Robust Reconstruction of Elasticity Using Ultrasound Imaging and Multi-Frequency Excitations

Frame composite imaging method based on time-sharing latency excitation for ultrasound shear wave elastography

Ultrasound shear wave elastography is an imaging modality that noninvasively assesses mechanical properties of tissues. The results of elastic imaging are obtained by accurately estimating the propagation velocity of shear wave fronts. However, the acquisition rate of the shear wave acquisition device is limited by the hardware of the system. Therefore, increasing the collection rate of shear waves can directly improve the quality of shear wave velocity images. In addition, the problem of velocity reconstruction with relatively small elastic inclusions has always been a challenge in elastic imaging and a very important and urgent issue in early disease diagnosis. For the problem of elastography detection of the shape and boundary of inclusions in tissues, Time-sharing latency excitation frame composite imaging (TS-FCI) method is proposed for tissue elasticity measurement. The method fuses the shear wave motion data generated by time sharing and latency excitation to obtain a set of composite shear wave motion data. Based on the shear wave motion data, the local shear wave velocity image is reconstructed in the frequency domain to obtain the elastic information of the tissue. The experimental results show that the TS-FCI method has a velocity estimation error of 11 % and a contrast to noise ratio (CNR) of 3.81 when estimating inclusions with smaller dimensions (2.53 mm). Furthermore, when dealing with inclusions with small elastic changes (10 kPa), the velocity estimation error is 3 % and the CNR is 3.21. Compared to conventional time-domain and frequency-domain analysis methods, the proposed method has advantages. Results and analysis have shown that this method has potential promotional value in the quantitative evaluation of organizational elasticity.

Phase-Domain Photoacoustic Mechanical Imaging for Quantitative Elastography and Viscography

The role and importance of mechanical properties of cells and tissues in pathophysiological processes have widely been acknowledged. However, current elastography techniques most based on transverse elastic waves, diminish the translation of wave speed into elastic modulus due to its limited wave propagation direction. Here, we propose phase-domain photoacoustic mechanical imaging (PD-PAMI), leveraging the initial time and phase response characteristics of an omnidirectional photoacoustic elastic wave to quantitatively extract elastic and viscous moduli. Theoretical simulations and experiment on tissue-mimicking phantoms with different levels of viscoelastic properties were conducted to validate the approach with a precision in elasticity and viscosity estimation of 4.6% and 6.6%, respectively. The trans-scale viscoelasticity mappings over three length scales-covering cell, tissue section, and in vivo organ, were provided to demonstrate the scalability of the technique with different implementations of PD-PAMI. Experiments on animal models of breast tumour and atherosclerosis reveal that PD-PAMI technique enables effective monitoring of the viscoelastic parameters for examinations of the diseases involved with the variations in collagen or lipid composition and in inflammation level. PD-PAMI technique opens new perspectives of conventional PA imaging and provides new technical way for biomechanical imaging, prefiguring potential clinical applications in mechanopathology-involved disease diagnosis.

Finite difference-embedded UNet for solving transcranial ultrasound frequency-domain wavefield

Transcranial ultrasound imaging assumes a growing significance in the detection and monitoring of intracranial lesions and cerebral blood flow. Accurate solution of partial differential equation (PDE) is one of the prerequisites for obtaining transcranial ultrasound wavefields. Grid-based numerical solvers such as finite difference (FD) and finite element methods have limitations including high computational costs and discretization errors. Purely data-driven methods have relatively high demands on training datasets. The fact that physics-informed neural network can only target the same model limits its application. In addition, compared to time-domain approaches, frequency-domain solutions offer advantages of reducing computational complexity and enabling stable and accurate inversions. Therefore, we introduce a framework called FD-embedded UNet (FEUNet) for solving frequency-domain transcranial ultrasound wavefields. The PDE error is calculated using the optimal 9-point FD operator, and it is integrated with the data-driven error to jointly guide the network iterations. We showcase the effectiveness of this approach through experiments involving idealized skull and brain models. FEUNet demonstrates versatility in handling various input scenarios and excels in enhancing prediction accuracy, especially with limited datasets and noisy information. Finally, we provide an overview of the advantages, limitations, and potential avenues for future research in this study.

Controllable Angle Shear Wavefront Reconstruction Based on Image Fusion Method for Shear Wave Elasticity Imaging

The generation and measurement of shear waves are critical in ultrasonic elasticity imaging. Generally, the resulting wavefront direction is very important for accurately measuring the shear speed and estimating the medium elasticity. In this article, the proposed method can generate a compound shear wavefront with the same direction as speed reconstruction and zero angles between the wavefront and the focus direction, which can improve the estimation accuracy of shear wave velocity. Also, this method, called time-division multipoint excitation image fusion (TDMPEIF), can reconstruct the shear wave propagation images acquired at different depths of a medium according to the frame sequence to produce the shear waves front with a regulable angle. Moreover, the shear wave speed and the elasticity of a medium can be mapped quantitatively with this method. The results demonstrate that the TDMPEIF can improve the quality of the shear wave velocity images, which has wide application value and good promotion prospects for quantitative evaluation of tissue elasticity.