Shear wave elastography methods are able to accurately measure tissue stiffness, allowing these techniques to monitor the progression of hepatic fibrosis. While many methods rely on acoustic radiation force to generate shear waves for 2-D imaging, probe oscillation shear wave elastography (PROSE) provides an alternative approach by generating shear waves through continuous vibration of the ultrasound probe while simultaneously detecting the resulting motion. The generated shear wave field in in vivo liver is complicated, and the amplitude and quality of these shear waves can be influenced by the placement of the vibrating probe. To address these challenges, a real-time shear wave visualization tool was implemented to provide instantaneous visual feedback to optimize probe placement. Even with the real-time display, it was not possible to fully suppress residual motion with established filtering methods. To solve this problem, the shear wave signal in each frame was decoupled from motion and other sources through the use of a parameter-free empirical mode decomposition before calculating shear wave speeds. This method was evaluated in a phantom as well as in in vivo livers from five volunteers. PROSE results in the phantom as well as in vivo liver correlated well with independent measurements using the commercial General Electric Logiq E9 scanner.

Robust clutter filtering is essential for ultrasound small vessel imaging. Eigen-based clutter filtering techniques have recently shown great improvement in clutter rejection over conventional clutter filters in small animals. However, for in vivo human imaging, eigen-based clutter filtering can be challenging due to the complex spatially-varying tissue and noise characteristics. To address this challenge, we present a novel block-wise adaptive singular value decomposition (SVD) based clutter filtering technique. The proposed method divides the global plane wave data into overlapped local spatial segments, within which tissue signals are assumed to be locally coherent and noise locally stationary. This, in turn, enables effective separation of tissue, blood and noise via SVD. For each block, the proposed method adaptively determines the singular value cutoff thresholds based on local data statistics. Processing results from each block are redundantly combined to improve both the signal-to-noise-ratio (SNR) and the contrast-to-noise-ratio (CNR) of the small vessel perfusion image. Experimental results show that the proposed method achieved more than two-fold increase in SNR and more than three-fold increase in CNR in dB scale over the conventional global SVD filtering technique for an in vivo human native kidney study. The proposed method also showed substantial improvement in suppression of the depth-dependent background noise and better rejection of near field tissue clutter. The effects of different processing block size and block overlap percentage were systematically investigated as well as the tradeoff between imaging quality and computational cost.

Ultrasound shear wave elastography (SWE) utilizes the propagation of induced shear waves to characterize the shear modulus of soft tissue. Many methods rely on an acoustic radiation force (ARF) "push beam" to generate shear waves. However, specialized hardware is required to generate the push beams, and the thermal stress that is placed upon the ultrasound system, transducer, and tissue by the push beams currently limits the frame-rate to about 1 Hz. These constraints have limited the implementation of ARF to high-end clinical systems. This paper presents Probe Oscillation Shear Elastography (PROSE) as an alternative method to measure tissue elasticity. PROSE generates shear waves using a harmonic mechanical vibration of an ultrasound transducer, while simultaneously detecting motion with the same transducer under pulse-echo mode. Motion of the transducer during detection produces a "strain-like" compression artifact that is coupled with the observed shear waves. A novel symmetric sampling scheme is proposed such that pulse-echo detection events are acquired when the ultrasound transducer returns to the same physical position, allowing the shear waves to be decoupled from the compression artifact. Full field-of-view (FOV) two-dimensional (2D) shear wave speed images were obtained by applying a local frequency estimation (LFE) technique, capable of generating a 2D map from a single frame of shear wave motion. The shear wave imaging frame rate of PROSE is comparable to the vibration frequency, which can be an order of magnitude higher than ARF based techniques. PROSE was able to produce smooth and accurate shear wave images from three homogeneous phantoms with different moduli, with an effective frame rate of 300 Hz. An inclusion phantom study showed that increased vibration frequencies improved the accuracy of inclusion imaging, and allowed targets as small as 6.5 mm to be resolved with good contrast (contrast-to-noise ratio ≥ 19 dB) between the target and background.

Shear wave speed can be used to assess tissue elasticity, which is associated with tissue health. Ultrasound shear wave elastography techniques based on measuring the propagation speed of the shear waves induced by acoustic radiation force are becoming promising alternatives to biopsy in liver fibrosis staging. However, shear waves generated by such methods are typically very weak. Therefore, the penetration may become problematic, especially for overweight or obese patients. In this study, we developed a new method called external vibration multi-directional ultrasound shearwave elastography (EVMUSE), in which external vibration from a loudspeaker was used to generate a multi-directional shear wave field. A directional filter was then applied to separate the complex shear wave field into several shear wave fields propagating in different directions. A 2-D shear wave speed map was reconstructed from each individual shear wave field, and a final 2-D shear wave speed map was constructed by compounding these individual wave speed maps. The method was validated using two homogeneous phantoms and one multi-purpose tissue-mimicking phantom. Ten patients undergoing liver magnetic resonance elastography (MRE) were also studied with EVMUSE to compare results between the two methods. Phantom results showed EVMUSE was able to quantify tissue elasticity accurately with good penetration. In vivo EVMUSE results were well correlated with MRE results, indicating the promise of using EVMUSE for liver fibrosis staging.

Comb-push ultrasound shear elastography (CUSE) has recently been shown to be a fast and accurate 2-D elasticity imaging technique that can provide a full field-of-view (FOV) shear wave speed map with only one rapid data acquisition. The initial version of CUSE was termed U-CUSE because unfocused ultrasound push beams were used. In this paper, we present two new versions of CUSE-focused CUSE (F-CUSE) and marching CUSE (M-CUSE), which use focused ultrasound push beams to improve acoustic radiation force penetration and produce stronger shear waves in deep tissues (e.g., kidney and liver). F-CUSE divides transducer elements into several subgroups which transmit multiple focused ultrasound beams simultaneously. M-CUSE uses more elements for each focused push beam and laterally marches the push beams. Both F-CUSE and M-CUSE can generate comb-shaped shear wave fields that have shear wave motion at each imaging pixel location so that a full FOV 2-D shear wave speed map can be reconstructed with only one data acquisition. Homogeneous phantom experiments showed that U-CUSE, F-CUSE, and M-CUSE can all produce smooth shear wave speed maps with accurate shear wave speed estimates. An inclusion phantom experiment showed that all CUSE methods could provide good contrast between the inclusion and background with sharp boundaries while F-CUSE and M-CUSE require shorter push durations to achieve shear wave speed maps with comparable SNR to U-CUSE. A more challenging inclusion phantom experiment with a very stiff and deep inclusion shows that better shear wave penetration could be gained by using F-CUSE and M-CUSE. Finally, a shallow inclusion experiment showed that good preservations of inclusion shapes could be achieved by both U-CUSE and F-CUSE in the near field. Safety measurements showed that all safety parameters are below FDA regulatory limits for all CUSE methods. These promising results suggest that, using various push beams, CUSE is capable of reconstructing a 2-D full FOV shear elasticity map using only one push-detection data acquisition in a wide range of depths for soft tissue elasticity imaging.

Ultrasound tissue harmonic imaging is widely used to improve ultrasound B-mode imaging quality thanks to its effectiveness in suppressing imaging artifacts associated with ultrasound reverberation, phase aberration, and clutter noise. In ultrasound shear wave elastography (SWE), because the shear wave motion signal is extracted from the ultrasound signal, these noise sources can significantly deteriorate the shear wave motion tracking process and consequently result in noisy and biased shear wave motion detection. This situation is exacerbated in in vivo SWE applications such as heart, liver, and kidney. This paper, therefore, investigated the possibility of implementing harmonic imaging, specifically pulse-inversion harmonic imaging, in shear wave tracking, with the hypothesis that harmonic imaging can improve shear wave motion detection based on the same principles that apply to general harmonic B-mode imaging. We first designed an experiment with a gelatin phantom covered by an excised piece of pork belly and show that harmonic imaging can significantly improve shear wave motion detection by producing less underestimated shear wave motion and more consistent shear wave speed measurements than fundamental imaging. Then, a transthoracic heart experiment on a freshly sacrificed pig showed that harmonic imaging could robustly track the shear wave motion and give consistent shear wave speed measurements of the left ventricular myocardium while fundamental imaging could not. Finally, an in vivo transthoracic study of seven healthy volunteers showed that the proposed harmonic imaging tracking sequence could provide consistent estimates of the left ventricular myocardium stiffness in end-diastole with a general success rate of 80% and a success rate of 93.3% when excluding the subject with Body Mass Index higher than 25. These promising results indicate that pulse-inversion harmonic imaging can significantly improve shear wave motion tracking and thus potentially facilitate more robust assessment of tissue elasticity by SWE.

The Non-Uniform Fast Fourier Transform (NUFFT) is a generalization of FFT to non-equidistant samples. It has many applications which vary from medical imaging to radio astronomy to the numerical solution of partial differential equations. Despite recent advances in speeding up NUFFT on various platforms, its practical applications are still limited, due to its high computational cost, which is significantly dominated by the convolution of a signal between a non-uniform and uniform grids. The computational cost of the NUFFT is particularly detrimental in cases which require fast reconstruction times, such as iterative 3D non-Cartesian MRI reconstruction. We propose novel and highly scalable parallel algorithm for performing NUFFT on x86-based multi-core CPUs. The high performance of our algorithm relies on good SIMD utilization and high parallel efficiency. On convolution, we demonstrate on average 90% SIMD efficiency using SSE, as well up to linear scalability using a quad-socket 40-core Intel(R) Xeon(R) E7-4870 Processors based system. As a result, on dual socket Intel(R) Xeon(R) X5670 based server, our NUFFT implementation is more than 4x faster compared to the best available NUFFT3D implementation, when run on the same hardware. On Intel(R) Xeon(R) E5-2670 processor based server, our NUFFT implementation is 1.5X faster than any published NUFFT implementation today. Such speed improvement opens new usages for NUFFT. For example, iterative multi channel reconstruction of a 240×240×240 image could execute in just over 3 minutes, which is on the same order as contemporary non-iterative (and thus less-accurate) 3D NUFFT-based MRI reconstructions.

Fast and accurate tissue elasticity imaging is essential in studying dynamic tissue mechanical properties. Various ultrasound shear elasticity imaging techniques have been developed in the last two decades. However, to reconstruct a full field-of-view 2-D shear elasticity map, multiple data acquisitions are typically required. In this paper, a novel shear elasticity imaging technique, comb-push ultrasound shear elastography (CUSE), is introduced in which only one rapid data acquisition (less than 35 ms) is needed to reconstruct a full field-of-view 2-D shear wave speed map (40 × 38 mm). Multiple unfocused ultrasound beams arranged in a comb pattern (comb-push) are used to generate shear waves. A directional filter is then applied upon the shear wave field to extract the left-to-right (LR) and right-to-left (RL) propagating shear waves. Local shear wave speed is recovered using a time-of-flight method based on both LR and RL waves. Finally, a 2-D shear wave speed map is reconstructed by combining the LR and RL speed maps. Smooth and accurate shear wave speed maps are reconstructed using the proposed CUSE method in two calibrated homogeneous phantoms with different moduli. Inclusion phantom experiments demonstrate that CUSE is capable of providing good contrast (contrast-to-noise ratio ≥ 25 dB) between the inclusion and background without artifacts and is insensitive to inclusion positions. Safety measurements demonstrate that all regulated parameters of the ultrasound output level used in CUSE sequence are well below the FDA limits for diagnostic ultrasound.

Magnetic resonance elastography (MRE) is an imaging modality capable of visualizing the elastic properties of an object using magnetic resonance imaging (MRI) measurements of transverse acoustic strain waves induced in the object by a harmonically oscillating mechanical vibration. Various algorithms have been designed to determine the mechanical properties of the object under the assumptions of linear elasticity, isotropic and local homogeneity. One of the challenging problems in MRE is to reduce the noise effects and to maintain contrast in the reconstructed shear modulus images. In this paper, we propose a new algorithm designed to reduce the degree of noise amplification in the reconstructed shear modulus images without the assumption of local homogeneity. Investigating the relation between the measured displacement data and the stress wave vector, the proposed algorithm uses an iterative reconstruction formula based on a decomposition of the stress wave vector. Numerical simulation experiments and real experiments with agarose gel phantoms and human liver data demonstrate that the proposed algorithm is more robust to noise compared to standard inversion algorithms and stably determines the shear modulus.

In clinical magnetic resonance imaging (MRI), any reduction in scan time offers a number of potential benefits ranging from high-temporal-rate observation of physiological processes to improvements in patient comfort. Following recent developments in compressive sensing (CS) theory, several authors have demonstrated that certain classes of MR images which possess sparse representations in some transform domain can be accurately reconstructed from very highly undersampled K-space data by solving a convex l(1) -minimization problem. Although l(1)-based techniques are extremely powerful, they inherently require a degree of over-sampling above the theoretical minimum sampling rate to guarantee that exact reconstruction can be achieved. In this paper, we propose a generalization of the CS paradigm based on homotopic approximation of the l(0) quasi-norm and show how MR image reconstruction can be pushed even further below the Nyquist limit and significantly closer to the theoretical bound. Following a brief review of standard CS methods and the developed theoretical extensions, several example MRI reconstructions from highly undersampled K-space data are presented.

We introduce a new type of data for classification of regional segments of myocardium. We have analyzed strain measurements taken throughout the cardiac cycle from the echocardiograms of pigs. Classifications by both principal component analysis (PCA) and by neural network (NN) are combined for a data mining operation. Differences in strain waveforms between normal and diseased myocardium may further elucidate the corresponding changes in physiology. Altered functioning of the heart muscle is reflected by strain, and objective computer analysis should aid in the diagnosis of ischemia. We hypothesize that the entire strain waveform over one heart cycle can be classified to functionally determine whether or not a myocardial region is perfused.

The elastic properties of soft tissues are closely related to their structure, biological conditions, and pathology. For years, physicians have used palpation as a crude elasticity measurement tool to diagnose diseases in the human body. Based on this simple concept, but using modern technology, several elasticity imaging schemes have been developed during the past two decades. In this paper, we present two elasticity imaging methods that use a low-frequency (Hz to kHz range) harmonic force to excite the tissue. The first method, called magnetic resonance elastography (MRE), uses a phase sensitive magnetic resonance technique to detect tissue motion. Excitation is usually performed with a mechanical actuator on the surface of the body, although other excitation methods are possible. In the second method, called vibro-acoustography, the radiation force from focused ultrasound is used for excitation in a limited region within the tissue. Tissue motion is detected by measuring the acoustic field emitted by the object in response to the vibration. The resulting images in both methods can be related to the dynamics of the object at the excitation frequency. The spatial resolution of MRE and vibro-acoustography images is in the millimeter and sub-millimeter ranges, respectively. Here, we present the theory and physical principles of MRE and vibro-acoustography and describe their performances. We also present results of experiments on various human tissues, including breast, brain, and vessels. Finally, we discuss potential clinical application of these two imaging methods.

Grayscale inhomogeneities in magnetic resonance (MR) images confound quantitative analysis of these images. Homomorphic unsharp masking and its variations have been commonly used as a post-processing method to remove inhomogeneities in MR images. However, little data is available in the literature assessing the relative effectiveness of these algorithms to remove inhomogeneities, or describing how these algorithms can affect image data. In this study, we address these questions quantitatively using simulated images with artificially constructed and empirically measured bias fields. Our results show that mean-based filtering is consistently more effective than median-based algorithms for removing inhomogeneities in MR images, and that artifacts are frequently introduced into images at the most commonly used window sizes. Our results demonstrate dramatic improvement in the effectiveness of the algorithms with significantly larger windows than are commonly used.

A newly developed magnetic resonance imaging technique can directly visualize propagating acoustic strain waves in tissue-like materials. By estimating the local wavelength of the acoustic wave pattern, quantitative values of shear modulus can be calculated and images generated that depict tissue elasticity or stiffness. Since tumors are significantly stiffer than normal tissue (the basis of their detection by palpation), this technique may have potential for "palpation by imaging", with possible application to the detection of tumors in breast, liver, kidney, and prostate. We describe the local wavelength estimation algorithm, study its properties, and show a variety of sample results.

Two visualization techniques based on nonlinear projections for fusing multispectral image data sets into a single most informative gray scale image are described. These techniques are fast, theoretically attractive, complement linear techniques, and may be of value in highlighting specific regions where the component images should be examined in detail.

Virtual reality offers the promise of highly interactive, natural control of the visualization process, greatly enhancing the scientific value of the data produced by medical imaging systems. Due to the computational and real time update requirements of virtual reality interfaces, however, the complexity of polygonal surfaces which can be displayed is limited. In this paper, we present a novel method for the production of a polygonal surface containing a pre-specified number of polygons from volumetric data. To preserve surface detail, we extract a set of curvature weights from the volumetric data and use these weights as the input to a 2-D Kohonen network. The adaptation of the network to the input vectors results in a display surface that preserves useful detail relative to the number of polygons used.

The effective display of multi-parameter medical image data sets is assuming increasing importance as more distinct imaging modalities are becoming available. For medical purposes, one desirable goal is to fuse such data sets into a single most informative gray-scale image without making rigid classification decisions. A visualization technique based on a non-linear projection onto a 1-D self-organizing map is described and examples are shown. The SOM visualization technique is fast, theoretically attractive, and has properties which complement those of projection-pursuit or other linear techniques. It may be of particular value in calling attention to specific regions in a multi-parameter image where the component images should be examined in detail.>