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Register a new userPlane Wave Elastography: A Frequency-Domain Ultrasound Shear Wave Elastography Approach
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In this paper, we propose Plane Wave Elastography (PWE), a novel ultrasound shear wave elastography (SWE) approach. Currently, commercial methods for SWE rely on directional filtering based on the prior knowledge of the wave propagation direction, to remove complicated wave patterns formed due to reflection and refraction. The result is a set of decomposed directional waves that are separately analyzed to construct shear modulus fields that are then combined through compounding. Instead, PWE relies on a rigorous representation of the wave propagation using the frequency-domain scalar wave equation to automatically select appropriate propagation directions and simultaneously reconstruct shear modulus fields. Specifically, assuming a homogeneous, isotropic, incompressible, linear-elastic medium, we represent the solution of the wave equation using a linear combination of plane waves propagating in arbitrary directions. Given this closed-form solution, we formulate the SWE problem as a nonlinear least-squares optimization problem which can be solved very efficiently. Through numerous phantom studies, we show that PWE can handle complicated waveforms without prior filtering and is competitive with state-of-the-art that requires prior filtering based on the knowledge of propagation directions.
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By decoupling motion and spatial encoding, magnitude contrast MR Elastography could be performed for the first time at ultrashort echo times (12 $mu$s). On the basis of a motion-sensitizing magnetization preparation, the available total magnetic moment is sensitized to the motion induced in the tissues so the information can be efficiently carried over by the MR signal magnitude when the selected imaging pulse sequence is applied. The new paradigm allows also for shorter total acquisition times as demonstrated here in a set of homogeneous and heterogeneous phantoms with up to 5-fold acceleration factors. Summary of Main Findings/Short Synopsis Magnitude contrast MR Elastography was developed on the basis of a motionsensitizing magnetization preparation to subsequently make use of any type of imaging sequence, like UTE or ZTE, to mechanically characterize tissues, otherwise inaccessible with standard MRE.
Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force. Different levels of deformation are obtained from different tissue types depending on their mechanical properties, where stiffer tissues deform less. Given two radio frequency (RF) frames collected before and after some deformation, we estimate displacement and strain images by comparing the RF frames. The quality of the strain image is dependent on the type of motion that occurs during deformation. In-plane axial motion results in high-quality strain images, whereas out-of-plane motion results in low-quality strain images. In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image. The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. The RF frames were collected from both phantom and in vivo data.
Objective: Realistic tissue-mimicking phantoms are essential for the development, the investigation and the calibration of medical imaging techniques and protocols. Because it requires taking both mechanical and imaging properties into account, the development of robust, calibrated phantoms is a major challenge in elastography. Soft polyvinyl chloride gels in a liquid plasticizer (plastisol or PVCP) have been proposed as soft tissue-mimicking phantoms (TMP) for elasticity imaging. PVCP phantoms are relatively low-cost and can be easily stored over long time periods without any specific requirements. In this work, the preparation of a PVCP gel phantom for both MR and ultrasoundelastography is proposed and its acoustic, NMR and mechanical properties are studied.Material and methods: The acoustic and magnetic resonance imaging properties of PVCP are measured for different mass ratios between ultrasound speckle particles and PVCP solution, and between resin and plasticizer. The linear mechanical properties of plastisol samples are then investigated over time using not only indentation tests, but also MR and ultrasound-elastography clinical protocols. These properties are compared to typical values reported for biological soft tissues and to the values found in the literature for PVCP gels.Results and conclusions: After a period of two weeks, the mechanical properties of the plastisol samples measured with indentation testing are stable for at least the following 4 weeks (end of follow-up period 43 days after gelation-fusion). Neither the mechanical nor the NMR properties of plastisol gels were found to be affected by the addition of cellulose as acoustic speckle. Mechanical properties of the proposed gels were successfully characterized by clinical, commercially-available MR Elastography and sonoelastography protocols. PVCP with a mass ratio of ultrasound speckle particles of 0.6% to 0.8% and a mass ratio between resin and plasticizer between 50 and 70% appears as a good TMP candidate that can be used with both MR and ultrasound-based elastography methods.
It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected.
This paper introduces a novel technique to estimate tissue displacement in quasi-static elastography. A major challenge in elastography is estimation of displacement (also referred to time-delay estimation) between pre-compressed and post-compressed ultrasound data. Maximizing normalized cross correlation (NCC) of ultrasound radio-frequency (RF) data of the pre- and post-compressed images is a popular technique for strain estimation due to its simplicity and computational efficiency. Several papers have been published to increase the accuracy and quality of displacement estimation based on NCC. All of these methods use spatial windows to estimate NCC, wherein displacement magnitude is assumed to be constant within each window. In this work, we extend this assumption along the temporal domain to exploit neighboring samples in both spatial and temporal directions. This is important since traditional and ultrafast ultrasound machines are, respectively, capable of imaging at more than 30 frame per second (fps) and 1000 fps. We call our method spatial temporal normalized cross correlation (STNCC) and show that it substantially outperforms NCC using simulation, phantom and in-vivo experiments.
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