Compressed sensing techniques for radial Ultra-short Echo Time (UTE) magnetic resonance imaging

This thesis proposes two techniques, namely Compressed Sensing (CS) and self-gating, for pre-clinical (CMRI) to reduce scan time and RF exposure to mouse heart, simply experimental procedures, and improve imaging quality. The proposed CS technique reduces the number of radial trajectories in Ultra-short Echo Time (UTE) CMRI scans on a 7 Tesla MRI machine to acquire 13% to 38% of the fully sampled k-space data. To reconstruct the image, the Non-Uniform Fast Fourier Transform (NUFFT) is utilized in each iteration of the l1-norm optimization algorithm of the CS to reduce error and aliasing. Experimental results with a phantom and a mouse heart samples show that the image quality of the proposed NUFFT-CS reconstructions, measured by the Peak Signal to Noise ratio (PSNR) and structural similarity (SSIM), is obviously better than those of traditional zero-filling method and regridding-CS method. Comparing the images of the CS technique with the reconstructions of fully sampled data, the quality degradation is illegible while the scan time is largely reduced. The proposed self-gating technique extracts the cardiac cycle information directly from the UTE CMRI measurements that are acquired without Electrocardiography (ECG) trigger. The proposed detector filters the k0 lines in the no-trigger UTE MRI scans to extract the cardiac cycle features, and automatically detects the peaks of the filtered signal as the cycle start points. The reconstruct cardiac images based on the self-gating signals reflect the cardiac cycle clearly and the scan time in MRI exams is reduced by 40% to 70%. The proposed self-gating detector differs from existing k0-line detector in the filter design and in the combination with NUFFT image reconstruction. Future research in this direction may include thorough analysis of the detector performance and may combine self-gated MRI with CS reconstruction. --Abstract, page iv

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

An Image Denoising Method for SAR Images with Low-Sampling Measurements

In this study, an image denoising method for the synthetic aperture radar (SAR) images is proposed. When reconstructed from low-sampling-rate measurements using a compressed sensing (CS) based method, the reconstructions still suffer from noise and aliasing for the sampling rate is much lower than the Nyquist sampling rate (15%-25%). To in future improve the reconstruction, we proposed an imaging denoising method for CS-based reconstructed SAR image. In this proposed denoising method, the pending SAR image is treated as a level set function. We design a step curvature flow function using which the aliasing and noise are eliminated and the clarity of objects of interest in the SAR images are enhanced. Simulation and experimental results illustrated that only a 20% measurement is necessary in the SAR experiment to identify the objects of interest with the proposed method

Compressed Sensing with Non-Uniform Fast Fourier Transform for Radial Ultra-Short Echo Time (UTE) MRI

This paper proposes a compressive sensing (CS) method for radial Magnetic Resonance Imaging (MRI) using nonuniform fast fourier transform (NUFFT). With the Ultra-short Echo Time (UTE) technology, radial MRI can reduce scan time, reduce artifacts, and achieve high imaging quality. We show that NUFFT-CS reconstruction from under-sampled radial k-space measurements achieves better image quality than the zero-filling (ZF) approaches and the existing CS approach that uses regridding. Experiments using phantom and animal samples have verified that the NUFFT-CS achieves better performance than the NUFFT-ZF, regrid-ZF, and regrid-CS, especially at small sampling rates

Automated Cardiac Self-Gated Radial CMRI

Distortions in electrocardiogram (ECG) signals affect the image quality and increase scan time of Cardiac Magnetic Resonance Imaging (CMRI) exams. This study proposes an alternative method of acquiring CMRI cine images in mouse heart using a self-gated Ultra-short Echo Time (UTE) protocol. In our method, a bandpass filter and a lowpass filter are adopted to extract the self-gated signals from the center of the raw no-gated k-space measurements, A live mouse has been tested as the example to verify the method. The results demonstrated that this method could be used for reconstruction of the no-gated UTE CMRI measurements without using of external ECG signals

Microwave Imaging from Sparse Measurements for Near-Field Synthetic Aperture Radar

This paper reports the experimental studies for four image reconstruction methods from sparse measurement using wideband microwave synthetic aperture radar systems. The four methods include two denoising methods using zero filling (ZF) and nonuniform fast Fourier transform (NUFFT), and two compressed sensing (CS) methods using the orthogonal matching pursuit and the conjugate gradient algorithms. The specimens under test (SUTs) consist of a tray of small rocks with different densities with/without one piece wrapped in an aluminum foil. The raw measurements of the SUTs are randomly undersampled in the spatial domain, and the images are reconstructed from the measurements of 10%-60% sparse-sampling rates. The results show that the CS method achieves good image quality with as low as 30% sparse-sampling rate, while ZF and NUFFT require 50% to obtain acceptable quality. An enhanced Otsu\u27s method is also proposed to detect the foiled rock from sparse reconstructions, which improves detection performance for the sparse-sampling rate of 5%-15%. The reduction of spatial measurement leads to reduced cost or reduced measurement time

Multifrequency Compressed Sensing for 2-D Near-Field Synthetic Aperture Radar Image Reconstruction

This paper investigates a new multifrequency compressed sensing (CS) model for 2-D near-field microwave and millimeter-wave synthetic aperture radar (SAR) imaging system, which usually collects multifrequency sparse data. Spatial data of each frequency are represented as a hierarchical tree structure under a wavelet basis and spatial data of different frequencies are modeled as a joint structure, because they are highly correlated. Based on the developed multifrequency CS model, a new CS approach is proposed by exploiting both the intrafrequency and interfrequency correlations, and enriches the existing CS approaches for 2-D near-field microwave and millimeter-wave SAR image reconstruction from undersampled measurements. Combining a splitting Bregman update with a variation of the parallel Fast Iterative Shrinkage-Thresholding Algorithm-like proximal algorithm, the proposed CS approach minimizes a linear combination of five terms: a least squares data fitting, a multi-ℓ1 norm, a multitotal variation norm, a joint-sparsity ℓ21 norm, and a tree-sparsity overlapping ℓ21 norm. Simulation and experimental results demonstrate the superior performance of the proposed approach in terms of both efficiency and convergence speed

Microwave Synthetic Aperture Radar Imaging using Sparse Measurement

This paper evaluates Compressed Sensing (CS) techniques for image recovery from sparse measurement of wideband microwave synthetic aperture radar. A specimen under test (SUT) consists of a tray of small rocks of different densities and with/without one piece that is wrapped in aluminum foil. The fully-sampled measurements of the SUT are randomly under-sampled in the space domain and the images are reconstructed from measurements of 10%-50% sparse-sampling rates using conventional zero-filling (ZF) and NUFFT methods in comparison to the advanced CS method. The results show that the CS method achieves good image quality with as low as 30% sparse-sampling rate, while ZF and NUFFT require 50% to obtain acceptable quality. The reduction of spatial measurement leads to reduced cost or reduced measurement time