early work reported for MSI were by Larsen and Jacobi [9] for imaging of perfused canine kidney. One of the limitations mentioned in their work was the long acquisition time of the used setup. Further, this early work used intensity modulation of a raster scanned display to convert measured scattering parameters into an image. Over the years, different data acquisition setup with linear arrangements of antennas [10] or more faster cylindrical arrangements [11] were proposed. Apart from the development of various reconstruction algorithms, MSI has moved from simple imaging of organs to application specific imaging for various pathological conditions. To this end, applications as respiration and heartbeat detection [12], [13], imaging brain for strokes [4] and cerebral edema [5], breast cancer [14], bone imaging [15], [16], heart imaging [17]-[21], and joint tissues [23], have been investigated. The working principle of the MSI is based on the difference between the electrical properties (relative permittivity and conductivity) of different tissues. A malignant tissue has usually different electrical properties than the surrounding normal and healthy tissues which can be differentiated in the image. As for imaging techniques, both quantitative and qualitative imaging algorithms have been developed. Quantitative imaging algorithms give a map or the image of the distribution of the various tissues with the values of electrical properties in the body. They are usually based on the inverse scattering problem. On the other hand, qualitative imaging algorithms use radar like techniques for image generation to differentiate the malignant tissues from the normal tissues as the malignant tissues are usually strong scatterer than the normal tissues. Initial clinical trials have been reported for MSI systems, especially for breast cancer [7], [24], [25] and brain stroke detection [6]. However, there are still many challenges, which have to be overcome and require further research for MSI systems to be practically implemented in a clinical setting. Currently, MSI systems face challenges both at system level like the requirement of a high dynamic range system to successfully measure weak scattering fields and also at the modeling level like the requirement of 3-D computationally efficient imaging algorithms which can model the effect of antennas, and feeding network for large biological bodies [26]. Challenges still remain open at experimental level where there is a need to efficiently couple the microwave power to the biological tissues, proper frequency for good resolution as well as penetration depth, and development of suitable contrast agents [26]. The main objective of this paper is to present an exhaustive literature review of the MSI for various medical applications which may provide a comprehensive study material for researchers in the field regarding current trend and future directions in MSI. It should be noted that the paper focus on the active microwave sensing and imaging where the tissues are illuminated with microwave energy, unlike the passive microwave imaging approach as used in thermography [27]. The paper further discusses the principle behind medical MSI techniques by highlighting various medical applications, challenges and possible solutions. It is arranged in following

order. Section II discusses the electrical properties of the tissues, both normal and malignant, that is used as a basis for MSI systems. Section III presents different imaging methods used for MSI. Section IV provides an overview along with the imaging results of various medical MSI applications. Open issues, challenges and future directions are discussed in Section V. Finally, Section VI concludes the paper. II. E LECTRICAL P ROPERTIES OF THE T ISSUE Different types of tissues, including both normal and malignant tissues of the same kind, have distinct electrical properties (relative permittivity and conductivity). This difference, primarily due to differences in the water content [32], results in the variation in the scattered field by different tissues which is the basis of MSI. An early review of the electrical properties of the human tissues is presented in [28]. A summary of measurements for various healthy tissues is provided by Gabriel et al. in [29]. The measured electrical properties of various healthy and the malignant tissues from 50 − 900 MHz is presented in [30]. For some tissues, the difference between the normal and the malignant tissue might be low. Further, the presence of multiple tissues with different properties result in a complex scattering environment. To solve these challenges, either use of a high dynamic system is required to capture the small difference in the scattered field or the use of contrast agents is proposed to enhance the electrical properties of the malignant tissues (discussed later in Section V). Another challenge is the frequency dispersion of electrical properties of the tissues that results in the distortion of the wideband pulses used in the radar-based imaging methods. To model the frequency dispersive nature of the tissues, one can either use the Debye model or the Cole-Cole model [31]. The attenuation of the microwave signal also increases with the frequency due to increase in the conductivity resulting in a lower penetration depth. Hence, choice of frequency is critical for imaging. Apart from the difference in the water content in healthy and malignant tissues [32], a variety of other factors have been explored which explains the difference in electrical properties between healthy tissues and malignant tissues. These are necrosis and inflammation causing breakdown of cell membrane [33], charging of the cell membrane [34], change in the dielectric relaxation time [35], and difference in the sodium content [33]. Fig. 1 shows the percentage change in the electrical properties of the different malignant tissues (colon, kidney, liver, lung, and breast) with respect to the healthy tissue from 50 MHz to 900 MHz based on the measurement data presented in [30]. As can be seen from Fig. 1(a), the relative permittivity can either increase, as for breast, colon, and liver, or can even decrease, as for lung and kidney, for the malignant type. The increase was highest in the case of breast, whereas for the kidney, low difference was measured. For the conductivity [Fig. 1(b)], breast tissues have the biggest difference and kidney have the least difference. To this end, various research about the electrical properties difference between healthy and malignant tissue have been done, especially for breast tissues [36]-[38], liver [39], lymph nodes [40], skin [41] and bone [42], and heart for myocardial ischemia and infarction [43].



300

Colon

Kidney

Liver

Lung

Breast

200

r

∆ε (%)s

250

20 0 −20 0

200

400 600 Frequencys (MHz)

800

(a) Fig. 2. Basic setup for microwave imaging in a two dimensional case when one antenna (TX) transmits the signal and all other antennas (RX) are in receiving mode. The imaging domain D contains the heterogeneous body to be imaged. The measurement domain S contain the transmit and the receive antennas. The whole system is in a homogeneous medium that acts as a matching medium.

∆σ (%)

600 550 40 20 0 0

200

600 400 Frequency (MHz)

800

(b) Fig. 1. Percentage change in the electrical properties of malignant tissues with respect to the healthy tissue from 50 MHz to 900 MHz: (a) percentage change in the relative permittivity (b) percentage change in the conductivity [30].

III. I MAGING M ETHODS Microwave sensing applications such as heartbeat, respiration, and vital signs sensing do not generate an image in the traditional sense, but a graph between different parameters and may be equivalent to sensor based methods such as electrocardiography (ECG). As for 2-D and 3-D imaging both quantitative and qualitative imaging algorithms have been developed. This section provides an overview of such methods that are summarized in Table I highlighting their positive and negative aspects. A. Quantitative Imaging

harmonic dependence exp(jωt) with j 2 = −1, and ω = 2πf where f is frequency, is assumed. The scattered field on S is defined by the following data equation: Escatt (p) = kb2

Z

G (p, r0 )χ(r)Etotal (r)dv(r0 ),

(1)

D

where p ∈ S , r0 , and r are position vectors. kb is the wavenumber of the matching medium, G is the Green’s function that has a different expression for transverse magnetic (TM), transverse electric (TE) or 3-D full vectorial case [44]. χ(r) is the contrast function containing the permittivity ε(r) of the body and is defined as: χ(r) =

ε(r) − εb . εb

(2)

The total electric field Etotal satisfies the domain equation in D: Z Etotal (r) = Einc (r)+kb2 G (r, r0 )χ(r0 )Etotal (r0 )dv(r0 ). (3) D

Quantitative imaging methods, also called microwave tomography, generates the image based on the values of electrical properties, both the relative permittivity and the conductivity, of various tissues of the body. The generated image shows how tissues with different electrical properties are distributed inside the body. The body to be imaged is surrounded by a number of transmit and receive antennas. Each transmitter antenna illuminates the body with the microwave signal at a time and the scattered signal by the body is collected by all the receive antennas. Usually this complete setup is immersed in a homogeneous medium with permittivity εb which acts as a matching medium for coupling the microwave energy transmitted to the body, reducing any reflection that may have occurred at the air-body interface in its absence. The body, that has to be imaged, lies within a bounded domain D and the antennas are located on measurement domain S as shown in Fig. 2. The microwave tomography problem is generally formulated in terms of electric fields. Three electric fields are defined for the purpose: the incident field Einc on D , the total field Etotal in D , and the scattered field Escatt on S . A time

The objective of the inverse scattering problem is to determine χ(r) in D by solving the equations (1) and (3) for known Einc and measured Escatt on S . This inverse problem is ill-posed. Further, the dependency of Escatt on χ(r) is nonlinear and hence solving it without iteration is computationally expensive. It should be noted that this dependency can be made linear if Born or Rytov approximation [45] is valid, which usually is the case when the contrast between different tissues is low. However, this condition is usually not valid in practical biomedical bodies due to a large difference between the dielectric properties of the tissues [45]. Hence, a wide variety of iterative reconstruction algorithms have been proposed [44]-[54]. Usually, the domain D is discretized into N cells where the contrast is assumed to be constant within the cells and the algorithm estimates the contrast of these N cells. These algorithms can be grouped into either Newtontype iterative algorithms [44], [46]-[52] or Modified Gradient (MGM) [53], [54] and Contrast Source Inversion (CSI) [55] depending upon the cost-function used. The cost-function for



Newton-type algorithms is of least-squares form: P scatt 2 ||Et − Escatt meas,t ||S t P scatt 2 , C N ewton = ||Emeas,t ||S

(4)

t

where the summation is done over the transmit antennas, Escatt is the computed scattered field at all the receive antent nas from the data and the domain equations for a particular transmit antenna t, and Escatt meas,t is the measured scattered field for that transmit antenna. The cost function is minimized iteratively by updating the contrast as χn+1 = χn +νn ∆χn , νn being the appropriate step length and ∆χn being the correction at the nth iteration. As the cost function is nonlinear in χ and is ill-posed, it is regularized before performing the minimization. A most common regularization method is Tikhonov regularization [49]. Newton-Kontorovich [44], Gauss-Newton inversion [52], Distorted Born Iterative method [46], LevernbergMarquadt [49], and Log-Magnitude and Phase Reconstruction (LMPF) [51] are some of the Newton-type algorithms that have been used for microwave imaging of human body parts. Though Newton-type algorithms converge in less number of iterations, the data equation or the forward equation has to be solved at each iteration resulting in high computational demand, that may limit the applicability in a full 3-D vectorial case [55], [56]. Hence, alternative to Newton-type algorithms, MGM and CSI methods have been proposed without solving the forward equation by formulating the cost-function in terms of unknown contrast and unknown contrast sources. The contrast source is defined as w(r) = χ(r)Etotal (r) [55]. Consequently, the cost function is written as [55]: P C CSI =

t

2 ||Escatt meas,t − GS (wt )||S P scatt 2 + ||Emeas,t ||S t

P t

2 ||χEinc t − wt + χGD (wt )||D P , 2 ||χEinc t ||D

(5)

t

where GS and GD are Green function’s operator in S and D , respectively. The minimization of the cost-functional iteratively constructs sequences of the contrast sources and the contrast. At each iteration step, each sequence is updated, assuming the other one as constant. The cost-function is often multiplied by a regularization term and the algorithm is called multiplicative regularized contrast source inversion (MR-CSI). The MGM is similar to CSI where the fields and the contrast is updated in each step. Avoiding the forward problem in each iteration in CSI and MGM leads to a large number of unknowns. Moreover, they take a larger number of iterations to converge [57]. However, in spite of these difficulties, MRCSI has been shown to be computationally less demanding than the Newton-type algorithms [55]. The reconstruction algorithms can be applied at a single frequency or at multiple frequencies in a frequency hopping manner [58]. Ignoring the frequency dispersion of the tissues, frequency hopping can be used to remove the artifacts, especially in the conductivity where the data obtained at one frequency are used as a starting point for the other frequency [59].

However, as discussed earlier in Section II, the electrical properties of the tissues have frequency dispersive nature and hence reconstruction algorithm that takes into account the frequency dispersion by using the Debye Model in the contrast has been developed in [60]. Time-domain instead of frequency domain inversion algorithm has also been developed [61] for a wideband pulse. B. Qualitative Imaging In some medical applications, such as breast tumor detection, the aim of imaging is not to obtain the electrical properties of the tissues, but to detect and localize the tumor. Hence, for such cases, solving computationally intensive iterative quantitative algorithms can be avoided. As discussed in Section II, the tumor usually has higher dielectric properties than normal tissues which makes it a strong scatterer. Various radar imaging algorithms can then be used to focus the tumor, such as confocal microwave imaging [3], beamforming [62], and tissue sensing adaptive radar [63]. In these techniques, ultra-wideband (UWB) signal is used to have a good timeresolution. The setup is same as that of the quantitative imaging as shown in Fig. 2. However, each antenna transmits a short pulse at a time (UWB in frequency domain) and the backscatter response is received by the same antenna. In the confocal microwave imaging, the backscatter response consists of incident waveform, tumor backscatter, the scattered signal from the skin, and other backscatter response known as clutter from other tissues inside the body. Signal processing is done to remove the scattered signal from the skin [3], [64] to get the response from the tumor and the clutter. The processed backscattered waveform at each of the antennas is integrated over time to obtain Bt integrated waveforms where t is the number of the transmit antennas. The reconstructed image is then created by time-shifting and summing data points from these integrated waveforms for each synthetic focal point r in the imaged body. The intensity I(r) of a pixel in the image at r is the the squares of 2 P coherently summed values [3]: I(r) = ct Bt (τt (r)) , t

where τt (r) = 2|r − rt |/(V ∆T ) is the time delay from the tth antenna at rt to the synthetic focal point at r in the body. V is the propagation velocity of the signal inside the body, assuming a homogeneous medium and ct are weights to compensate for the radial spreading of the cylindrical waves as it propagates outward from the transmit antenna. Similar to confocal approach, another algorithm called tissue sensing adaptive radar (TSAR) [63] has also been developed which assigns more weight to antennas closest to the focal point. Further, TSAR algorithm includes compensation term for the distance traveled within the body and spreading of the waveform. The time-shifting and the summation of the backscattered signal done in the confocal imaging may not compensate for frequency-dependent propagation effects that are associated with an UWB signal. Moreover, it has limited ability to discriminate against artifacts and noise [62]. Hence, microwave imaging via space time beamforming has been proposed [62]



which can spatially focus the backscattered signals to discriminate against clutter and noise while compensating for frequency dependent propagation effects. To achieve the spatial focus, time shifting of the received signal is done to align the backscattered signal from a hypothesized scatterer at a probable location of the strong scatterer. This time aligned signals are then passed through a bank of finite-impulse response (FIR) filters, for each antenna location, whose weights are chosen using the least square method. This ensures that the components of the backscattered signal originating from the probable strong scatterer location passes the beamformer with unit gain while compensating for the frequency-dependent propagation effect [62]. Further, the beamformer output is time gated to the time interval which would contain the backscattered signal from the probable strong scatterer location. The beamformer is scanned to different locations inside the body by appropriately changing the time shifts, gating, and FIR filter coefficients and the energy at each location energy is calculated [62]. The reconstructed image shows the backscattered signal energy at different locations. C. Sensing One of the most commonly employed methods for microwave sensing applications, especially for heartbeat and respiration is based on the Doppler radar. In such a method, the radar transmits the microwave signal of wavelength λ that is then reflected by the moving human body parts as the chestwall. The reflected signal is received by another antenna. The phase of the reflected signal changes according to the Doppler theorem. The change in the phase ∆φ of the received signal at time t is related to the displacement of the moving body x(t) and is given by [13]: ∆φ = 4πx(t)/λ. x(t) contains displacement both due to heartbeat and respiration. Holding of breath may result in the displacement which is just due to the heartbeat. The heartbeat signal can also be extracted by use of a low pass filter which can filter out high frequency respiration signal. Other sensing applications may involve sending microwave signal from an antenna towards the body and recording the reflected or the transmitted signal from another or the same antenna. Due to changes in the electrical properties of the tissues during pathological conditions, the reflected signal will be different from what it would be for a normal healthy case. Based on this, pathological conditions can be detected. For such a purpose, usually ultra-wideband (UWB) radar are used as they can penetrate the body and also have a good temporal resolution [65].

leading to comfort of patients. In [12], the Doppler radar was used at different frequencies, 2.4 GHz, 5.8 GHz, and 10 GHz, and different power levels for heartbeat extraction. In [13], a comparison between different preprocessing methods for the demodulation of the received signal using the data collected by the Doppler radar for heartbeat and respiration monitoring is presented. Fig. 3 shows the phase of the heartbeat signal obtained with the Doppler radar with different preprocessing technique presented in [13]. The plot of the ECG that correlates well with the extracted heartbeat is also shown. 2) Blood Flow and Pressure: Microwave sensing can also be used for detection in changes of blood flow and blood pressure. For example, in [66], it is shown that microwave sensing has potential for detection of 0.3 − 0.5% changes in extremity blood flow and about 2.5% change in elevated compartment pressure using the experiments done on a pig’s hind leg. Changes in the amplitude of the microwave signal at 2.5 GHz transmitted through the pig’s thigh on reduction in the blood flow is shown in Fig. 4. In [67], an experimental investigation is done on a phantom and a human subject using an UWB radar to estimate the radius and changes in the radius of the aorta that is related to the blood pressure. 3) Fluid or Water Accumulation: Another sensing application is detection of fluid or water accumulation in various organs. The detection and monitoring of cerebral edema are presented in [5]. A head phantom was used that constitute of separate compartment of water and ethanol inside a glass sphere to stimulate the head tissues (water for cerebral fluid and ethanol for brain tissues). The microwave signal at 2.4 GHz from one side of the head phantom was transmitted and received from the other side. The received signal was compared with the reference signal. The amplitude and phase of the received signal are related to the variable amounts of excess water. It was found that 2◦ in phase change was related to 10 ml of addition of water and 1◦ phase changes was related to 10 ml change in the ethanol volume. Similar to this principle, water accumulation in the body using an UWB radar is presented in [68] where it was found that the reflected signal from the bladder with water had different received signal than without water. A microwave stethoscope for monitoring vital signs and changes in lung water content is presented in [69]. A coplanar waveguide operating at 915 MHz and 920 MHz was used to measure the reflection coefficient on a phantom as well as on the humans. From the measured reflection coefficient, various vital signs like respiration rate, heart rate and changes in the lung water content was successfully extracted using signal phase of heart-beat signal (diff. est. methods)

IV. A PPLICATIONS This section gives the overview of the different medical applications where MSI has been studied. A. Medical Sensing Applications 1) Heartbeat Detection: Heartbeat extraction using the Doppler radar is a common sensing application. The advantage of using the Doppler radar is that it is a non-contact method

ECG 35

40 Time (s)

45

Fig. 3. Phase of the received signal for heartbeat after filtering out the respiration signal [13].



TABLE I S UMMARY OF S ENSING AND I MAGING M ETHODS

Method

Theoretical Basis

Quantitative Iteratively solving (Section III-A) inverse EM scattering problem.

Qualitative (Section III-B)

Positive Aspects

Negative Aspects

Frequency

Remarks

Newton Methods: Newton-Kantorovich [44] Gauss-Newton [52] Levenberg-Marquadt [49] Distorted Born [46] Log-Magnitude and Phase Reconstruction [51]

Less number of iterations required to converge, Lower number of unknowns.

Computationally demanding, Limited 3-D applicability

Single frequency, multiple frequency or wideband

2-D or 3-D image with dielectric properties. Newton methods are similar if same regularization technique is applied.

Contrast Source Inversion [55] Modified Gradient Method [53]

Computationally less demanding (than Newton Methods), 3-D applicability Avoids inverse problem, computationally very less demanding Simple setup

Larger number of unknowns, more iterations required for convergence. UWB

Image of backscattered energy

Single frequency or UWB

No image but 2-D plot

Confocal Imaging [3] Tissue Sensing Adaptive Radar [63] Space-Time Beamforming [62] Doppler Radar [13] Transmission/Reflection [6]

Microwave Signal Amplitude

Sensing (Section III-C)

Strong scatterer detection using radar techniques Change in amplitude and/or phase

Techniques/Algorithm [ref.]

Time (s)

Fig. 4. Changes in the amplitude of the microwave signal transmitted through the pig’s thigh at 2.5 GHz. Baseline flow is uncompromised blood flow, whereas 1st flow reduction represent when the blood flow was reduced by certain amount. The plot of the ECG is also shown which correlates c Institute of Physics and well with the uncompromised blood flow [66] ( Engineering in Medicine. Reproduced by permission of IOP Publishing. All rights reserved).

processing techniques. 4) Brain Stroke: In [6], Persson et al. presented a working prototype using microwaves in 0.3 − 3 GHz for detection of the strokes for which clinical testing was done. It uses measured scattered data of the different transmission channels for different subjects with known conditions in a supervised learning based detection algorithm based on a subspace classifier method. The algorithm was able to differentiate stroke patients from healthy volunteers. 5) Brain Edema and Duct Obstruction: There are other sensing applications using radio-frequency-identification (RFID) technology for monitoring of brain edema and duct obstruction [70]. However, these uses implanted self-sensing tags and require invasive surgery. This paper focuses on noninvasive MSI applications. Therefore, these are out of scope. B. Brain Imaging The objective of brain imaging is to detect and locate the area of the damaged brain tissues due to injuries or conditions such as ischemic or hemorrhagic stroke. These

Detection challenging in low contrast Motion artifacts, reference needed

results in a blood clot or a blood pool within the brain. Due to the difference between the electrical properties of normal brain tissues and the blood, the stroke can be detected using microwave imaging. Brain imaging for stroke detection and monitoring is investigated in [4], [64], [71]-[74]. Quantitative methods used so far includes a Newton-type iterative scheme for 2-D tomography [4], Born iterative method [71], and a multiplicative regularized Gauss-Newton inversion [75]. These investigations have been done numerically using a simple brain model with stroke as in [4], or on a numerical anatomically real head phantom [71]. Further, they have used single frequency in the range of 0.5 − 2.5 GHz. The stroke can be observed in the reconstructed quantitative image having different electrical properties than the normal brain tissues as can be seen in Fig. 5(b) where a 2 cm acute stroke area in the image was successfully detected. The difference in the contrast of the stroke with the normal brain tissues also makes the stroke a significant scatterer and hence, qualitative approach as confocal imaging has shown to detect stroke [64], [72]. Confocal imaging of the brain in 1 − 4 GHz frequency band was done on a physical head phantom in [72] using a microwave imaging setup shown in Fig. 6. The stroke in the image obtained through confocal imaging can be identified as a region having significant backscattered energy as shown in Fig. 7. This method detected an ellipsoid stroke of size 2 × 1 × 0.5 cm3 with a localization accuracy within a few millimeters. C. Breast Imaging The objective of breast imaging is to detect breast cancer or tumor. It is one of the most widely investigated medical applications of MSI. One of the reasons for this, as discussed in Section II, is because of the relatively high contrast of the breast tumor compared to the prominent fat tissue in a breast. Hence, such a high contrast makes the breast tumor a significant scatterer where radar-based techniques can be effectively applied to locate the tumor as reported in [3], [62], [63], [76]-[82]. Moreover, the tomographic methods can also be used where the tumor can be distinguished easily from the



(a)

(b)

depth (cm)

Fig. 5. (a) Numerical brain phantom with tumor (b) Reconstructed relative permittivity showing the encircled tumor [4]

Fig. 7. Reconstructed image using the confocal method [72] showing position of stroke at two different locations. The true position of the stroke is shown by an ellipse.

span (cm)

Fig. 6. A measurement setup for brain imaging using a physical head phantom and the Vivaldi antenna [72].

rest of the fat tissues in the reconstructed image as discussed in [52], [86]-[91]. A review of extensive research work done for breast cancer imaging is provided in [14] and [92]. Different radar-based approaches that have been used for breast imaging are confocal imaging [3], [78], [79], the TSAR algorithm [63], [80], [25], space-time beamforming [81], and a two stage Capon beamforming called multistatic adaptive microwave imaging [82]. An UWB confocal algorithm is developed and investigated on a 2-D anatomically realistic MRIderived breast model [Fig. 8(a)] in [78]. It was extended to 3-D cases in [3]. In [79], a signal processing technique called delay-multiply-and-sum is used with confocal imaging where the backscattered signals were time shifted, multiplied in pair, and the products were summed to form a synthetic focal point. This resulted in an improvement in the usual confocal imaging in which just delay-and-sum of the backscattered signal is done. The TSAR algorithm used in [63] for breast cancer detection used simple tumor models using an experimental setup. In [80], a signal processing algorithm based on the Bayesian estimator is used to enhance the tumor response in the TSAR data by suppressing late-time clutter. Spacetime beamforming in [81] used experimental backscattered data of multilayer breast phantoms for the tumor detection. A two stage Capon beamforming called multistatic adaptive microwave imaging that has higher resolution, and better noise and interference rejection capability, is presented in [82] for an early breast cancer detection. These radar-based qualitative approaches used UWB, 3.1 − 10.6 GHz for confocal imaging in [78], [3], 1 − 10 GHz for the TSAR algorithm in [63], and 1 − 11 GHz for the space-time beamforming [81]. The

depth (cm)

(a)

span (cm)

(b) Fig. 8. (a) Numerical breast phantom with tumor model and (b) Reconstructed image using a qualitative confocal method [78] showing position of detected tumor.

detection of a tumor of size as small as 2 mm diameter at a depth of 3.1 cm from the top surface of the breast has been shown using 2-D confocal imaging [78] as shown in in Fig. 8(b). However, using the 3-D confocal imaging, the detected tumor size was of 6 mm diameter [3]. The TSAR algorithm detected a tumor of size 1 cm [63] whereas Bayesian estimator used with the TSAR algorithm detected a smaller tumor of diameter 3 mm. There are other radarbased approaches which do not aim to construct an image of the breast with the scatterer or the tumor, but to present the likelihood of the presence of a tumor [83], [84] or to classify a lesion as benign or malignant [85]. In [83], tumor of size 2 mm is detected based on generalized likelihood test and in [84], a 10 mm tumor is detected with a detection accuracy up to 92% with a support vector machine classifier, a machine learning classification algorithm. The work in [85] demonstrated that characteristic morphological features of the lesion can be identified with relatively high rates up to 87% of correct classification. Tomographic imaging approaches that have been used for breast imaging are Newton-type iterative algorithms [86], [87], Newton-type conjugate gradient method [88], the Gauss-


This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TBME.2015.2432137, IEEE Transactions on Biomedical Engineering

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Fig. 9. Numerical breast phantom (a) relative permittivity (b) conductivity. Reconstructed contrast with MR-CSI (c) real part (d) imaginary part [91].

Newton reconstruction [89], the distorted Born iterative method [90], and the CSI method [91]. These approaches reconstruct the breast electrical properties where a tumor region can be identified by having an apriori knowledge of the electrical properties of the tumor. Moreover, these approaches use different single frequencies from 0.3 GHz up to 5 GHz. A clinical prototype where a Newton-type iterative algorithm was employed is reported in [86]. The imaging was done on 5 volunteers using the prototype. Based on the measurement, it was found that the average relative permittivity of the breast as a whole may correlate with radiological breast density categorization. Other than this investigation that has been done on actual humans, the rest are numerical investigations. For example, a feasibility study using 2-D breast models with the tumor using a Newton-type iterative algorithm is done in [87] and using a Newton-type conjugate gradient method in a numerical breast model is presented for a 3-D case in [88]. An example of a reconstructed quantitative image of a numerical breast model with a tumor of radius 4 mm using the CSI method [91] is shown in Fig. 9. Most of the breast cancer imaging setups use a cylindrical or a hemispherical antenna array. A hemispherical measurement setup is presented in [93] and an improved version of this setup used for clinical trials is presented in [7]. However, the planar configuration of the antenna arrays, where the breast is compressed between two planar arrays of the antennas, similarly to that of X-ray mammography, has also been investigated in [3], [94], [95]. D. Bone Imaging Bone Imaging using microwave has been done for applications like detecting the leukemia in the bone marrow [15], and to determine the bone density for detection of osteoporosis [16]. Leukemia causes the cellular population in the bone to increase that in turn increases the relative permittivity and decreases the conductivity up to a factor of 2. Both of these

(b)

Fig. 10. Reconstructed images of the left leg and the right leg of a patient affected by injury in the left leg through a Newton method with softregularization (a) relative permittivity (b) conductivity. Left side is for the left leg and right side images are for the right leg [16].

applications require the electrical properties of the bone to be known and thus, the approach has to be quantitative. The possibility of using Levenberg-Marquardt method for detection of the leukemia in the bone marrow is discussed in [15] where the authors were able to reconstruct the proliferated cells in the marrow using a simple 2-D cylindrical model of the leg at 800 MHz. In [16], microwave imaging at 1300 MHz using the Gauss-Newton iterative method with soft regularization was done to acquire 2-D and 3-D images of the calcaneus bone of two patients. Their one leg was immobilized for at least six weeks during recovery from a lower leg injury. The reconstructed images are shown in Fig. 10. A good correlation was observed between the recovered electrical properties of the injured calcaneus bone by microwave imaging and the CT Hounsfield density measure. Hence, microwave imaging of the bone can also help in determining the bone density for detection of osteoporosis [16]. E. Soft Tissues and Joint Imaging Microwave can image the soft tissues which might also get injured when bone fracture happens. The injured soft tissues are not detected by the X-rays [2]. This is a challenging task as the high dielectric contrast between bone and soft tissues may obscure less pronounced contrast between soft tissue abnormality. In [66], a feasibility study using a Newton and the MR-CSI method for functional imaging of the extremity soft tissues of a pig’s hind leg is presented at 1 GHz. A feasibility study of the TSAR technique [63] for imaging of the knee joint is presented in [23]. The objective was to detect lesions of clinically relevant sizes and shapes in the menisci, ligaments, and tendons in the knee. Both numerical simulations and measurements were done, and data were collected in 50 MHz−13.51 GHz. Bovine patellar tendons were used for measurements. Images were reconstructed with and without the model of the meniscal tear in the knee. A tear thin as 1.3 mm×8 mm was detected in the difference image (but not in the reconstructed image) and two tears separated by a minimum distance of 9 mm were detected as illustrated in Fig. 11. High permittivity lesions were simulated by inserting a small water filled plastic plug in the tendon and a low permittivity scar-tissue defect by inserting a material whose permittivity varies from 48 to 27 in the measured frequency range. In this case too, the differential image was able to detect the lesions. It was concluded that since, the detection is based on the differential imaging, there might be a challenge to obtain the reference image (for the healthy case) and hence, a more promising clinical application of this technique could be for monitoring and healing of a lesion.


This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TBME.2015.2432137, IEEE Transactions on Biomedical Engineering

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Fig. 11. Differential image when two tear are present in meniscus. The differential image is obtained by subtracting images obtained by the TSAR imaging algorithm, with and without the tear. The meniscus on which measurements were done is shown in inset with the positions of the antennas marked with ’*’ [23].

F. Heart Imaging Heart imaging is done to detect any pathological conditions as myocardial infarction [19] as the electrical properties of myocardial tissue have a strong dependence on coronary blood flow. It can also be used to obtain the temporal images of a beating heart for heartbeat extraction [22] due to millisecond temporal resolution. However, the heart is located inside an anatomically complex and highly electromagnetically scattering structure of the thorax, that make microwave imaging of the heart a challenging task [2]. Moreover, there might be strong reflections from the air-filled lungs that might make the detection of potentially weak field scattered by the heart challenging [20]. Hence, heart imaging requires minimum SNR of 30 dB and a dynamic range of 150 dB or more [20]. In [17], Semenov et al., reconstructed the quantitative images of an excised canine perfused heart in a static as well as in a beating case using a modified Rytov approximations at 2.45 GHz. In [18], authors were able to successfully reconstruct the internal structure of an excised canine heart including the left and the right ventricles using the 3-D MGM and in [19] for the detection of the myocardial infarction. In [20], using the same 3-D gradient method, authors were able to reconstruct the heart in an intact swine at 0.9 GHz. Further, measurements were done with and without the heart in the thorax, and it was found that the presence of the heart changes the amplitude of EM field by 10 − 20% and a change of 7 − 10 degrees in the phase was observed. An UWB radar-based approach to image the human heart in real time is presented in [21], [22]. An UWB switched array body-contact radar was used and a back-projection imaging algorithm was used. The frequency utilized was 0.8 − 3 GHz. The purpose of imaging was to obtain the heartbeat rate. Successive images were reconstructed of the beating heart of a male subject with a frame rate of 25 Hz. Using the reconstructed image, heartbeat waveforms were extracted. It was concluded that using this approach, different moving parts of the heart can be observed and may provide the basis for an online diagnosis.

temperature monitoring so that the temperature of the cancerous tissue is elevated to a proper level for its destruction. The electrical properties of the soft tissues are sensitive to the change in temperature primarily due to changes in the electrical properties of the water with temperature. The change in electrical properties of the animal liver tissue is reported with temperature in [98]. For example, it was observed that the properties change approximately 1% per ◦ C between 28◦ C and 53◦ C at 915 MHz. The idea of microwave imaging for remote thermal sensing was used in an early work [99]. This has been reconsidered in recent works [100]-[102]. In [101], tomographic images over the frequency range of 300 − 1000 MHz of the pig abdomen were generated where the recovered conductivity was found to vary linearly with the controlled temperature values. In [102], a real-time microwave imaging system at 915 MHz to image differential temperature based on the change in the electrical properties of water with the goal of achieving noninvasive temperature monitoring of thermal therapy systems was developed, and tested on a simple breast phantom. Hence, exploiting temperature sensitivity of the tissues, MSI can also be used for noninvasive thermal therapy monitoring. H. Miscellaneous Biomedical Body Imaging There are many other studies which have been done for imaging the biomedical bodies that does not fit into any aforementioned applications. However, these shows the potential fields where MSI can be utilized. Some of them are briefly summarized here. The potential to image the human arm is shown using the CSI method in [103]. In [104], the Newton-Kantorovich algorithm is used to obtain a quantitative permittivity image of a numerical human thorax and a real human arm at 434 MHz. In [105], the author of this paper used the Levernerg-Marquadt method presented in [49] for imaging the human torso at the level of small intestine in the 403.5 MHz MedRadio band. The reconstructed image was then used as a basis for the localization of wireless capsule endoscopes. Using this method, a localization accuracy within a centimeter was obtained. The possibility to image the whole body using microwave imaging is shown in [106] where measurements were done on a dog and a 3-D gradient based iterative algorithm was used to invert the data. In [45], comparisons between different quantitative image reconstruction methods are done on the experimental data of a phantom and a pig’s hind leg in the frequency range 0.9−2.05 GHz. These methods are Newton based 2-D method and 3-D gradient method, and MR-CSI both for 2-D and 3-D cases. These research shows the feasibility of imaging large biological bodies using quantitative approaches and opens up opportunities in terms of several new application areas where MSI can be used. I. Summary of the Applications

G. Thermal Therapy Monitoring One of the ways to treat subcutaneous cancer of the soft tissue is through thermal therapy using microwaves as in hyperthermia [96] or thermal ablation [97]. These require

Table II summarizes the various applications of MSI studied in this paper. It mentions that the study in the literature was done whether on the numerical (Num.) or the experimental data (Exp.) and at what frequencies. Moreover, it also mentions



TABLE II S UMMARY OF MSI A PPLICATIONS Application

Algo. [ref]

Heartbeat (Section IV-A1) Blood Flow/Pressure (Section IV-A2) Cerebral Edema (Section IV-A3) Water Accumulation (Section IV-A3) Brain Stroke (Section IV-A4) Brain Imaging (Section IV-B)

Doppler Theorem [12], [13] Tranmission meas. [66], [67] Transmission meas. [5] Reflection meas. [69], [68] Transmission meas. [6] Newton-type [4], [48] Born Iterative [71] Gauss-Newton [75] Confocal [64], [72] Confocal [78], [79] TSAR [63], [80], [25] Space-time Beamforming [81] Two stage Capon Beamforming [82] Newton-type [86], [87] Conjugate Gradient Method [88] Gauss-Newton [75] DBIM [90] CSI [91] Levenberg-Marquadt [15] Gauss-Newton [16] Newton/MR-CSI [66] TSAR [23] MGM [18], [20] Back-Projection [21], [22] LMPF [101] Born Approximation [102] CSI [103] Gauss-Newton [75] Newton-Kantorovich [104] Newton-Kantorovich [104] Levenberg-Marquadt [105] 3-D Gradient [106] Newton Gradient, MR-CSI [45]

Breast Imaging (Section IV-C)

Bone Imaging (Section IV-D) Soft-Tissue (Section IV-E) Heart Imaging (Section IV-F) Thermal Therapy (Section IV-G) Arm Imaging (Section IV-H)

Thorax (Section IV-H) Localization (Section IV-H) Whole-Body (Dog) (Section IV-H) Leg (Pig) (Section IV-H)

Num.

Exp.

Human/Animal

Clinical Trial

No Yes No Yes No Yes Yes Yes Yes Yes Yes No Yes No Yes Yes Yes Yes Yes Yes No Yes Yes Yes No Yes No No No Yes Yes No No

Yes Yes Yes Yes Yes No No No Yes No Yes Yes No Yes No No No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No Yes Yes

Yes Yes No Yes Yes No No No No No Yes No No Yes No No No No No Yes Yes No Yes Yes Yes No Yes Yes Yes No No Yes Yes

No No No No Yes No No No No No Yes No No Yes No No No No No No No No No No No No No No No No No No No

that if the experimental data were collected on human/animal and whether clinical trials have been performed. Clinical trials are initial testing of the MSI devices where prior approval has to be taken from the health and ethical committee of the country concerned. Moreover, many subjects either patients or healthy volunteers participate after giving their written consent. V. C HALLENGES , O PEN T OPICS AND F UTURE R ESEARCH D IRECTIONS The above section showed that MSI has many promising medical applications. So far, the feasibility studies for many applications have been mostly done through numerical simulations. The ultimate objective of any MSI system is to be used in a clinical setting as a cost-effective alternative to the existing sensing and imaging modalities. To meet this objective, the MSI system should be developed beyond theory and simulations into working prototypes that can be used for clinical trials. However, there are many technical challenges and open topics that needs to be solved before going for clinical trials providing the future research directions for MSI. Some of them are discussed in this section. A. Effective Coupling of Microwave Signal For tomographic as well as sensing system, there is a challenge to effectively couple the transmitted microwave signal to the body. Due to a high difference in the electrical properties of the medium in which the antenna is placed

Freq. 2.4 GHz, 5.8 GHz, 10 GHz 2.5 GHz, 0.1-5 GHz 2.4 GHz 915 MHz, 920 MHz, 3.1-10.6 GHz 0.3-3 GHz Diff. single freq. in 0.5-2.5 GHz 600, 850, 1000 MHz 1 GHz 1-4 GHz 3.1-10.6 GHz 1-10 GHz, 50 MHz-15 GHz 1-11 GHz UWB Single freq. in 300-900 MHz, 2 GHz 2, 3.5, 5 GHz 1 GHz 0.5-3.5 GHz 1 GHz 800 MHz 1.3 GHz 1 GHz 50 Mz - 13.51 GHz 0.9 GHz 0.8-3 GHz Diff. single freq. in 300-1000 MHz 915 MHz 0.8, 1, 1.2 GHz 2.33 GHz 434 MHz 434 MHz 403.5 MHz 0.9 GHz 0.9-2.05 GHz

and the body, strong reflection occurs at the tissue-medium boundary, leaving a weak signal to penetrate the body which is further attenuated by the lossy tissues. This leaves very weak scattered signal to be used for imaging and requires a system with very large dynamic range [26], [20]. An approach to reduce the reflections is using water as a coupling medium or matching medium. However, water has high loss at imaging frequencies. A low loss coupling medium made of sodium meta silicate gel is proposed for 2.45 GHz in [107]. Similarly, development of a low loss coupling medium at other frequencies is required. Another method to reduce the loss and effectively couple the signal is to design an antenna suitable in shape, size, and efficiency that could be placed very close to the body. In [95], design of such an UWB antenna is presented for breast cancer detection that can be placed close to the body and can couple 90% of the microwave power to the tissues. B. Contrast Agents A significant difference in the electrical properties of the malignant tissue and the healthy tissues of the same kind is used as a basis for detection of such malignant tissues. However, if any healthy tissue having a very small difference in the electrical properties with this malignant tissue is present in a close proximity, microwave imaging may fail to distinguish between these two kinds of tissues. As for example, there exist a large contrast between the breast cancerous and the healthy fatty tissues. This difference is small when the cancerous tissue is compared with the healthy glandular or the fibro-connective breast tissue [36]. Hence, the detection of the cancerous tissue



C. Imaging Algorithms

(a)

(b)

Fig. 12. Effect of varying concentration of contrast agent, single-walled carbon nantotube (SWCNT), on tissue-mimicking material (a) relative permittivity (b) conductivity, measured from 0.6 GHz to 20 GHz [110].

in a close vicinity of the glandular tissue may be a challenging task with microwave imaging. One of the methods proposed to tackle this challenge is to use contrast agents that can enhance the contrast of the cancerous tissue [108]-[111]. The idea is to administer a contrast agent to the body by methods like intravenous injection. Some volume of the contrast agent will then reach and bind the cancerous tissues enhancing their electrical properties. In [108], a computational study using 3-D realistic numerical breast phantoms is presented using assumed effects of two contrast agents, microbubles and carbon nanotubes. Differential imaging was done by taking the difference between the image with and without the contrast agent effect and was concluded that small tumors, even below the resolution limit of the tomography system, can be detected. In [109], a feasibility study is done using microbubbles and single-walled carbon nanotubes (SWCNT) as contrast agents in an UWB breast imaging to classify a lesion as malignant or benign. This is done by analyzing the complex natural resonances of the differential backscatter responses before and after application of the contrast agents at the suspicious site. Similarly, [110] uses carbon nanotube for contrast enhancing of the breast tumors. It was shown that 0.22% of SWCNTs concentration by weight can increase the average relative permittivity by 37% and the conductivity by 81% of tissue mimicking material. The measured enhancement in the contrast of the tissue mimicking material is shown in Fig. 12 [110]. Use of magnetic nanoparticles as the contrast agent is discussed in [111] for microwave breast cancer imaging. The method is based on reconstruction of the magnetic contrast induced by the magnetic nanoparticles using the differential scattered signal obtained at the receive antenna in the presence and the absence of the polarizing magnetic field. These research have shown that the challenge of small contrast between the cancerous tissue and any healthy tissue can be addressed, but there still remain several open challenges that need to be addressed. For example, increasing the concentration level of the contrast agents within the pathological tissues [26] as only a low volume of contrast agent can reach the pathological tissues [111]. The contrast agent may also distort the lesion border profile that may be used in classification of lesions as benign and malignant [109]. Hence, development of effective contrast agents can be a possible future direction.

As discussed before, the quantitative microwave imaging algorithms are known to have inherit challenges of an inverse problem like the non-linearity and the ill-posedness. Though several methods have been proposed to attack the problem [45], they still suffer from several limitations. For example, oscillations occur in the estimated electrical properties at the boundary of the high dielectric contrast tissues with both Newton and MR-CSI methods though employing different regularization methods [45]. It was suggested that an adaptive-density grid and regularization schemes for both the EM field and the electrical properties might overcome this problem [45]. Moreover, a 2-D based imaging algorithm may not work for a 3-D biological object, and 3-D imaging algorithms are computationally demanding. Moreover, as listed in Table I, each of the imaging algorithms has its own limitations. Selecting a particular algorithm for a given application, depends upon available computational resources. Hence, further in-depth studies are required for development of a computationally efficient and more accurate inversion algorithm. Adoption of the algorithm for parallel computing can reduce the time complexity to some extent [45]. D. Signal Processing For sensing applications as the heartbeat detection, removal of the motion and respiration artifacts of a patient, and the background clutter remains an open challenge to effectively extract the heartbeat from the reflected signal [13]. Hence, a robust signal processing algorithm is required for such cases to remove such artifacts from the received signal. Moreover, qualitative imaging approaches use backscattered data for breast tumor or brain stroke detection. In such cases, signal processing is required to remove the skin backscatter and late time clutter response to effectively focus the tumor or the stroke. Towards this end, some signal processing algorithms have been developed to tackle skin reflections [64] and clutter response [80]. E. Antennas and Measurement System To relax the non-uniqueness and the ill-posedness of nonlinear inverse problems to some extent, large number of antennas have to be used so that the scattered field data set from which the electrical properties are retrieved is large [49]. However, the number of antennas that can be used is limited by their finite size. Moreover, placing the antennas too close to each other may result in high mutual coupling between the antennas introducing error in the measured scattered field. Other errors that might get introduced in a measurement setup are cable losses, phase shifts, or mismatch at the connectors [112]. Further, it is the electric field that is required in the inversion, and using a vector network analyzer based measurement system would measure the path-gain between the transmit and the receive antennas. Hence, calibration of the measured data is required. A calibration process to remove some of these errors is suggested in [112], [113]. Further, [112] provides a method to avoid the frequencies where the coupling is large enough to



prevent successful imaging in a wideband measurement setup. Another method to avoid coupling is to use a virtual array to scan the body, but at a cost of increased scan-time. Therefore, the design of a measurement setup having an optimum scantime, low error, and also a better antenna design having a minimal mutual coupling can be a future research direction. Moreover, further research on effective calibration methods of the measured data is also needed. The error may also occur in the EM system model for the nonlinear optimization problem due to the presence of nonactive antennas if a fixed antenna array is used for microwave imaging. Typically, the perturbations caused in the scattering field due to the presence of these non-active antennas are not taken into account in the EM system model. Moreover, an error may also get introduced due to the assumption of infinite matching medium. Hence, calibration methods as well as a proper measurement system which introduces less error are required. A compensation method for nonactive antennas is discussed in [114]. A formulation that provides a way to compensate the perturbations resulting from the presence of an array of antennas around the imaging body is proposed in [115]. Mojabi et al. in [116] proposes a measurement setup using a rotatable conductive enclosure using a minimal antenna array. It is discussed that such a setup will reduce errors due to the assumption of infinite matching medium as the boundary conditions that can be easily modeled. Moreover, lesser number of the antennas used in the setup will further reduce any error because of the presence of many nonactive antennas. F. Frequency Band and Resolution Another open issue in MSI is choosing a suitable imaging frequency. As discussed in Section IV, different applications or even for the same application, different frequencies have been used. A choice of suitable frequency band is a tradeoff between the penetration depth and the resolution. It is well known that the penetration depth decreases with the frequency due to increased attenuation in the tissues, however, the resolution increases with the frequency. Moreover, a frequency used for imaging one body part may not be optimal for another body part due to the difference in the size and the tissue compositions. Hence, further investigations are required for developing a standard with an optimum frequency and an acceptable resolution. G. Electromagnetic Interference and Noise A clinical environment is an environment where EM interference abounds. Hence, using an MSI system in a clinical environment is a challenging task as it will be susceptible to EM interference and noise resulting in error in the measured data. The sources of such interference and noise in a clinical environment are RF emissions from wireless devices like mobile phones, medical telemetry, wireless local area network and other devices as radiology equipments, electrocautery equipments, fluorescent lights and computers [117]. Further, noisy electrical power supplies and grounding (earth), magnetic fields (static and alternating), and surges (static discharge,

lightning) may also contribute to interference and noise in a medical facility [118]. Thus, development of MSI system that is robust to these EM interference is required. Furthermore, the MSI system must not be a source of interference to other wireless systems. It should follow the appropriate regulations and standards set for medical devices. H. Commercial Challenge Apart from aforementioned technical challenges, one of the non-technical challenge that would be faced by the MSI systems to be embraced by clinician is competition from other well established imaging modalities like MRI, CT or ultrasound. As mentioned earlier, though the MSI system has advantages such as low risk, mobility, time resolution, and cost-effectiveness, it lacks especially in spatial resolution compared to CT or MRI, that may be desired for a clear interpretation of the reconstructed images in some applications. However, in spite of these limitations, there have been startup companies based on MSI technology as EMTensor [8] and Medfield Diagnostics [119] for stroke diagnosis, and Micrima [120] for breast imaging, making a mark in the medical imaging industry that has been so far dominated by MRI or CT modalities. The success of such companies based on MSI technology would certainly depend upon how well MSI overcomes the technical as well as commercial challenges from other well established modalities. I. Other Future Directions Some of the other future research work will naturally follow the ways and means to improve the signal excitation, detection, and the reconstruction techniques to obtain high quality, low noise signals and images. In addition, novel approaches like using multiple-input-multiple-output (MIMO) techniques can be considered for reducing the complexity of imaging systems [121] or improving the image by increasing the signal to clutter ratio when compared with a bistatic or a monostatic configuration [122]. Moreover, 3-D reconstruction with temporal information in real time will be useful for new applications like obtaining temporal images of a beating heart. Another direction that can be explored for MSI is compressive sensing. Compressive sensing is a method by which signals can be reconstructed by sampling them at a rate much lower than the Nyquist rate. However, for such a reconstruction, following criteria have to be met: “sparse” representation of the unknown signal in some domain, and “incoherence” of the signal used for measurement with respect to the unknown signal. Compressed sensing has been shown to substantially improve the performance of ranging with an UWB medical radar in comparison with more conventional methods in scenarios with low signal-to-noise-ratio [123]. VI. C ONCLUDING R EMARKS Microwave imaging and sensing (MSI) opens new opportunities in the field of medical imaging. MSI is shown to be low-risk due to the non-ionizing EM signal used, low-cost, portable, and have a good temporal resolution. This paper



presented an exhaustive summary of various quantitative and qualitative imaging methods for medical MSI. It was discussed that the physical basis of these quantitative and qualitative imaging methods is the difference in the electrical properties of different tissues. Quantitative methods solve the ill-posed inverse EM problem through different iterative schemes to minimize the error between the measured and the modeled scattered field to obtain the electrical properties. Qualitative methods avoid solving ill-posed inverse problem by using radar-based techniques, and can be used in cases where the objective is the detection of the strong scatterer like the tumor. Various medical imaging applications using microwaves for which proof-of-concept have been shown were discussed. These applications include heartbeat, brain imaging for stroke and edema detection, bone imaging for density measures, breast cancer detection, heart imaging, and soft tissue imaging. Apart from these applications, MSI has shown to work for anatomical imaging of different body parts like arms, torso, and also full-body. The feasibility studies of MSI for these various applications shows the potential MSI has to be used as a low-risk and a low cost alternative to the currently used imaging modalities like MRI and CT-scan. Successful initial clinical trials, especially for breast cancer imaging and stroke detection using MSI have been done. Commercial start-up companies using MSI technology like Micrima [120] (for breast cancer), EMtensor [8] and Medfield Diagnostics (for brain stroke) are involved in clinical studies. However, for many other applications as listed in Table II, MSI is still far from maturity as many open challenges remain that have to be addressed before MSI can be used in a clinical setting. The paper discussed these open topics and gave some insight on what could be the probable solutions and future research directions for MSI. ACKNOWLEDGMENT R. Chandra acknowledges the financial support provided by the European Research Consortium for Informatics and Mathematics (ERCIM) ‘Alain Bensoussan’ Fellowship Programme. H. Zhou, I. Balasingham, and R. M. Narayanan acknowledge the financial support provided by the Research Council of Norway through the MELODY project under the contract number 225885/O70. R EFERENCES [1] L. Fass, “Imaging and cancer: a review,” Mol. Oncol., vol 2, no. 2, pp. 115-152, Aug. 2008. [2] S. Semenov, “Microwave tomography: review of the progress towards clinical applications,” Philos. Trans. A Math. Phys. Eng. Sci., pp. 30213042, Aug. 2009. [3] E.C. Fear et al., “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. [4] S. Y. Semenov, and D. R. Corfield, “Microwave Tomography for Brain Imaging: Feasibility Assessment for Stroke Detection,” Int. J. Antennas Propag., vol. 2008, Article ID 254830, 2008. [5] J. C. Lin, and M. J. Clarke, “Microwave imaging of cerebral edema,” Proc. IEEE, vol. 70, no. 5, pp. 523-524, May 1982. [6] M. Persson et al., “Microwave-Based Stroke Diagnosis Making Global Prehospital Thrombolytic Treatment Possible,” IEEE Trans. Biomed. Eng., vol. 61, no. 11, pp. 2806-2817, Nov. 2014.

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Huiyuan Zhou received his B.E. degree from Honor School in Harbin Institute of Technology, China, in 2010, and his M.E. degree from School of Electronics and Information Engineering at Harbin Institute of Technology, in 2012. Currently, he is a Ph.D. candidate from Pennsylvania State University, University Park, USA. His current research interests include electromagnetic (EM) inverse scattering, image reconstruction, and microwave circuits.



Ilangko Balasingham (SM’11) received the M.Sc. and Ph.D. degrees from the Department of Electronics and Telecommunications, Norwegian University of Science and Technology (NTNU), Trondheim, Norway in 1993 and 1998, respectively, both in signal processing. He performed his Masters degree thesis at the Department of Electrical and Computer Engineering, University of California Santa Barbara, USA. From 1998 to 2002, he worked as a Research Scientist developing image and video streaming solutions for mobile handheld devices at Fast Search and Transfer ASA, Oslo, Norway, which is now part of Microsoft Inc. Since 2002 he has been with the Intervention Center, Oslo University Hospital, Oslo, Norway as a Senior Research Scientist, where he heads the Wireless Sensor Network Research Group. He was appointed as a Professor in Signal Processing in Medical Applications at NTNU in 2006. His research interests include super robust short range communications for both in-body and onbody sensors, body area sensor network, microwave short range sensing of vital signs, short range localization and tracking mobile sensors, and nanoneural communication networks. He has authored or co-authored 192 journal and conference papers, 5 book chapters, 42 abstracts, 2 patents, and 10 articles in media. Ilangko has given 14 invited/keynotes at the international conferences. In addition to organizing special sessions and workshops on wireless medical technology at the major conferences and symposiums, he served as General Chair of the 2012 Body Area Networks (BODYNETS) conference and serves as TPC Co-Chair of the 2015 ACM NANOCOM and Area Editor of Elsevier Nano Communication Networks. He is a Senior IEEE member.

Ram M. Narayanan (F’01) received his B.Tech. degree from the Indian Institute of Technology, Madras, in 1976, and his Ph.D. degree from the University of Massachusetts, Amherst, in 1988, both in electrical engineering. From 1976 to 1983, he worked as an R& D engineer at Bharat Electronics Ltd., Ghaziabad, where he developed microwave communications equipment. In 1988, he joined the Electrical Engineering Department at the University of NebraskaLincoln, where he last served as Blackman and Lederer Professor. Since 2003, he has been a professor of electrical engineering at The Pennsylvania State University. He has coauthored 115 journal papers and over 300 conference publications. His current areas of interest are noise radar, medical radar applications, radar networks, and compressive sensing. He currently serves as Member of the IEEE Committee on Ultrawideband Radar Standards Development and as Associate Editor for Radar for the IEEE Transactions on Aerospace and Electronic Systems.


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