Three-Dimensional Shear Wave Elastography Using Acoustic Radiation Force and a 2-D Row-Column Addressing (RCA) Array

Analysis wave speed of optic nerve and optic nerve head for assessing normal tension glaucoma by using vibro-elastography technique

BACKGROUND

This research aims to evaluate wave speed and viscoelasticity of ocular tissues including the optic nerve and optic nerve head of human eyes between normal tension glaucoma patients and healthy controls by using vibro-elastography techniques.

METHODS

Participants included 12 patients and 12 controls. Wave speed was measured at the optic nerve and optic nerve head in each subject and viscoelasticity was estimated by using Voigt model. Wave speed and viscoelasticity of the optic nerve and optic nerve head were compared between patients and controls by linear mixed models via a restricted maximum likelihood method. The correlation between intraocular pressure and wave speed, elasticity, and viscosity of patients was performed using the Pearson correlation coefficient.

FINDINGS

Significant differences in wave speed (p < 0.0005), elasticity (p = 0.0001) and viscosity p < 0.0001) between patients and controls at the optic nerve head. There was a moderate negative correlation (r = -0.50, p < 0.05) between wave speed and elasticity and intraocular pressure at the optic nerve of patients but no correlation (p > 0.05) between wave speed, elasticity, and viscosity and intraocular pressure at the optic nerve head of patients. No significant difference and correlation among wave speed, elasticity, and viscosity vs. intraocular pressure of the control group at the optic nerve and optic nerve head.

INTERPRETATION

Ultrasound vibro-elastography is useful for noninvasive measurement of viscoelasticity of ocular structures. The glaucoma patient is associated with biomechanical property changes in the optic nerve and optic nerve head, suggesting another way to assess glaucoma focusing on the optic nerve and optic nerve head.

Four-Dimensional (4D) Ultrasound Shear Wave Elastography Using Sequential Excitation

OBJECTIVE

Current shear wave elastography methods primarily focus on 2D imaging. To explore mechanical properties of biological tissues in 3D, a four-dimensional (4D, x, y, z, t) ultrasound shear wave elastography is required. However, 4D ultrasound shear wave elastography is still challenging due to the limitation of the hardware of standard ultrasound acquisition systems. In this study, we introduce a novel method to achieve 4D shear wave elastography, named sequential-based excitation shear wave elastography (SE-SWE). This method can achieve 4D elastography implemented by a 1024-element 2D array with a standard ultrasound 256-channel system.

METHODS

The SE-SWE method employs sequential excitation to generate shear waves, and utilizes a 2D array, dividing it into four sub-sections, to capture shear waves across multiple planes. This process involves sequentially exciting each sub-section to capture shear waves, followed by compounding the acquired data from these subsections.

RESULTS

The phantom studies showed strong concordance between the shear wave speeds (SWS) measured by SE-SWE and expected values, confirming the accuracy of this method and potential to differentiate tissues by stiffness. In ex vivo chicken breast experiments, SE-SWE effectively distinguished between orientations relative to muscle fibers, highlighting its ability to capture the anisotropic properties of tissues.

CONCLUSION

The SE-SWE method advances shear wave elastography significantly by using a 2D array divided into four subsections and sequential excitation, achieving high-resolution volumetric imaging at 1.6mm resolution.

SIGNIFICANCE

The SE-SWE method offers a straightforward and effective approach for 3D shear volume imaging of tissue biological properties.

Enhancing Row-Column Array (RCA)-Based 3D Ultrasound Vascular Imaging With Spatial-Temporal Similarity Weighting

Ultrasound vascular imaging (UVI) is a valuable tool for monitoring the physiological states and evaluating the pathological diseases. Advancing from conventional two-dimensional (2D) to three-dimensional (3D) UVI would enhance the vasculature visualization, thereby improving its reliability. Row-column array (RCA) has emerged as a promising approach for cost-effective ultrafast 3D imaging with a low channel count. However, ultrafast RCA imaging is often hampered by high-level sidelobe artifacts and low signal-to-noise ratio (SNR), which makes RCA-based UVI challenging. In this study, we propose a spatial-temporal similarity weighting (St-SW) method to overcome these challenges by exploiting the incoherence of sidelobe artifacts and noise between datasets acquired using orthogonal transmissions. Simulation, in vitro blood flow phantom, and in vivo experiments were conducted to compare the proposed method with existing orthogonal plane wave imaging (OPW), row-column-specific frame-multiply-and-sum beamforming (RC-FMAS), and XDoppler techniques. Qualitative and quantitative results demonstrate the superior performance of the proposed method. In simulations, the proposed method reduced the sidelobe level by 31.3 dB, 20.8 dB, and 14.0 dB, compared to OPW, XDoppler, and RC-FMAS, respectively. In the blood flow phantom experiment, the proposed method significantly improved the contrast-to-noise ratio (CNR) of the tube by 26.8 dB, 25.5 dB, and 19.7 dB, compared to OPW, XDoppler, and RC-FMAS methods, respectively. In the human submandibular gland experiment, it not only reconstructed a more complete vasculature but also improved the CNR by more than 15 dB, compared to OPW, XDoppler, and RC-FMAS methods. In summary, the proposed method effectively suppresses the side-lobe artifacts and noise in images collected using an RCA under low SNR conditions, leading to improved visualization of 3D vasculatures.

Multimodal assessment of brain stiffness variation in healthy subjects using magnetic resonance elastography and ultrasound time-harmonic elastography

Magnetic resonance elastography (MRE) is a noninvasive brain stiffness mapping method. Ultrasound-based transtemporal time-harmonic elastography (THE) is emerging as a cost-effective, fast alternative that has potential applications for bedside monitoring of intracranial pressure. We aim to investigate the accuracy of THE in comparison to MRE performed in the brain. Ten healthy volunteers (25-40 years old) underwent multifrequency MRE (20-35 Hz) and THE (27-56 Hz). Fiducial-marker-based optical tracking of the ultrasound field of view was used to align THE to 3D MRE. THE- and MRE-derived shear wave speed (SWS) was determined as a measure of brain stiffness and averaged within regions of various depths for cross-modality correlation analysis. MRE-measured SWS ranged from 1.0 to 1.3 m/s and was negatively correlated with age (R2 = 0.44, p = 0.035). After registration of both modalities, SWS values were linearly correlated (MRE: 1.14 ± 0.08 m/s, THE: 1.13 ± 0.10 m/s; R2 = 0.62, p = 0.007). Best agreement between modalities was achieved at depths of 40-60 mm, suggesting this range provides a viable trade-off between ultrasound attenuation and near-field bias. Similar brain regions can be consistently measured with both elastography modalities, despite the regional and individual variations of stiffness. Transtemporal THE yields stiffness values in a range similar to those obtained with more expensive MRE.

The Role of Endoscopic Ultrasound-Guided Shear Wave Elastography in Pancreatic Diseases

Elastography is a non-invasive imaging modality that has been developed for the evaluation of the stiffness of various organs. It is categorized into two main types: strain elastography and shear wave elastography. While strain elastography offers valuable information on the mechanical properties of the organ being studied, it is limited by the qualitative nature of its measurements and its reliance on operator skills. On the other hand, shear wave elastography overcomes these limitations as it provides a quantitative assessment of tissue stiffness, offers more reproducibility, and is less operator-dependent. Endoscopic ultrasound-guided shear wave elastography (EUS-SWE) is an emerging technique that overcomes the limitations of transabdominal ultrasound in the evaluation of the pancreas. A growing body of literature has demonstrated its safety and feasibility in the evaluation of pancreatic parenchyma. This article provides a comprehensive review of the current state of the literature on EUS-SWE, including its technical aspects, clinical applications in the evaluation of various pancreatic conditions, technological limitations, and future directions.

SWENet: A Physics-Informed Deep Neural Network (PINN) for Shear Wave Elastography

Shear wave elastography (SWE) enables the measurement of elastic properties of soft materials in a non-invasive manner and finds broad applications in various disciplines. The state-of-the-art SWE methods rely on the measurement of local shear wave speeds to infer material parameters and suffer from wave diffraction when applied to soft materials with strong heterogeneity. In the present study, we overcome this challenge by proposing a physics-informed neural network (PINN)-based SWE (SWENet) method. The spatial variation of elastic properties of inhomogeneous materials has been introduced in the governing equations, which are encoded in SWENet as loss functions. Snapshots of wave motions have been used to train neural networks, and during this course, the elastic properties within a region of interest illuminated by shear waves are inferred simultaneously. We performed finite element simulations, tissue-mimicking phantom experiments, and ex vivo experiments to validate the method. Our results show that the shear moduli of soft composites consisting of matrix and inclusions of several millimeters in cross-section dimensions with either regular or irregular geometries can be identified with excellent accuracy. The advantages of the SWENet over conventional SWE methods consist of using more features of the wave motions and enabling seamless integration of multi-source data in the inverse analysis. Given the advantages of SWENet, it may find broad applications where full wave fields get involved to infer heterogeneous mechanical properties, such as identifying small solid tumors with ultrasound SWE, and differentiating gray and white matters of the brain with magnetic resonance elastography.