Clutter Suppression in Ultrasound: Performance Evaluation of Low-Rank and Sparse Matrix Decomposition Methods

Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound Color Flow Imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue is one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is an essential step for many applications of ultrasound CFI. In this thesis, we focus on a state-of-art algorithm framework called Decomposition into Low-rank and Sparse Matrices (DLSM) framework for ultrasound clutter suppression. Currently, ultrasound clutter suppression is often performed by Singular Value Decomposition (SVD) of the data matrix, which is a branch of eigen-based filtering. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the data set. A potential solution to these issues is the use of DLSM framework. SVD, as a means for singular values, is also one of the widely used algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and lower computing time due to the expensive computational cost of full SVD. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and the moving blood component is sparse. In this thesis, we exploit the feasibility of exploiting low-rank and sparse decomposition schemes, originally developed in the field of computer vision, in ultrasound clutter suppression. Since ultrasound images have different texture and statistical properties compared to images in computer vision, it is of high importance to evaluate how these methods translate to ultrasound CFI. We conduct this evaluation study by adapting 106 DLSM algorithms and validating them against simulation, phantom and in vivo rat data sets. The advantage of simulation and phantom experiments is that the ground truth vessel map is known, and the advantage of the in vivo data set is that it enables us to test algorithms in a realistic setting. Two conventional quality metrics, Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating the clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression

Eigen-based clutter filter design for ultrasound color flow imaging: A review

Proper suppression of tissue clutter is a prerequisite for visualizing flow accurately in ultrasound color flow imaging. Among various clutter suppression methods, the eigen- based filter has shown potential because it can theoretically adapt its stopband to the actual clutter characteristics even when tissue motion is present. This paper presents a formative review on how eigen-based filters should be designed to improve their practical efficacy in adaptively suppressing clutter without affecting the blood flow echoes. Our review is centered around a comparative assessment of two eigen-filter design considerations: 1) eigen-component estimation approach (single-ensemble vs. multi-ensemble formulations), and 2) filter order selection mechanism (eigenvalue-based vs. frequencybased algorithms). To evaluate the practical efficacy of existing eigen-filter designs, we analyzed their clutter suppression level in two in vivo scenarios with substantial tissue motion (intra-operative coronary imaging and thyroid imaging). Our analysis shows that, as compared with polynomial regression filters (with or without instantaneous clutter downmixing), eigen-filters that use a frequency-based algorithm for filter order selection generally give Doppler power images with better contrast between blood and tissue regions. Results also suggest that both multi-ensemble and single-ensemble eigen-estimation approaches have their own advantages and weaknesses in different imaging scenarios. It may be beneficial to develop an algorithmic way of defining the eigen-filter formulation so that its performance advantages can be better realized. © 2010 IEEE.published_or_final_versio

Robust Eigen-Filter Design for Ultrasound Flow Imaging Using a Multivariate Clustering

Blood flow visualization is a challenging task in the presence of tissue motion. Unsuppressed tissue clutter produces flashing artefacts in ultrasound flow imaging which hampers blood flow detection by dominating part of the blood flow signal in certain challenging clinical imaging applications, ranging from cardiac imaging (maximal tissue vibrations) to microvascular flow imaging (very low blood flow speeds). Conventional clutter filtering techniques perform poorly since blood and tissue clutter echoes share similar spectral characteristics. Eigen-based filtering was recently introduced and has shown good clutter rejection performance; however, flow detection performance in eigen filtering suffers if tissue and flow signal subspaces overlap after eigen components are projected to a single signal feature space for clutter rank selection. To address this issue, a novel multivariate clustering based singular value decomposition (SVD) filter design is developed. The proposed multivariate clustering based filter robustly detects and removes non-blood eigen components by leveraging on three key spatiotemporal statistics: singular value magnitude, spatial correlation and the mean Doppler frequency of singular vectors. A better clutter suppression framework is necessary for high-frame-rate (HFR) ultrasound imaging since it is more susceptible to tissue motion due to poorer spatial resolution (tissue clutter bleeds into flow pixels easily). Hence, to test the clutter rejection performance of the proposed filter, HFR plane wave data was acquired from an in vitro flow phantom testbed and in vivo from a subject’s common carotid artery and jugular vein region induced with extrinsic tissue motion (voluntary probe motion). The proposed method was able to adaptively detect and preserve blood eigen components and enabled fully automatic identification of eigen components corresponding to tissue clutter, blood and noise that removes dependency on the operator for optimal rank selection. The flow detection efficacy of the proposed multivariate clustering based SVD filter was statistically evaluated and compared with current clutter rank estimation methods using the receiver operating characteristic (ROC) analysis. Results for both in vitro and in vivo experiments showed that the multivariate clustering based SVD filter yielded the highest area under the ROC curve at both peak systole (0.98 for in vitro; 0.95 for in vivo) and end diastole (0.96 for in vitro; 0.93 for in vivo) in comparison with other clutter rank estimation methods, signifying its improved flow detection capability. The impact of this work is on the automated as well as adaptive (in contrast to a fixed cut-off) selection of eigen components which can potentially allow to overcome the flow detection challenges associated with fast tissue motion in cardiovascular imaging and slow flow in microvascular imaging which is critical for cancer diagnoses

Measuring blood flow and pro-inflammatory changes in the rabbit aorta

Atherosclerosis is a chronic inflammatory disease that develops as a consequence of progressive entrapment of low density lipoprotein, fibrous proteins and inflammatory cells in the arterial intima. Once triggered, a myriad of inflammatory and atherogenic factors mediate disease progression. However, the role of pro-inflammatory activity in the initiation of atherogenesis and its relation to altered mechanical stresses acting on the arterial wall is unclear. Estimation of wall shear stress (WSS) and the inflammatory mediator NF-κB is consequently useful. In this thesis novel ultrasound tools for accurate measurement of spatiotemporally varying 2D and 3D blood flow, with and without the use of contrast agents, have been developed. This allowed for the first time accurate, broad-view quantification of WSS around branches of the rabbit abdominal aorta. A thorough review of the evidence for a relationship between flow, NF-κB and disease was performed which highlighted discrepancies in the current literature and was used to guide the study design. Subsequently, methods for the measurement and colocalization of the spatial distribution of NF-κB, arterial permeability and nuclear morphology in the aorta of New Zealand White rabbits were developed. It was demonstrated that endothelial pro-inflammatory changes are spatially correlated with patterns of WSS, nuclear morphology and arterial permeability in vivo in the rabbit descending and abdominal aorta. The data are consistent with a causal chain between WSS, macromolecule uptake, inflammation and disease, and with the hypothesis that lipids are deposited first, through flow-mediated naturally occurring transmigration that, in excessive amounts, leads to subsequent inflammation and disease.Open Acces

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Robust inversion and detection techniques for improved imaging performance

Thesis (Ph.D.)--Boston UniversityIn this thesis we aim to improve the performance of information extraction from imaging systems through three thrusts. First, we develop improved image formation methods for physics-based, complex-valued sensing problems. We propose a regularized inversion method that incorporates prior information about the underlying field into the inversion framework for ultrasound imaging. We use experimental ultrasound data to compute inversion results with the proposed formulation and compare it with conventional inversion techniques to show the robustness of the proposed technique to loss of data. Second, we propose methods that combine inversion and detection in a unified framework to improve imaging performance. This framework is applicable for cases where the underlying field is label-based such that each pixel of the underlying field can only assume values from a discrete, limited set. We consider this unified framework in the context of combinatorial optimization and propose graph-cut based methods that would result in label-based images, thereby eliminating the need for a separate detection step. Finally, we propose a robust method of object detection from microscopic nanoparticle images. In particular, we focus on a portable, low cost interferometric imaging platform and propose robust detection algorithms using tools from computer vision. We model the electromagnetic image formation process and use this model to create an enhanced detection technique. The effectiveness of the proposed technique is demonstrated using manually labeled ground-truth data. In addition, we extend these tools to develop a detection based autofocusing algorithm tailored for the high numerical aperture interferometric microscope

Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1-MechSOUL (L1-norm-based MechSOUL), which optimize L2- and L1-norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1-MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1-MechSOUL at http://code.sonography.ai.Comment: Link to the Supplemental Video: https://drive.google.com/file/d/1uOmt-T4i9MwR98jUoMsu-eOhQ2mgjrBd/view?usp=sharin

Ultrafast Ultrasound Imaging

Among medical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), ultrasound imaging stands out due to its temporal resolution. Owing to the nature of medical ultrasound imaging, it has been used for not only observation of the morphology of living organs but also functional imaging, such as blood flow imaging and evaluation of the cardiac function. Ultrafast ultrasound imaging, which has recently become widely available, significantly increases the opportunities for medical functional imaging. Ultrafast ultrasound imaging typically enables imaging frame-rates of up to ten thousand frames per second (fps). Due to the extremely high temporal resolution, this enables visualization of rapid dynamic responses of biological tissues, which cannot be observed and analyzed by conventional ultrasound imaging. This Special Issue includes various studies of improvements to the performance of ultrafast ultrasoun