This study investigates the spatial resolution (SR) and super-resolution effect in microwave tomography in terms of single-frequency measured data. The applied method is based on our recently proposed concept of average SR (ASR). We apply truncated singular value decomposition to calculate a regularized forward-modeling matrix and to limit the truncation index by the acceptable level of the imaging noise. A simple relation of the ASR with the truncation index calculates the ASR in the imaging zone. The described method to calculate SR is quite common, and it considers not only the geometrical parameters of the microwave tomography system and object under test but also the noise in the measured data. This method is applicable to the linear and nonlinear considerations of the inverse scattering problem with respect to two- or three-dimensional solutions. In particular, our investigation confirms the conclusion of some other authors that applying nonlinear inverse scattering methods can achieve the super-resolution imaging even when based on far-field measured signals only.
- This paper investigates the super-resolution effect in microwave tomography by utilizing a new method of analysis of spatial resolution
- The proposed concept of average spatial resolution can examine the dependence of imaging noises on measurement noises
- This work confirms that the nonlinear consideration has advanced in providing the super-resolution effect, which can occur even with far-field measured signals
Microwave tomography (MT) is a method of microwave imaging based on multistatic measurement configuration, which often exploits the cylindrical array of transmitting (Tx) and receiving (Rx) antennas (Pastorino, 2010; Rubæk et al., 2007). The main area of potential MT application is the medical imaging of inner permittivity distribution inside bodies. One of the important characteristics of image quality is the spatial resolution (SR), which defines the minimal distance between the distinguishable points of the object or the minimal image size of a small object. For the purpose of SR reduction, MT systems utilize some matching liquid or matching solid materials and the advantage of multi-input/multi-output (MIMO) approach (Rubæk et al., 2007; Simonov et al., 2012a).
Most imaging theories, including optics and the analysis of microwave or synthetic aperture radar (SAR) imaging, utilize the far-field data and linear (or Born) approximation. In the linear consideration, SR cannot be smaller than the half-wavelength Rayleigh (or diffraction) limit in the medium (Born & Wolf, 1997). A more detailed consideration reveals that this limitation is valid if the imaging system can detect only the far field of a body's scattered waves and the linear inverse solver. Overcoming the Rayleigh limit is practically possible if to gather enough information on the near-field components of scattered waves (Cui et al., 2004; Gilmore et al., 2010; Simonov et al., 2012b, 2014). Cui et al. (2004) regarded that the super-resolution phenomenon occurs when the image resolution is less than 0.25 wavelength. In this work, we consider this quarter-wavelength definition of the super-resolution, although some authors use the half-wavelength Rayleigh criterion.
Microwave reconstruction algorithms control the imaging noise and SR with the help of the regularization parameters (Hansen, 1997; Pastorino, 2010). The imaging noise depends on the receiver noise and other uncertainties in the measured data. Regularization smooths images, increases SR, and reduces the dimension of the image space of the image resolution matrix. Some optimal regularization parameter exists, and any attempt to reduce the corresponding SR leads to the catastrophic growth of the image reconstruction noise. Therefore, we can conclude that SR and the super-resolution effect strongly depend on the signal-to-noise ratio of the detected far and near fields. SR also depends on the sensitivity of Rx antennas to the scattered near field, the total number of measured signals, and the values of illumination-sampling spacing (period).
Note that probe-type antennas with an aperture size remarkably smaller than the half-wavelength provide better sensitivity of Rx antennas to the near field, whereas antennas with wider apertures have worse resolution properties because of the space smoothing of the measured electric field. In this study, we limit our investigation to the case of infinitesimal-sized (point-source) antennas.
According to the Whittaker-Kotel'nikov-Shannon sampling theorem (Meijering, 2002; Unser, 2000), the SR is restricted in the sampling period and corresponds to the antenna spacing in the MT system. Therefore, expecting the sampling period to be smaller than the required imaging SR for detecting scattered waves seems natural. However, doing so for electromagnetic scattering is not always necessary. For example, Bucci and Franceschetti (1989), Bucci and Isemia (1997) demonstrated that the spacing of antennas could be larger than the Nyquist period, and the theorem specified above only restricts the period of scattered field sampling near the body surface. In addition, our investigation showed that the MT system overcomes this limitation because of the positive effect of the multi-illumination approach.
Image reconstruction usually provides to the inhomogeneous distribution of SR over an imaging zone (IZ). This fact is obvious in the case of inhomogeneous permittivity distribution. However, the heterogeneous distribution of SR can take place even for homogeneous materials: The resolution can be remarkably smaller near the antennas than in the central area of the antenna array.
In contrast to linear imaging, the nonlinear consideration reveals some unusual properties. Chew and coworkers (Cui et al., 2004) discovered that for the nonlinear inverse techniques, SR could be remarkably smaller than that for the linear approach and that the super-resolution effect is achievable even if the system measures far-field signals only. They used the following explanation for this effect: “even though … only scattered waves corresponding to propagating waves can be measured, the scattered waves contain high resolution information about the scatterer because of the evanescent-propagating waves conversion in a multiply scattered field.”
Okhmatovski et al. (2012) used an original method to noniteratively solve a nonlinear inverse scattered problem. They examined the advantage of using a Veselago lens in the imaging experiment to observe the super-resolution using far-field measured data only.
The concept of SR is not strictly defined because of several reasons. First, the SR definition, which is based on the full width at half maximum (FWHM) of the point spread function (PSF), depends on the profile of this function. In MT, the profiles of the PSF are usually nonregular or nonsymmetric even for the homogeneous background and linear imaging. The situation with the PSF becomes especially complicated in the case of inhomogeneous media, in which the PSF can be especially irregular. The same properties of the PSF take place, particularly in the low-frequency case of electrical capacitance tomography (Lucas et al., 2015).
Second, the methods of estimating SR for SAR, despite their convenience, speed, and visibility, are quite approximated even in the air environment and do not consider the measurement noises properly. Most of them are based on space spectral analysis, consider propagating the scattered waves only, and replace the actual space spectrum profile with the rect function, which is related to the sinc PSF (Cui et al., 2004; Sheen et al., 2001). Other authors (Devaney, 2005; Devaney & Dennison, 2003) considered SR implicitly and calculated the PSF instead, which do not demonstrate regular profiles.
The current study proposes the concept of an average SR (ASR), which helps to solve the above mentioned problems with SR. The important feature of our approach is that it extends the definition of SR to the case of nonlinear imaging in an inhomogeneous body even if it is arranged in the near zone of the array. In addition, the singular value decomposition (SVD) analysis of the sensitivity matrix helps to consider the noises of measurements.
The ASR assumes a homogeneous distribution of SR in the IZ and can be modeled, for example, with the help of sinc-like basis functions (BFs; Simonov et al., 2017). As far as these BFs are centered in the nodes of the grid with spacing equal to SR, their number is equal to the effective rank of the corresponding image resolution matrix (Wiggins, 1972). Despite the property of averaging in volume, the ASR concept is applicable for investigation of more local SR as well, if to consider a smaller investigation domain and then scan this local region over the whole IZ.
This study examines the single-frequency imaging, investigates the dependence of SR on the MT parameters, and demonstrates some super-resolution cases. We consider imaging as an inverse problem in the framework of L2-norm minimization and do not refer to compressive sensing methods, which are based on the L1/L2-norm minimization (Bingchen et al., 2012). However, some studies claimed that a higher resolution could be achieved with the application of compressive sensing-based imaging algorithms (e.g., Yang & Ling, 2017; Zhu, 2011; Zhu & Bamler, 2012).
2 Theoretical Base of SR Investigation
2.1 Determination of the Concept of ASR
As mentioned in section 1, the existing methods to estimate SR in the SAR imaging theory have certain limitations and are not suitable for MT configuration (Figure 1). First, they do not consider explicitly the effect of measurement noise on SR. Second, they utilize a simple estimation of SR based on the spatial spectrum of scattered waves in the far-field zone or consider PSFs.
For MT, the PSF-based estimation of SR is limited by the irregular profiles of PSFs because the testing body in MT is arranged in the near zone of the antennas. Consequently, the PSF can have an irregular profile and an uneven distribution of SR in space (Figure 2) despite the homogeneous background and Born (i.e., linear) approximation. Clearly, the PSF profile can be especially irregular in the case of nonlinear imaging in heterogeneous media. Therefore, accurately defining the local SR in MT, especially for nonlinear image reconstructions, is not possible. In this situation, Gilmore et al. (2010) introduced the alternative concept of separation resolution.
To overcome these problems, this work introduces the concept of ASR instead of the local SR of the image. The ASR approach is quite general because it is valid for any background with losses and considers the noises of measurements. The developed algorithm calculates the ASR for the linear and nonlinear imaging considerations directly in the entire IZ or in its smaller investigation domains, thus providing an analysis of the local SR.
Bayat and Mojabi (2016) described a similar framework for SR analysis, but they did not derive an ultimate equation for the SR calculation.
Let us apply the measurement configuration for the MT system, as illustrated in Figure 1. The configuration includes a circular array with elements, indicated as bold spots, and an object under test, which are both immersed in the coupling liquid. In this study, we use vertically polarized point Tx and Rx antennas. The array can be planar, scanning in the vertical direction and rotating in the horizontal plane.
The Jacobian is an ill-conditioned matrix, and a direct inverse solution for Cn provides a huge numerical error. To obtain a reasonable approximate inverse of equation 3, different types of regularization are used, providing smooth (space-filtered) solutions (Chew, 1995; Hansen, 1997; Pastorino, 2010). In the present work, we apply a simple truncated SVD (TSVD) regularization (Hansen, 1997; Pastorino, 2010) that provides a fine control of the reconstruction noise.
Simple calculations demonstrate that matrix Rr 6 provides imaging with an inhomogeneous distribution of SR in the IZ. Specifically, the SR for points near the antennas is remarkably smaller than that in the center of the IZ. The same property demonstrates the Tikhonov regularization method (Figure 2), which is equivalent to filtering singular values with a smooth profile filter (Pastorino, 2010, Eq. (5.6.5)). The TSVD and the Tikhonov regularization are related to L2-norm minimization. Note that the PSFs of the resolution matrix have an irregular profile in both cases. Nevertheless, the local SR values in Figure 2 are estimated using the FWHM of the PSFs, that is, the Rayleigh criterion (Born & Wolf, 1997).
2.2 Relation Between Measurement and Imaging Noises
2.3 Definition of ASR
We now explain our method of ASR evaluation by utilizing equation 12. Choosing an acceptable value of the ratio is necessary. Then, using equation 12, we can find the effective rank r of the resolution matrix Rr 6, which depends on the selected ratio . The physical sense of the parameter r is the same as the number of well-resolved (point) targets (Devaney, 2005; Devaney & Dennison, 2003). In other terms, parameter r is a dimension of the image space of the image resolution matrix.
Therefore, we can regard SR for Rbr as an average value of SR that is related to matrix Rr.
2.4 Comparison With SR Based on the PSF
- based on the first zero of the PSF and
- based on the integral over area (or volume) of the PSF.
The related equations are exact for the sinc function and for its 2D and 3D extensions. Although the PSF methods of the SR estimation are not correct in the general case, we can apply them to homogeneous media for ASR verification.
2.4.1 Two-Dimensional Imaging
2.4.2 Three-Dimensional Imaging
2.5 Calculation of the Jacobian in the Born Approximation
In the linear (Born) approximation, the Jacobian is defined by Green's function of the background material and does not depend on the contrast of the object. The Born approximation is acceptable in the case of a small object contrast.
2.5.1 Two-Dimensional Consideration
2.5.2 Three-Dimensional Consideration
2.6 Calculation of the Jacobian for Nonlinear Consideration
In conducting the nonlinear analysis, we consider a mutual coupling (or multiscattering) among all pixels or voxels of the imaging object (Chew, 1995; Pastorino, 2010). This step is valid even in the case of a body with a uniform material. This coupling leads to the nonlinear dependence of the Jacobian on the object contrast.
2.6.1 Two-Dimensional Consideration
2.6.2 Three-Dimensional Consideration
3 Characteristics of the SR in the 2D Consideration and Born Approximation
For simplicity, we use the term SR as an equivalent to the term ASR.
3.1 Dependence of SR on the Spacing of Tx Antennas
3.1.1 Computed Results
Figure 3 shows examples of the SR dependence on the spacing of Tx antennas. We imply the arc spacing of the array elements for all simulated results and occasionally omit the word arc for simplicity. We consider the case of a fixed number of Rx antennas distributed evenly over the contour of the 150-mm diameter of the circular array. The background is the coupling liquid PG 90% (90% solution of propylene glycol in water); its dielectric parameters at testing frequencies of 900, 1,500, 2,100, and 2,900 MHz are shown in Table 1. Here ε is a real part of the permittivity, and σ is the conductivity. We utilize the noise ratio in equation 12 to calculate the ASR 16.
|Frequency (MHz)||Bath liquid PG 90%|
|Ε||σ (S/m)||Diffraction limit Dlim (mm)|
- Note. PG = propylene glycol.
Figure 3 demonstrates that the SRs are below the Rayleigh limits (RLs) for all the considered frequencies, and that the super-resolution occurs if the spacing of the Tx antennas is smaller than some critical values: (1) approximately 3 mm for a 75-mm array diameter and (2) less than 1 mm for a 150-mm array diameter. Figure 3 shows also that for the multistatic setup, the SR can be smaller than the RL even when the spacing of both Tx and Rx antennas exceeds the RL.
Another interesting feature is that SR slowly (logarithmically) decreases when the spacing of Tx antennas tends to 0. Clearly, the MT configuration tends toward setup of the electrical impedance or electrical capacitance tomography. For the last low-frequency system, the SR depends on the spacing of the electrodes but is not limited by the wavelength (Lucas et al., 2015). In any case, the continuing decline of SR is explained by the application of the point-source antennas, but it must be limited with finite apertures of antennas.
3.1.2 Verification of the Computed Results
For verification, we compare the ASR, which is calculated by equation 16, and SR for the PSF in the center of the IZ (Figure 4). The PSFs are calculated on the basis of the image resolution matrix Rr 6.
Figure 5 illustrates the comparison of ASR 16 with the PSF-based SR DFZ 20 and DInt 21. Although the calculated values of SR are close, diversions can be observed in the values DFZ and DInt of the PSF-based SR, thus indicating their principal inaccurate definition.
Values DFZ and DInt of the PSF-based SR demonstrate interesting property, not smooth and threshold-like dependence on the spacing of Tx antennas. This feature is explained by the fact that the cylindrical symmetry causes some singular vectors of the matrix V in 4 and 5 to have the same spatial pattern but different angles of rotation.
3.2 Equivalency of Information Gathering by Tx or Rx Antennas
3.2.1 Simulated Data
With equations 24–29 demonstrating symmetry that relates Tx and Rx antennas, we can expect an equal contribution of the information gathered by Tx or Rx antennas to the SR. The results presented in this section confirm this conclusion, but they also demonstrate a general rule: SR depends mostly on the total number of measurements and weakly depends on the specific numbers of Tx and Rx antennas. We call this property equivalency of information gathering by Tx or Rx antennas (Simonov et al., 2012b). This finding is not directly related to the reciprocity theorem because we compare cases with different positions and numbers of Tx and Rx antennas.
This rule enables, for example, the use of a sampling period over a half-wavelength Nyquist rate and provides the super-resolution at the expense of a small illumination period, as discussed in section 3.1. Note that the same effect takes place in the opposite case, in which the illumination period is relatively large, but the sampling period is small. This finding is also related to the advantage of the MIMO approach in information gathering.
- illumination points and illumination period with respect to the Tx antennas and
- sampling points and sampling period with respect to the Rx antennas.
where Ra is the radius of the circular array.
Figure 6 presents some examples of such dependencies in the semilogarithmic scale. We test a circular array with a 150-mm diameter immersed in a PG 90% bath liquid and operating at 2,100 MHz. The calculation of the SR utilizes the parameter in equation 12. Figure 6 shows the SR dependencies for the selected five values of total measurements Nm: 128, 256, 512, 1,024, and 2,048. The dependence of the SR is symmetrical for the illumination and sampling periods, normalized by the optimal spacing dopt 30.
Figure 6 shows that if the number Nm is fixed, the SR almost does not depend on the specific numbers of sampling and illumination points, Nsmp and Nill, in wide diapasons; that is, the SR mostly depends on the total number of measurements. Clearly, this conclusion assumes the condition that the illumination and sampling points spread evenly in the same observation domain.
However, the side parts of the traces in Figure 6 show some deviations of the SR from its minimal values, that is, when numbers Nsmp or Nill are smaller than 4. A more detailed analysis shows that the reason for such deviations is due to the fact that the PSF profiles in this case become irregular because of the lack of circular symmetry in the location of the antennas.
3.2.2 Verification of the Simulated Data
An example of the PSF image for the point object in the center of the measurement setup of Figure 6 is presented in Figure 7. The background used is PG 90%, the frequency is 2,100 MHz, and 32 Tx and 64 Rx antennas are used.
In this case, the computed ASR and PSF-based estimations of the SR, as defined in equation 20, are close: D = 13.5 mm, DFZ = 13.2 mm, and DInt = 13.5 mm. These values are about 0.6 of the RL value (21 mm).
4 Super-Resolution Effects in the 2D Linear and Nonlinear Considerations
4.1 Super-Resolution for a Low NSR
4.1.1 Computed Results
We investigate the dependence of SR on the variance σm of the average NSR (ANSR) of the measured noise using equations 12 and 16. In our case, we choose the contrast variance σc = 0.1 as a reasonable acceptable parameter of the imaging noise.
Figure 8 shows the dependencies of SR on the ANSR in the interval of ( 10−4…1.0 ) for both linear (a) and nonlinear (b) considerations at 3 GHz calculated with equations 24 and 26. We consider the fully multistatic data measurement setup ( Nsmp= Nill) for the array with a diameter of 150 mm immersed in a 1.3 butylene glycol (13BG) background liquid (see Table 1 for its parameters). As mentioned previously, the array elements are the 2D point source and are vertically oriented. We compare three cases for the spacing of the array elements: 23 mm (diffraction limit for the background), 11.5 mm (half of the diffraction limit for the background), and 6 mm (quarter of the diffraction limit for the background) for both linear and nonlinear imaging. The IZ is bounded by the object surface (Figure 1) with a diameter of 100 mm for both linear and nonlinear considerations.
In the case of the Born approximation (Figure 8a) and the ANSR = 1, the SR Dlin is around the background diffraction limit , but is a very small ANSR value.
For the ANSR = 1, SR is around the background diffraction limit and for ANSR = 1.0E−4 the super-resolution takes place in both Born and nonlinear considerations. Indeed, in linear approximation, , and in the nonlinear consideration (Figure 8b), the SR Dnonlin ≅ 7 mm, which is about 0.15 of the background wavelength and even becomes below the diffraction limit of the material of the object ( ). The close value of the SR emerges even in the case of the same NSR and an array spacing equal to the background diffraction limit (23 mm), although it is the Nyquist rate that detects the far-field signals only.
The result is consistent with that of Chew and coworkers (Cui et al., 2004): The super-resolution effect can be observed in the nonlinear image reconstruction even if only far-field measured data are used.
4.1.2 Verification of the Computed Results
Figure 9 presents the images of the point object (PSF) placed in the center of the IZ for the linear and nonlinear reconstructions and for the measurement setup, as described in the previous section (see Figure 8) for a particular case: Tx spacing 6 mm, ANSR = 1.0E−4. These images demonstrate that SR in the nonlinear method is remarkably smaller. We compare ASR, which is calculated by 16, with the SR estimations based on the PSF: , , and DInt equations 20 and 21). The results of the comparison are given in Table 2.
- Note. Measurement setup of Figure 8: Tx spacing 6 mm, NSR 10E−4, frequency 3 GHz, 13BG background. point spread function; Tx = transmitting; NSR = noise-to-signal ratio; 13BG = 1.3 butylene glycol.
5 Investigation of the Super-Resolution in the 3D Nonlinear Consideration
5.1 Computed Results
In addition to the 2D case, the 3D consideration involves the dependence of ASR on the vertical scan period. Figure 10 gives an example of the analysis at 3 and 6 GHz.
Here we test the MT configuration with the antenna array with a diameter 180 mm immersed in the 13BG liquid. This array can scan in the vertical direction and in the horizontal plane, rotating with a period of 4.5°. The illumination period is 7 mm, and the sampling period is 35 mm in the horizontal plane. The object under test is the cylinder of the ED breast tissue phantom with a diameter of 100 mm and height of 40 mm (Figure 1). Table 3 shows the parameters of all the dielectric media.
|Ε||σ (S/m)||Ε||σ (S/m)||ε||σ (S/m)|
- Note. 13BG = 1.3 butylene glycol.
We produce the 3D nonlinear consideration with noise relation in equation 12 to calculate the ASR. The IZ is slightly larger than the object volume. Figure 10 shows the lines of the diffraction limits for the background and the ED medium of the phantom.
Figure 10a indicates that the ASR is close to the super-resolution at 3 GHz with respect to the background materials but is above the RL level in terms of the material of the object. Figure 10b shows that the ASR becomes almost 2 times smaller than the RL at 6 GHz. However, in contrast to the 3-GHz case, it is above the RL level in terms of the material of the object.
5.2 Verification of the Computed Results and Discussion
To verify the obtained ASR data, we compute the image resolution matrix Rr 6 and the PSF for the point in the center of the IZ. Figure 11 shows the horizontal (x, y) and vertical (x, z) sections of the 3D PSF in the case of 3-GHz and 2-mm period of the vertical array scan. The SR is considerably larger in the vertical than in the horizontal direction. This finding can be explained by the fact that the measurement setup is multistatic in the horizontal plane but quasi-monostatic in the vertical direction. This property is reflected in the calculated PSF-based SR , , , DFZ, and DInt, as defined by equations 22 and 23. ASR D is presented in Table 4. Figure 10 illustrates the value comparison of D, SR DFZ (circles), and DInt (crosses).
- Note. Measurement setup of Figure 10: frequency 3 GHz, 13BG background.; 13BG = 1.3 butylene glycol.
Given the diffraction limit of 23 mm for the 13BG background and 12 mm for the ED phantom for 3 GHz, Table 4 shows that the horizontal SR , corresponds to the super-resolution for the background and is even smaller than the RL for the body. The values of the averaged SR DFZ, DInt, and D are greater than the quarter-wavelength level (see Figure 10 and Table 4) and are related to the effect of a relatively large vertical component of SR (e.g., equation 23).
The presented 3D analysis demonstrates that the nonlinear image reconstruction can provide the super-resolution, consistent with the findings by Cui et al. (2004). The main conclusion of these authors is that the scattered propagating waves carry information about the evanescent waves inside the object, which appear because of multiple scattering. This multiscattering provides coupling and mutual transformations of evanescent and propagating waves to the contrast heterogeneity. A similar mechanism was considered by the study of Simonov (1998, 2000), which showed that the coupling between the propagating plane wave and the evanescent waves scattered in the inhomogeneous material explains its nonlocal properties.
Although we use homogeneous phantoms, the nonlinear reconstruction provides the multiscattering consideration by multiplying the Jacobian by the matrix IGC in 27 and 29. This finding explains the super-resolution effects obtained in the current and previous sections.
6 Inhomogeneous Distribution of SR in the Reconstructed Image of Phantom
For additional verification of the suggested ASR approach, we demonstrate here an example of nonlinear image reconstruction of 2D phantom (Figure 12) at frequency 3 GHz. This phantom, immersed into a matching liquid, consists of circular body with relatively big contrast and two circular inclusions with lower contrast. The body's parameters, matching liquid, and measurement setup correspond to that of section 4.1.1; the difference is only in the ability of two inclusions. The body's material is ED, the background liquid is 13BG, the inclusions material is most fatty breast type (see averaged dielectric parameters in Table 3 at 3 GHz; Shea et al., 2010). The body's diameter is 100 mm, the diameter of inclusions is 32 mm, and the gap between inclusions is 8 mm. We used the contrast variance 0.1 and the ANSR = 1.0E−4, and the spacing of array elements is 11.8 mm (compare with Figure 8b). The calculated value of the ASR in the phantom is D = 7.3 mm for the nonlinear consideration, at 3 GHz, and for the investigation domain, coincided with the phantom shape. Note that this D is close to the 8-mm gap between the inclusions.
Figure 12d demonstrates the smoothed image of the contrast, which is the product of original contrast vector C (Figure 12c with the image resolution matrix Rr 6, which corresponds to nonlinear image reconstruction. We calculate PSFs as columns of matrix Rr, and Figure 13 shows that their images in different points of the phantom have different shapes and SR. PSF in Figure 13a is located in the center point between the inclusions, having an elongated form, while PSF in Figure 13b corresponds to the point above the inclusions and has a round profile. Notice that the SR of PSFs in Figures 13c and 13d is bigger than that of PSFs in Figures 13a and 13b, due to the influence of the lower contrast of the inclusions. Actually, PSF in Figure 13c is located in the center of the inclusion, and PSF in Figure 13d corresponds to the edge of the inclusion. All images of PSFs in Figure 12 demonstrate that the evaluated ASR = 7.3 mm is sa reasonable value.
- array diameter—150 mm
- spacing of array elements—11.8 mm
- number of array elements—40
- full multistatic scattering matrix
- testing frequency—3 GHz
We use Nst = 0.3 and 200 iterations for both imaging methods. As an initial guess, we applied homogeneous distribution of contrast over the whole IZ equal to Cig = 1.3 + 0.58i with initial misfit error err(1) = ‖Cig − C‖2/‖C‖2 = 0.84 for both cases. After 200 iterations, the misfit error err(200) = 5.8E−3 for the case of TSVD-like reconstruction, and err(200) = 1.1E−2 for the Tikhonov regularization.
Figure 14 demonstrates reconstructed images (module of contrast and permittivity) for both regularizations. Notice that the images, reconstructed by these different methods, look similar, and it can be concluded that their SR is consistent with the SR of PSFs in Figure 12 and the calculated ASR = 7.3 mm.
This work analyzes the SR and the super-resolution effects, utilizing a TSVD-based approach for the MT system in the linear (Born approximation) and nonlinear modeling in near-field zone, employing 2D and 3D considerations. The following conclusions are formulated.
The SR and the super-resolution effects in the MT depend on many factors, including the aperture size of the antenna, background coupling medium, operating frequency, total number of measurements, average NSR of the measured data, distance between the antennas and the object under investigation, sampling and illumination periods, and method of image reconstruction (linear or nonlinear).
This study examines the case of vertically oriented point sources arranged in a circular array of the MT system. Clearly, the limited-sized antennas provide the SR, which is greater than that for the point sources, because of the space filtering properties of the aperture.
In the Born approximation, the SR of the MT system can be smaller than the illumination or sampling periods because of the MIMO method of information gathering. For example, a super-resolution imaging can be observed even if the illumination or sampling periods exceed the half-wavelength in the background.
Investigations on the 2D linear case demonstrate that the SR depends mostly on the total number of measurements and almost does not depend on specific numbers of illumination and sampling points. This conclusion assumes that in all considered cases, the illumination and sampling points are evenly distributed in the same observation domain. The noticeable deviation from this rule occurs only for sampling or illumination numbers smaller than 4.
This work also examines the case in which the illumination period becomes considerably smaller than the half-wavelength in the matching liquid. This dependence of the SR demonstrates a weak decline (in the semilogarithmic scale) when the argument tends to 0. In the zero-spacing limit, the MT configuration transforms into the electrical capacitance tomography, in which the SR depends on the spacing of the electrodes but is not limited by the wavelength.
This work also confirms results of some previous studies, for example, Cui et al. (2004) and Gilmore et al. (2010) that the nonlinear reconstruction produces an SR that can be remarkably smaller than that for the linear consideration. Furthermore, the super-resolution effect can occur even when the system measures propagating waves only.
This work was supported by the Electronics and Telecommunications Research Institute grant funded by the Korean government (17ZR1400, Research on Beam Focusing Algorithm for Microwave Treatment). According to the AGU data policy, all simulation data of the method proposed in this paper will be available upon request.
- 2016). A mathematical framework to analyze the achievable resolution from microwave tomography. IEEE Transactions on Antennas and Propagation, 64(4), 1484–1489. https://doi.org/10.1109/TAP.2016.2526061
- 2012). Sparse microwave imaging: Principles and applications (review). Science China, Information Sciences, 55(8), 1722–1754. https://doi.org/10.1007/s11432-012-4633-4
- 1997). Principles of optics. Cambridge, UK: Cambridge University Press.
- 1989). On the degrees of freedom of scattered fields. IEEE Transactions on Antennas and Propagation, 37(7), 918–926. https://doi.org/10.1109/8.29386
- 1997). Electromagnetic inverse scattering: Retrievable information and measurement strategies. Radio Science, 32, 2123–2137. https://doi.org/10.1029/97RS01826
- 1995). Waves and fields in inhomogeneous media. Piscataway, NJ: IEEE Press.
- 2001). Inverse scattering of two-dimensional dielectric objects buried in a lossy earth using the distorted Born iterative method. IEEE Transactions on Geoscience and Remote Sensing, 39(2), 339–346. https://doi.org/10.1109/36.905242
- 2004). Study of resolution and super resolution in electromagnetic imaging for half-space problems. IEEE Transactions on Antennas and Propagation, 52(6), 1398–1411. https://doi.org/10.1109/TAP.2004.829847
- 2005). Time reversal imaging of obscured targets from multistatic data. IEEE Transactions on Antennas and Propagation, 53(5), 1600–1610. https://doi.org/10.1109/TAP.2005.846723
- 2003). Inverse scattering in inhomogeneous background media. Inverse Problems, 19(4), 855–870. https://doi.org/10.1088/0266-5611/19/4/305
- 2010). On super-resolution with an experimental microwave tomography system. IEEE Antennas and Wireless Propagation Letters, 9, 393–396. https://doi.org/10.1109/LAWP.2010.2049471
- 1997). Rank-deficient and discrete ill-posed problems: Numerical aspects of linear inversion. Philadelphia: SIAM.
- 2015). Physical limitations on spatial resolution in electrical capacitance tomography. Measurement Science and Technology, 26(12), 125,105–125,112. https://doi.org/10.1088/0957-0233/26/12/125105
- 2002). A chronology of interpolation: From ancient astronomy to modern signal and image. Proceedings of the IEEE, 90(3), 319–342. https://doi.org/10.1109/5.993400
- 2012). A well-conditioned non-iterative approach to solution of the inverse problem. IEEE Transactions on Antennas and Propagation, 60(5), 2418–2430. https://doi.org/10.1109/TAP.2012.2189703
- 2010). Microwave imaging. Hoboken, NJ: John Wiley. https://doi.org/10.1002/9780470602492
- 2007). Nonlinear microwave imaging for breast cancer screening using Gauss-Newton's method and the CGLS inversion algorithm. IEEE Transactions on Antennas and Propagation, 55(8), 2320–2331. https://doi.org/10.1109/TAP.2007.901993
- 1991). Statistical signal processing (Vol. 98). Reading, MA: Addison-Wesley.
- 2010). Three-dimensional microwave imaging of realistic numerical breast phantoms via a multiple-frequency inverse scattering technique. Medical Physics, 37(8), 4210–4226. https://doi.org/10.1118/1.3443569
- 2001). Three-dimensional millimeter-wave imaging for concealed weapon detection. IEEE Transactions on Microwave Theory and Techniques, 49(9), 1581–1592. https://doi.org/10.1109/22.942570
- 2012a). 3D microwave breast imaging based on multistatic radar concept system. Journal of the Korean Institute of Electromagnetic Engineering and Science, 12(1), 107–114. https://doi.org/10.5515/JKIEES.2012.12.1.107
- 2017). Advanced fast 3D electromagnetic solver for microwave tomography imaging. IEEE Transactions on Medical Imaging, 36(10), 2160–2170. https://doi.org/10.1109/TMI.2017.2712800
- 1998). One-dimensional nonlocal model of medium. Paper presented at the 7th International Conference of Complex Media, Braunschweig, Germany. Proc. Bianisotropics'98, 281–284.
- 2000). Two-wave approximation for transition layer of inhomogeneous media. Paper presented at the 8th International Conference on Electromagnetics of Complex Media. Proc. Bianisotropics 2000, 411–414.
- 2012b). About equivalency of two methods of information gathering in microwave imaging. In Proc. ISAP2012 (pp. 487–490). Nagoya, Japan.
- 2014). Investigation of spatial resolution in a microwave tomography system. In Proc. ICEIC2014 (pp. 189–190). Malaysia. https://doi.org/10.1109/ELINFOCOM.2014.6914442
- 2000). Sampling—50 years after Shannon. Proceedings of the IEEE, 88(4), 569–587. https://doi.org/10.1109/5.843002
- 1972). The general linear inverse problem: Implication of surface waves and free oscillations for Earth structure. Reviews of Geophysics and Space Physics, 10(1), 251–285. https://doi.org/10.1029/RG010i001p00251
- 2017). Application of compressive sensing to two-dimensional radar imaging using a frequency-scanned microstrip leaky wave antenna. Journal of Electromagnetic Engineering and Science, 17(3), 113–119. https://doi.org/10.5515/JKIEES.2017.17.3.113
- 2011). Very high resolution tomographic SAR inversion for urban infrastructure monitoring—A sparse and nonlinear tour, (PhD thesis). dDeutsche Geodätische Kommission, Reihe C, Nr. 666, Verlag der Bayerischen Akademie der Wissenschaften.
- 2012). Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR. IEEE Transactions on Geoscience and Remote Sensing, 50(1), 247–258. https://doi.org/10.1109/TGRS.2011.2160183