Microwave Medical Imaging Research Articles

Synthetic Microwave Focusing Techniques for Medical Imaging: Fundamentals, Limitations, and Challenges.

Synthetic microwave focusing methods have been widely adopted in qualitative medical imaging to detect and localize anomalies based on their electromagnetic scattering signatures. This paper discusses the principles, challenges, and limitations of synthetic microwave-focusing techniques in medical applications. It is shown that the various focusing techniques, including time reversal, confocal imaging, and delay-and-sum, are all based on the scalar solution of the electromagnetic scattering problem, assuming the imaged object, i.e., the tissue or object, is linear, reciprocal, and time-invariant. They all aim to generate a qualitative image, revealing any strong scatterer within the imaged domain. The differences among these techniques lie only in the assumptions made to derive the solution and create an image of the relevant tissue or object. To get a fast solution using limited computational resources, those methods assume the tissue is homogeneous and non-dispersive, and thus, a simplified far-field Green's function is used. Some focusing methods compensate for dispersive effects and attenuation in lossy tissues. Other approaches replace the simplified Green's function with more representative functions. While these focusing techniques offer benefits like speed and low computational requirements, they face significant ongoing challenges in real-life applications due to their oversimplified linear solutions to the complex problem of non-linear medical microwave imaging. This paper discusses these challenges and potential solutions.

A Circular Shape Arc Slot Ultra-Wideband Antenna for Biomedical Applications

In modern communication systems, ultra-wideband (UWB) technology has garnered substantial attention due to its superior attributes compared to traditional narrowband communication systems. Over the past decade, UWB technology has also found applications in microwave-based imaging systems. This study introduces a simple planar coplanar waveguide-fed circular shape arc slot antenna designed specifically for biomedicine and microwave medical imaging applications. The proposed design is implemented on a 1.6-mm-thick FR4 substrate with a relative permittivity of 4.4 and a loss tangent of 0.0009. The antenna has physical dimensions of 26 mm × 29 mm and achieves an impressive bandwidth of 16.6 GHz, spanning 2.4 to 19 GHz. It exhibits a peak gain of 2.5 dBi and consistent omnidirectional radiation characteristics. Thorough temporal analysis validates the antenna's performance within acceptable limits, which is further affirmed through practical fabrication and testing, demonstrating strong agreement with simulation results.

Perspective: Microwave Medical Imaging Using Space-Time-Frequency A Priori Knowledge for Health Monitoring

Perspective: Microwave Medical Imaging Using Space-Time-Frequency A Priori Knowledge for Health Monitoring

UWB Antenna with Enhanced Directivity for Applications in Microwave Medical Imaging.

Microwave medical imaging (MMI) is experiencing a surge in research interest, with antenna performance emerging as a key area for improvement. This work addresses this need by enhancing the directivity of a compact UWB antenna using a Yagi-Uda-inspired reflector antenna. The proposed reflector-loaded antenna (RLA) exhibited significant gain and directivity improvements compared to a non-directional reference antenna. When analyzed for MMI applications, the RLA showed a maximum increase of 4 dBi in the realized gain and of 14.26 dB in the transmitted field strength within a human breast model. Moreover, it preserved the shape of time-domain input signals with a high correlation factor of 94.86%. To further validate our approach, another non-directional antenna with proven head imaging capabilities was modified with a reflector, achieving similar directivity enhancements. The combined results demonstrate the feasibility of RLAs for improved performance in MMI systems.

Transfer Deep Learning for Dielectric Profile Reconstruction in Microwave Medical Imaging

Transfer Deep Learning for Dielectric Profile Reconstruction in Microwave Medical Imaging

A Novel Analysis of Super-Resolution for Born-Iterative-Type Algorithms in Microwave Medical Sensing and Imaging.

Microwave medical sensing and imaging (MMSI) is a highly active research field. In MMSI, electromagnetic inverse scattering (EIS) is a commonly used technique that infers the internal characteristics of the diseased area by measuring the scattered field. It is worth noting that the image formed by EIS often exhibits the super-resolution phenomenon, which has attracted much research interest over the past decade. A classical perspective is that multiple scattering leads to super-resolution, but this is subject to debate. This paper aims to analyze the super-resolution behavior for Born-iterative-type algorithms for the following three aspects. Firstly, the resolution defined by the traditional Rayleigh criterion can only be applied to point scatterers. It does not suit general scatterers. By using the Sparrow criterion and the generalized spread function, the super-resolution condition can be derived for general scatterers even under the Born approximation (BA) condition. Secondly, an iterative algorithm results in larger coefficients in the high-frequency regime of the optical transfer function compared to non-iterative BA. Due to the anti-apodization effect, the spread function of the iterative method becomes steeper, which leads to a better resolution following the definition of the Sparrow criterion mentioned above. Thirdly, the solution from the previous iteration, as the prior knowledge for the next iteration, will cause changes in the total field, which provides additional information outside the Ewald sphere and thereby gives rise to super-resolution. Comprehensive numerical examples are used to verify these viewpoints.

Narrow-band Circularly Polarized Antenna for Medical Microwave Imaging and Health Monitoring Applications

In this paper, a circularly polarized printed monopole antenna (CPPMA) is proposed for medical microwave imaging and health monitoring applications. The proposed CPPMA is optimized to operate at the Industrial, Scientific and Medical (ISM) band. A prototype of the designed antenna is fabricated and printed on the low-cost FR-4 substrate that has a compact size of 34 × 28 × 1.5 mm3. The simulated results indicate that the designed CPPMA operates between 2.425 GHz and 2.475 GHz while the measured results range between 2.32 GHz and 2.515 GHz. The designed CPPMA also reveals a circular polarization performance at 2.45 GHz (2.4386 GHz - 2.4633 GHz). The suitability of CPPMA for microwave imaging is confirmed by checking its aptitudes to detect the presence of breast tumors and brain strokes. A great detection capability is achieved for breast tumors and brain strokes of various sizes inserted at different positions with a high sensitivity to changes or anomalies in the dielectric properties of human tissues. In addition, the usefulness of the proposed CPPMA for wearable application is justified experimentally. Excellent agreement is achieved between the simulated results and the measured ones.

Quantitative Analysis of Super Resolution in Electromagnetic Inverse Scattering for Microwave Medical Sensing and Imaging.

Microwave medical sensing and imaging (MMSI) has been a research hotspot in the past years. Imaging algorithms based on electromagnetic inverse scattering (EIS) play a key role in MMSI due to the super-resolution phenomenon. EIS problems generally employ far-field scattered data to reconstruct images. However, the far-field data do not include information outside the Ewald's sphere, so theoretically it is impossible to achieve super resolution. The reason for super resolution has not been clarified. The majority of the current research focuses on how nonlinearity affects the super-resolution phenomena in EIS. However, the mechanism of super-resolution in the absence of nonlinearity is routinely ignored. In this research, we address a prevalent yet overlooked problem where the image resolution due to scatterers of extended structures is incorrectly analyzed using the model of point scatterers. Specifically, the classical resolution of EIS is defined by the Rayleigh criterion which is only suitable for point-like scatterers. However, the super-resolution in EIS is often observed for general scatterers like cylinders, squares or Austria shapes. Subsequently, we provide theoretical results for the Born approximation framework in EIS, and employ the Sparrow criteria to quantify the resolution for symmetric objects of extended structures. Furthermore, the modified Sparrow criterion is proposed to calculate the resolution of asymmetric scatterers. Numerical examples show that the proposed approach can better explain the super-resolution phenomenon in EIS.

Wearable Microwave Medical Sensing for Stroke Classification and Localization: A Space-Division-Based Decision-Tree Learning Method

Microwave medical imaging systems have shown a competitive advantage in stroke detection due to their cost-effectiveness, non-ionization, and portability. However, these systems often rely on time-consuming image reconstruction techniques, which are disadvantageous for timely stroke treatment. In this paper, a novel microwave medical sensing (MMS) method is proposed for fast stroke classification and localization, which utilizes space division of the region under examination (i.e., the head). The space division is enabled by the generalized scattering matrix theory and incorporates the brain anatomy. Then a novel decision tree learning method is proposed, which facilitates efficient stroke feature identification for classification. The spatial information acquired from the decision tree also results in rapid stroke localization. In order to verify the proposed method, we investigate the feasibility of classifying brain strokes between an intracranial haemorrhage stroke and an ischemic stroke with a wearable MMS system. Both numerical and experimental results are obtained. Compared to the traditional method, the classification rates for simulation and experimental results are improved by 14.1% and 19.2%, respectively. Furthermore, by utilizing the a priori information, the localization time is reduced by 21.1%. Finally, the localization accuracy is higher than 0.90 in both simulation and experimental studies. The classification accuracy and localization efficiency are shown to be greatly improved compared to the traditional method, which has great significance for wearable devices. This study proposes an efficient space-division-based detection method to localize the brain stroke without imaging.

3-D EM Modeling of Medical Microwave Imaging Scenarios With Controllable Accuracy

Medical microwave imaging (MMWI) is a multidisciplinary task that requires precise electromagnetic modeling of heterogeneous human tissues, solving non-linear and ill-posed inverse scattering problems with weak contrasts, and design of compact antenna arrays well-matched to adjacent body parts. If any of the activities listed above does not perform well, the resulting imaging will be poor. In this article, we devote a special effort to achieving high accuracy in electromagnetic simulations of MMWI scenarios. This complex task requires developing a suitable meshing procedure, which balances model fidelity and computational bottlenecks such as a large number of unknowns in the electromagnetic model. Thus, instead of a simple conversion from triangles to quads, we offer a technique that generates high-quality mesh (in terms of both simulation speed and geometrical accuracy) with controllable deviation from the initial model. In addition, we developed a strategy for self-convergence tests that prove the reliability of the simulation results in the MMWI framework. Moreover, an additional validity test has been proposed, based on the inverse scattering operator, commonly used in many microwave imaging algorithms. The effectiveness of these novel techniques is demonstrated with the example of a 21-antenna system placed around the 5-tissue head phantom with and without stroke.

Fast and Accurate CNN-Based Machine Learning Approach for Microwave Medical Imaging in Cancer Detection

Fast and Accurate CNN-Based Machine Learning Approach for Microwave Medical Imaging in Cancer Detection

Real-Time 3D Microwave Medical Imaging With Enhanced Variational Born Iterative Method.

In this paper, we present a new variational Born iterative method (VBIM) for real-time microwave imaging (MWI) applications. The S-parameter volume integral equation and waveport vector Green's function are implemented to utilize the measured signal of the MWI system. Meanwhile, the real and imaginary separation (RIS) approach is used at each iterative step to simultaneously reconstruct the dielectric permittivity and conductivity of unknown objects. Compared with the Born iterative method and distorted Born iterative method, VBIM requires less computational time to reach the convergence threshold. The graphics processing unit based acceleration technique is implemented for real-time imaging. To demonstrate the efficiency and accuracy of this VBIM-RIS method, synthetic analysis of a complex multi-layer spherical phantom is first conducted. Then, the algorithm is tested with measured data using our new MWI system prototype. Finally, a synthetic brain-tumor phantom model under a thermal therapy procedure is monitored to exemplify the real-time imaging with about 5 seconds per reconstruction frame.

Multi-Element UWB Probe Optimization for Medical Microwave Imaging

The need for non-ionizing techniques for medical imaging applications has led to the use of microwave signals. Several systems have been introduced in recent years based on increasing the number of antennas and frequency bandwidth to obtain high resolution and good accuracy in locating objects. A novel microwave imaging system that reduces the number of required antennas for precise target location appropriate for medical applications is presented. The proposed system consists of four UWB extended gap ridge horn (EGRH) antennas covering the frequency band from 0.5 GHz to 1.5 GHz mounted on a cylindrical phantom that mimics the brain in an orthogonal set of two EGRH probes. This configuration has the ability to control both the longitudinal and transversal dimensions of the reconstructed target's image, rather than controlling the spatial resolution, by increasing the frequency band that can be easily affected by medium losses. The system is tested numerically and experimentally by the detection of a cylindrical target within a human brain model.

Effect of Coupling Medium on Penetration Depth in Microwave Medical Imaging.

In microwave medical imaging, the human skin reflects most of microwave energy due to the impedance mismatch between the air and the body. As a result, only a small portion of the microwave energy can enter the body and work for medical purpose. One solution to tackle this issue is to use a coupling (or matching) medium, which can reduce unwanted reflections on the skin and meanwhile improve spatial imaging resolution. A few types of fluids were measured in this paper for their dielectric properties between 500 MHz and 13.5 GHz. Measurements were performed by a Keysight programmable network analyzer (PNA) with a dielectric probe kit, and dielectric constant and conductivity of the fluids were presented in this paper. Then, quantitative computations were exercised to present the attenuations due to the reflection on the skin and to the loss in each coupling medium, based on the measured liquid dielectric values. Finally, electromagnetic simulations verified that the coupling liquid can allow more microwave energy to enter the body to allow for a more efficient medical examination.

Circular slotted patch with defected grounded monopole patch antenna for microwave-based head imaging applications

Medical microwave imaging (MWI) research has gained more attention in recent decades, particularly with regard to the early detection of brain tumors. It offers numerous benefits over existing imaging technologies, including magnetic resonance imaging (MRI) and computed tomography (CT) scans. Antennas represent a key element of MWI systems, so optimizing their performance is critical. A new, simple patch antenna for MWI is presented in this article. The radiating patch has a circular slot and a tiny triangular cut at the top right corner, and it is designed and manufactured with a low-cost FR4-epoxy substrate with a loss tangent of 0.02 and a dielectric constant of 4.4. The overall dimensions are 0.28λ × 0.22λ × 0.005λ (where λ is the wavelength of a lower frequency of 1.22 GHz). A total impedance bandwidth (<-10 dB) of 2.23 GHz (1.22–3.45 GHz) is achieved with a very decent gain of above 5 dBi (measured) and above 85 % efficiency over the functional range. Time-domain assessment of the antenna confirms minimum signal distortion and shows a better response in terms of group delay (GD) and fidelity factor (FF, 92 %). Measurements confirm the results from simulations (CST and HFSS) with a reasonable degree of agreement. Finally, the 3D-electromagnetic CST simulator was utilized to analyze the behavior of a nine-antenna array placed around a 3D-realistic Hugo-head model; it yielded satisfactory results and proved the practical utility of the proposed antenna in the intended MWI application.

UWB Bowtie Antenna for Medical Microwave Imaging Applications

This work presents a design and experimental validation of a compact ultrawideband (UWB) bowtie antenna and balanced-to-unbalanced (balun) circuit for medical microwave imaging (MWI) applications working in the frequency band of 1–6 GHz. The UWB balun is perpendicularly attached to the planar bowtie arms, whose dimensions were miniaturized by rounding the bowtie edges. The antenna reflection coefficient was lower than −12 dB for tissues with a higher water content. The antenna possesses a high radiation efficiency (over 80%) and very low backward radiation. The predicted and measured specific absorption rate (SAR) distributions together with the electric field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\vert E\vert $ </tex-math></inline-formula> distribution proved a symmetrical radiation pattern. The potential of the presented antenna element was demonstrated by successful reconstruction of the images from the measured data by MWI methods. For this purpose, an MWI system equipped with eight antennas was implemented. The radar approach was used to determine the region of interest (ROI) where the area for microwave tomography reconstruction was identified. The dielectric properties within the ROI were reconstructed by using the Born approximation method at 1 GHz. This confirmed that the antenna can be used as the basis for more accurate multifrequency MWI systems and can be implemented in UWB MWI hybrid systems.

Validation of an RF Image System for Real-Time Tracking Neurosurgical Tools.

A radio frequency (RF)-based system for surgical navigation is presented. Surgical navigation technologies are widely used nowadays for aiding the surgical team with many interventions. However, the currently available options still pose considerable limitations, such as line-of-sight occlusion prevention or restricted materials and equipment allowance. In this work, we suggest a different approach based on a microwave broadband antenna system. We combine techniques from microwave medical imaging, which can overcome the current limitations in surgical navigation technologies, and we propose methods to develop RF-based systems for real-time tracking neurosurgical tools. The design of the RF system to perform the measurements is shown and discussed, and two methods (Multiply and Sum and Delay Multiply and Sum) for building the medical images are analyzed. From these measurements, a surgical tool’s position tracking system is developed and experimentally assessed in an emulated surgical scenario. The reported results are coherent with other approaches found in the literature, while overcoming their main practical limitations. The discussion of the results discloses some hints on the validity of the system, the optimal configurations depending on the requirements, and the possibilities for future enhancements.

Smooth Polynomial Approach for Microwave Imaging in Sparse Processing Framework

We developed a novel qualitative imaging algorithm based on a polynomial approximation of the unknown contrast and sparse ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ) regularization. Contrary to previously published results, we defined polynomial basis functions on subdomains that divide the investigation domain. Moreover, we formulated constraints that ensure the continuity of the contrast on subdomain borders. We showed that the proposed algorithm improved imaging resolution, particularly in multiple target scenarios. We demonstrated that partitioning the investigation domain together with contrast continuity formulation enhanced the numerical stability and reduced the computation time. The obtained results were significantly less sensitive to the regularization parameter values than those obtained using the standard polynomial approximation. Namely, smaller domains allow lower polynomial orders, which are numerically more favorable. Continuity constraints reduce the search space and mitigate the occurrence of false solutions. Another contribution of this study is a novel strategy for regularization parameter selection. We considered different figures of merit and numerical scenarios to study the influence of various parameters involved in the imaging process, such as the polynomial order and number of subdomains. An extensive analysis proved the robustness of the approach against noise. The proposed algorithm was designed for two-dimensional geometry. However, generalization to three-dimensional space is straightforward. The algorithm can also be used with other types of regularization such as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization. Potential applications include medical microwave imaging, in which high resolution and noise immunity are vital features.

Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis.

Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.

Experimental evaluation of a Software-Defined Radio through a Breast Phantom aiming at Microwave Medical Imaging

This paper presents an assessment of a low-cost apparatus aiming at a Microwave Imaging (MWI) system realization targeting women breast cancer detection. Software Defined Radio (SDR) is a programmable device with the potential to circumvent the Vector Network Analyzer usage to accomplish an MWI system, especially during the prototyping stage, once the more suitable system is not established yet. BladeRF, an SDR platform, coupled to a power amplifier and a low noise amplifier, and using two Vivaldi antennas, has been assessed in terms of the radio power ability over two different propagation media: air and a breast phantom mimicking a heterogeneous breast. It features transmitter and receiver power control to deal with different signal attenuation scenarios. The results measured over the air have shown that the bladeRF can provide up to +15 dBm output power at 1 GHz carrier frequency and close to -10 dBm at 6 GHz, and distinguish a received signal as low as -80 dBm from the Vivaldi antenna. Besides, the measured results over the breast phantom have shown a signal to noise ratio above 52 dB along with the carrier frequency increase. Thus, from the performed assessment, the bladeRF met the requirements for MWI system implementation, justifying and motivating its usage.