Citations

Compression elastography allows the precise measurement of large deformations of soft tissue in vivo. From a measured large deformation, an inverse problem for both the linear and nonlinear elastic moduli distributions can be solved. As part of a larger clinical study to evaluate NEMs in breast cancer, we evaluate the repeatability of linear and nonlinear modulus maps from repeat measurements. Within the cohort of 31 subjects scanned to date, several had repeated scans. These repeated scans were processed to evaluate NEM repeatability. In vivo data were acquired by a custom, digitally controlled, uniaxial compression device with force feedback. RF-data were acquired using plane wave imaging, at a frame-rate of 200 Hz, with a ramp-and-hold compressive force of 8N, applied at 8 N/s. A two-dimensional (2D) block-matching algorithm was used to obtain sample-level displacement fields which were then tracked at subsample resolution using 2D cross correlation. Linear and nonlinear elasticity parameters in the Blatz model of tissue elasticity are estimated using iterative optimization. Repeatability between both modes and elastic modulus maps is measured and compared. Preliminary results indicate that when images are acquired in the same region of tissue, the modulus maps are consistent. [Work supported by NIH R01CA195527.]

We present a full-wave inversion approach with total variation regularization for elastography. The proposed method is based on the minimization of an error in constitutive equations functional augmented with a least squares data misfit term referred to as MECE for “modified error in constitutive equations.” The main theme of this paper is to demonstrate several key strengths of the proposed method on experimental data. In addition, some illustrative examples are provided where the proposed method is compared with a common shear wave elastography (SWE) approach. To this end, ultrasonically tracked displacement data from an acoustic radiation force (ARF) pulse are used in different phantoms including phantom with layered inclusion and triangle inclusion. The results indicate that the MECE approach can produce accurate shear modulus reconstructions in comparison with SWE, especially around the sharp edges in the layered and triangle inclusions. We compare shear modulus reconstruction using MECE and SWE with original inclusion shapes using two-dimensional normalized zero mean cross correlation, edge preservation index and dice coefficient similarity index. [Work supported by NIH Grant R01 CA174723.]

Sparse array (SA) is an approach to reduce the number of system channels. However, SA suffers from grating lobe (GL) artefacts due to the sparsity of array aperture resulting in degradation of the ultrasound image quality. Based on a given or known data sets of radio frequency (RF) echo acquired from active elements of an array, RF echo data in unknown and/or inactive elements of array can be created virtually and used to suppress the GL artefact in SA. This letter presents gap-filling (GF) approaches to generate channel data, which are not physically acquired. It is demonstrated that the proposed GF technique can reduce the artefacts of SA by filling the gaps in the array aperture. Simulation results show that the GF technique can suppress the GL level of SA by up to 16 dB. Also, the GF technique can improve the image quality of fully sampled arrays with small number of active elements.

Vibrational characteristics of bone are directly dependent on its physical properties. In this study, a vibrational method for bone evaluation is introduced. We propose a new type of quantitative vibro-acoustic method based on the acoustic radiation force of ultrasound for bone characterization in persons with fracture. Using this method, we excited the clavicle or ulna by an ultrasound radiation force pulse which induces vibrations in the bone, resulting in an acoustic wave that is measured by a hydrophone placed on the skin. The acoustic signals were used for wave velocity estimation based on a cross-correlation technique. To further separate different vibration characteristics, we adopted a variational mode decomposition technique to decompose the received signal into an ensemble of band-limited intrinsic mode functions, allowing analysis of the acoustic signals by their constitutive components. This prospective study included 15 patients: 12 with clavicle fractures and three with ulna fractures. Contralateral intact bones were used as controls. Statistical analysis demonstrated that fractured bones can be differentiated from intact ones with a detection probability of 80%. Additionally, we introduce a “healing factor” to quantify the bone healing progress which successfully tracked the progress of healing in 80% of the clavicle fractures in the study.

Vascular networks can provide invaluable information about tumor angiogenesis. Ultrafast Doppler imaging enables ultrasound to image microvessels by applying tissue clutter filtering methods on the spatio-temporal data obtained from plane-wave imaging. However, the resultant vessel images suffer from background noise that degrades image quality and restricts vessel visibilities. In this paper, we addressed microvessel visualization and the associated noise problem in the power Doppler images with the goal of achieving enhanced vessel-background separation. We proposed a combination of patch-based non-local mean filtering and top-hat morphological filtering to improve vessel outline and background noise suppression. We tested the proposed method on a flow phantom, as well as in vivo breast lesions, thyroid nodules, and pathologic liver from human subjects. The proposed non-local-based framework provided a remarkable gain of more than 15 dB, on average, in terms of contrast-to-noise and signal-to-noise ratios. In addition to improving visualization of microvessels, the proposed method provided high quality images suitable for microvessel morphology quantification that may be used for diagnostic applications.

Acoustic radiation force has been used in many interesting medical and industrial applications in the past two decades. The fact that radiation stress can be confined to a small focal area and exerted remotely inside or on an object allows one to use this phenomenon in a variety of ways to explore properties of the object. Vibro-acoustography is a material characterization and imaging method that uses harmonic or impulsive acoustic radiation force to stimulate the targeted material and receives the resulting acoustic response. The output acoustic signal is then used to extract important information about the object. Biomedical applications of vibro-acoustography and its closely related methods include imaging, flow detection, and tissue characterization. Other applications include non-destructive evaluation/testing of materials, modal analysis of small structures, and microscopy. This talk presents an overview of vibro-acoustography and its closely related techniques over the past 20 years. Various biomedical and industrial applications will be presented. The talk will conclude with a discussion on potential future applications of this technology.

Minimally invasive procedures using ultrasound are becoming prominent, especially in applications like biopsy, catheter guidance, and surgeries. In our previous work a catheter tip is fitted with a miniature omnidirectional piezoelectric crystal; the ultrasonic field from the imaging array and the piezoelectric crystal interact resulting in a symmetric Doppler shift. Previously, a frequency domain algorithm was used to identify pixels having the symmetric Doppler shift. The identified pixels can then be visualized on B-mode and color map with a unique color thus representing the location of the catheter tip. However, frequency domain algorithms are computationally expensive and require changes to data workflow. Using mean frequency estimators with symmetric Doppler shift results in the catheter tip being displayed as zero mean noise making it hard to distinguish from color Doppler noise. To overcome this limitation, complex demodulation can be used. Modulating the time domain signal with sine and cosine by the piezoelectric excitation frequency and summing them up results in cancellation of one of the symmetric Doppler components. With only one frequency component remaining mean frequency estimators can be used to identify the location of the catheter. The presented algorithm has lower computational complexity and requires minimal change in software design.

While conventional filtering techniques can effectively exclude tissue-related Doppler shifts from those resulting from blood motions in large vessels (flow rates ~ cm/s), significant spectral overlap can hinder proper clutter filtering in imaging slow flows (flow rates ~ mm/s or tenths of mm/s). Recently, several groups have proposed clutter filtering via processing of echo ensembles in the rank space instead of frequency domain. Though significant Doppler content can result from fast moving tissues, global coherence of the induced variations enables a low rank representation. In this talk, we first provide a brief overview of the low rank clutter removal methods. We then present a new framework for the analysis of Doppler ensembles using multi-linear decomposition of a tensor comprising Doppler ensembles at different rates. The model results in a high dimensional tensor from which, after proper rank selection, images of flow at different speeds can be extracted along different fibers. Using simulation models, we demonstrate the superior performance of the proposed method over singular value clutter filtering in imaging small vessels embedded in moving tissue undergoing both translational and shearing motions. We also present in vivo examples where the proposed method provides significant blood to clutter + noise ratio enhancement compared to singular value clutter filtering.

We hypothesize that pattern of microvasculature in the thyroid nodules can be a biomarker of thyroid cancer. In this study, morphological features of microvasculature networks are assessed as potential biomarkers for differentiation of benign, suspicious and malignant thyroid nodules. This prospective study included n = 81 patients, with a total of 61 benign, 12 suspicious and 8 malignant nodules verfied by pathology and cytology findings. Thyroid nodules were first identified using conventional B-mode ultrasound. A sequence of raw ultrasound data were acquired from each nodule site in two perpendicular planes at ~600 frames/sec. Power Doppler image of the microvasculature was formed using singular value decomposition clutter removal filtering. Gross images were further processed using a combination of morphology and Hessian-based vessel enhancement filtering. Background-free images of the microvasculature networks were segmented (vessels segment length >385μm and diameter >90μm) for morphological analysis. Using a Bayesian framework, measures of vessels density, diameter and tortuosity were tested for significant differences in the three groups. Among all measures, the probability of vessel diameter >600μm in benign cases was significantly higher than that in malignant cases with the p-value <0.01 using Wilcoxon-Rank-Sum test, suggesting reliable cancer detection performance of the proposed method. [Acknowledgment: NIH Grant EB17213.]