Signal processing for microwave imaging systems with very sparse array

This dissertation investigates image reconstruction algorithms for near-field, two dimensional (2D) synthetic aperture radar (SAR) using compressed sensing (CS) based methods. In conventional SAR imaging systems, acquiring higher-quality images requires longer measuring time and/or more elements in an antenna array. Millimeter wave imaging systems using evenly-spaced antenna arrays also have spatial resolution constraints due to the large size of the antennas. This dissertation applies the CS principle to a bistatic antenna array that consists of separate transmitter and receiver subarrays very sparsely and non-uniformly distributed on a 2D plane. One pair of transmitter and receiver elements is turned on at a time, and different pairs are turned on in series to achieve synthetic aperture and controlled random measurements. This dissertation contributes to CS-hardware co-design by proposing several signal-processing methods, including monostatic approximation, re-gridding, adaptive interpolation, CS-based reconstruction, and image denoising. The proposed algorithms enable the successful implementation of CS-SAR hardware cameras, improve the resolution and image quality, and reduce hardware cost and experiment time. This dissertation also describes and analyzes the results for each independent method. The algorithms proposed in this dissertation break the limitations of hardware configuration. By using 16 x 16 transmit and receive elements with an average space of 16 mm, the sparse-array camera achieves the image resolution of 2 mm. This is equivalent to six percent of the λ/4 evenly-spaced array. The reconstructed images achieve similar quality as the fully-sampled array with the structure similarity (SSIM) larger than 0.8 and peak signal-to-noise ratio (PSNR) greater than 25 --Abstract, page iv

COMPRESSIVE SENSING FOR SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY

Spectral domain optical coherence tomography (SD OCT) imaging with high axial resolution and a large imaging depth requires a large number of sampling points in the spectral domain. This requires a high-resolution spectrometer with a large linear array camera which leads to a large amount of k-space measurements and a long data acquisition time that makes the imaging susceptible to unavoidable motion artifact. Furthermore such devices can be expensive and require high-speed electronics. In this dissertation, compressive sensing (CS) SD OCT that reconstructs the images using only a portion of the k-space measurements required by the classical Shannon/Nyquist rate was proposed and studied. Several advanced CS SD OCT algorithms have been developed and evaluated. First, modified non-uniform discrete Fourier transform (MNUDFT) matrix was proposed, which enables CS SD OCT using under-sampled non-linear wavenumber spectral data. Second, the noise reduction using Modified-CS was studied which shows that the averaged Modified-CS SD OCT results in better image quality in terms of SNR, local contrast and contrast to noise ratio (CNR), compared to the classical averaging method. Third, a novel three-dimensional (3D) CS SD OCT sampling pattern and reconstruction procedure was proposed. The novel 3D approach enables efficient volumetric image reconstruction using the k-space measurements under-sampled in all three directions and reduces the amount of required measurements to less than 20% of that required by regular SD OCT. CS SD OCT is commonly solved by an iterative algorithm that requires numerous matrix-vector computation, which is computationally complex and time-consuming if solved on CPU-based systems. However, such computation is ideal for parallel processing with graphics processing unit (GPU) which can significantly reduce its computation time. In this dissertation, real-time CS SD OCT was developed on a conventional desktop computer architecture having three GPUs. The GPU-accelerated CS non-uniform in k-space SD OCT and real-time CS SD OCT with dispersion compensation were also proposed and implemented using the same computer architecture. %Real-time CS SD OCT with dispersion compensation was proposed

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

Architectural Support for Medical Imaging

Advancements in medical imaging research are continuously providing doctors with better diagnostic information, removing the need for unnecessary surgeries and increasing accuracy in predicting life-threatening conditions. However, newly developed techniques are currently limited by the capabilities of existing computer hardware, restricting them to expensive, custom-designed machines that only the largest hospital systems can afford or even worse, precluding them entirely. Many of these issues are due to existing hardware being ill-suited for these types of algorithms and not designed with medical imaging in mind. In this thesis we discuss our efforts to motivate and democratize architectural support for advanced medical imaging tasks with MIRAQLE, a medical image reconstruction benchmark suite. In particular, MIRAQLE focuses on advanced image reconstruction techniques for 3D ultrasound, low-dose X-ray CT, and dynamic MRI. For each imaging modality we provide a detailed background and parallel implementations to enable future hardware development. In addition to providing baseline algorithms for these workloads, we also develop a unique analysis tool that provides image quality feedback for each simulation. This allows hardware designers to explore acceptable image quality trade-offs in algorithm-hardware co-design, potentially allowing for even more efficient solutions than hardware innovations alone could provide. We also motivate the need for such tools by discussing Sonic Millip3De, our low-power, highly parallel hardware for 3D ultrasound. Using Sonic Millip3De, we illustrate the orders-of-magnitude power efficiency improvement that better medical imaging hardware can provide, especially when developed with a hardware-software co-design. We also show validation of the design using a scaled-down FPGA proof-of-concept and discuss our further refinement of the hardware to support a wider range of applications and produce higher frame rates. Overall, with this thesis we hope to enable application specific hardware support for the critical medical imaging tasks in MIRAQLE to make them practical for wide clinical use.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137105/1/rsamp_1.pd

Advanced ultrawideband imaging algorithms for breast cancer detection

Ultrawideband (UWB) technology has received considerable attention in recent years as it is regarded to be able to revolutionise a wide range of applications. UWB imaging for breast cancer detection is particularly promising due to its appealing capabilities and advantages over existing techniques, which can serve as an early-stage screening tool, thereby saving millions of lives. Although a lot of progress has been made, several challenges still need to be overcome before it can be applied in practice. These challenges include accurate signal propagation modelling and breast phantom construction, artefact resistant imaging algorithms in realistic breast models, and low-complexity implementations. Under this context, novel solutions are proposed in this thesis to address these key bottlenecks. The thesis first proposes a versatile electromagnetic computational engine (VECE) for simulating the interaction between UWB signals and breast tissues. VECE provides the first implementation of its kind combining auxiliary differential equations (ADE) and convolutional perfectly matched layer (CPML) for describing Debye dispersive medium, and truncating computational domain, respectively. High accuracy and improved computational and memory storage efficiency are offered by VECE, which are validated via extensive analysis and simulations. VECE integrates the state-of-the-art realistic breast phantoms, enabling the modelling of signal propagation and evaluation of imaging algorithms. To mitigate the severe interference of artefacts in UWB breast cancer imaging, a robust and artefact resistant (RAR) algorithm based on neighbourhood pairwise correlation is proposed. RAR is fully investigated and evaluated in a variety of scenarios, and compared with four well-known algorithms. It has been shown to achieve improved tumour detection and robust artefact resistance over its counterparts in most cases, while maintaining high computational efficiency. Simulated tumours in both homogeneous and heterogeneous breast phantoms with mild to moderate densities, combined with an entropy-based artefact removal algorithm, are successfully identified and localised. To further improve the performance of algorithms, diverse and dynamic correlation weighting factors are investigated. Two new algorithms, local coherence exploration (LCE) and dynamic neighbourhood pairwise correlation (DNPC), are presented, which offer improved clutter suppression and image resolution. Moreover, a multiple spatial diversity (MSD) algorithm, which explores and exploits the richness of signals among different transmitter and receiver pairs, is proposed. It is shown to achieve enhanced tumour detection even in severely dense breasts. Finally, two accelerated image reconstruction mechanisms referred to as redundancy elimination (RE) and annulus predication (AP) are proposed. RE removes a huge number of repetitive operations, whereas AP employs a novel annulus prediction to calculate millions of time delays in a highly efficient batch mode. Their efficacy is demonstrated by extensive analysis and simulations. Compared with the non-accelerated method, RE increases the computation speed by two-fold without any performance loss, whereas AP can be 45 times faster with negligible performance degradation

Microwave NDT&E using open-ended waveguide probe for multilayered structures

Ph. D. Thesis.Microwave NDT&E has been proved to be suitable for inspecting of dielectric structures due to low attenuation in dielectric materials and free-space. However, the microwave responses from multilayered structures are complex as an interrogation of scattering electromagnetic waves among the layers and defects. In many practical applications, electromagnetic analysis based on analytic- and forward structural models cannot be generalised since the defect shape and properties are usually unknown and hidden beneath the surface layer. This research proposes the design and implementation of microwave NDT&E system for inspection of multilayered structures. Standard microwave open-ended rectangular waveguides in X, Ku and K bands (frequency range between 8-26.5 GHz) and vector network analyser (VNA) generating sweep frequency of wideband monochromatic waves have been used to obtain reflection coefficient responses over three types of challenging multilayered samples: (1) corrosion progression under coating, (2) woven carbon fibre reinforced polymer (CFRP) with impact damages, and (3) thermal coated glass fibre reinforced polymer (GFRP) pipe with inner flat-bottom holes. The obtained data are analysed by the selected feature extraction method extracting informative features and verify with the sample parameters (defect parameters). In addition, visualisation methods are utilised to improve the presentation of the defects and material structures resulting in a better interpretation for quantitative evaluation. The contributions of this project are summarised as follows: (1) implementation of microwave NDT&E scanning system using open-ended waveguide with the highest resolution of 0.1mm x 0.1 mm, based on the NDT applications for the three aforementioned samples; (2) corrosion stages of steel corrosion under coating have been successfully characterised by the principal component analysis (PCA) method; (3) A frequency selective based PCA feature has been used to visualise the impact damage at different impact energies with elimination of woven texture influences; (4) PCA and SAR (synthetic aperture radar) tomography together with time-offlight extraction, have been used for detection and quantitative evaluation of flat-bottom hole defects (i.e., location, size and depth). The results conclude that the proposed microwave NDT&E system can be used for detection and evaluation of multilayered structures, which its major contributions are follows. (1) The early stages (0-12month) of steel corrosion undercoating has been successfully characterised by mean of spectral responses from microwave opened rectangular waveguide probe and PCA. (2) The detection of low energy impact damages on CFRP as low as 4 Joules has been archived with microwave opened rectangular waveguide probe raster scan together with SAR imaging and PCA for feature extraction methods. (3) The inner flat-bottom holes beneath the thermal coated GFRP up to 11.5 mm depth has been successfully quantitative evaluated by open-ended waveguide raster scan using PCA and 3-D reconstruction based on SAR tomography techniques. The evaluation includes location, sizing and depth. Nevertheless, the major downside of feature quantities extracted from statistically based methods such as PCA, is it intensely relies on the correlation of the input dataset, and thus hardly link them with the physical parameters of the test sample, in particular, the complex composite architectures. Therefore, there are still challenges of feature extraction and quantitative evaluation to accurately determine the essential parameters from the samples. This can be achieved by a future investigation of multiple features fusion and complementary features.Ministry of Science and Technology of Royal Thai Government and Office of Educational Affairs, the Royal Thai Embass

Methods for MRI RF Pulse Design and Image Reconstruction.

This thesis describes methods to improve magnetic resonance imaging (MRI) reconstruction and system calibration, namely, B1 field mapping which is to measure the spatial distribution of the magnetic field produced by radiofrequency (RF) coils. We also developed methods of RF pulse design and steady-state imaging sequence design for applications such as fat suppression and magnetization transfer contrast imaging. There are five projects: (a) We developed a framework of iterative image reconstruction with separate magnitude and phase regularization where compressed sensing is used for the magnitude and special phase regularizers that are compatible with phase wrapping are designed for different applications. The proposed method significantly improves the phase image reconstruction while accelerates the data acquisition. (b) A modified Bloch-Siegert B1 mapping was developed to efficiently acquire both magnitude and phase of the B1 maps of multi-channel RF transmission systems. A regularized method was developed to jointly estimate the B1 magnitude and phase to reduce low signal-to-noise ratio regions. Furthermore, we developed a method for coil combination optimization for this multi-channel B1 mapping sequence based on Cramer-Rao lower bound analysis, to improve the raw data quality for B1 estimation. (c) We developed a four dimensional spectral-spatial fat saturation pulse that uniformly suppresses fat without exciting water in the presence of main magnetic field and B1 field inhomogeneity. At 3T, we showed that the proposed pulse can work more robustly than the standard spectrally selective fat saturation pulse with half the pulse length. (d) We applied the proposed fat saturation pulse to spoiled gradient echo sequence and small-tip fast recovery imaging sequence, with a modified RF spoiling scheme. We tested these proposed sequences on clinical applications like cartilage imaging and MR angiography and demonstrated their ability to simultaneously produce fat suppression and magnetization transfer contrast. We show that the proposed sequences can reduce the minimal repetition time and potentially lower the overall RF power deposition. (e) We designed a small tip fast recovery imaging sequence combined with a post-processing method to separate water from fat and remove banding artifacts simultaneously.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107071/1/zhaofll_1.pd

Annual Review of Progress in Applied Computational Electromagnetics

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