Diffuse shear wave spectroscopy for soft tissue viscoelastic characterization

Beuve, Steve; Kritly, Leopold; Callé, Samuel; Remeniéras, Jean-Pierre

doi:10.1016/j.ultras.2020.106239

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…4 , top right. Shear wave phase velocity estimation is performed in the frequency domain similar to 36 , 37 . First, we define 30 randomly sampled subsets with a length of 800 time steps.…”

Section: Methodsmentioning

confidence: 99%

“…Instead, we implement shear wave elastography imaging (SWEI), where a shear wave is excited on the tissue surface, e.g., by a piezoelectric element 33 or an air-pulse 34 , and the high frequency imaging can be used to track the propagating wave. The elasticity of the tissue is directly related to the velocity of the shear wave which can be estimated by detecting the dominant local wavenumber in the frequency domain 35 – 37 . We combine OCT and OCE, jointly perform data processing with a multi-input deep learning network and estimate tool-tissue interaction forces.…”

Section: Introductionmentioning

confidence: 99%

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Optical force estimation for interactions between tool and soft tissues

Neidhardt

Mieling

Bengs

et al. 2023

Sci Rep

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Robotic assistance in minimally invasive surgery offers numerous advantages for both patient and surgeon. However, the lack of force feedback in robotic surgery is a major limitation, and accurately estimating tool-tissue interaction forces remains a challenge. Image-based force estimation offers a promising solution without the need to integrate sensors into surgical tools. In this indirect approach, interaction forces are derived from the observed deformation, with learning-based methods improving accuracy and real-time capability. However, the relationship between deformation and force is determined by the stiffness of the tissue. Consequently, both deformation and local tissue properties must be observed for an approach applicable to heterogeneous tissue. In this work, we use optical coherence tomography, which can combine the detection of tissue deformation with shear wave elastography in a single modality. We present a multi-input deep learning network for processing of local elasticity estimates and volumetric image data. Our results demonstrate that accounting for elastic properties is critical for accurate image-based force estimation across different tissue types and properties. Joint processing of local elasticity information yields the best performance throughout our phantom study. Furthermore, we test our approach on soft tissue samples that were not present during training and show that generalization to other tissue properties is possible.

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“…4 , top right. Shear wave phase velocity estimation is performed in the frequency domain similar to 36 , 37 . First, we define 30 randomly sampled subsets with a length of 800 time steps.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optical force estimation for interactions between tool and soft tissues

Neidhardt

Mieling

Bengs

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Recently as in [18], many authors decided to use one or multiple shakers to generate a controlled diffuse field. In this way the signal to noise ratio inside the region of interest can be increased and the frequency content of the field can be controlled [16,30,31,45]. For example, in Reverberant Shear Wave Elastography (R-SWE) a narrowband diffuse field is generated with several mechanical actuators, then, the shear wavelength is derived from the spatial autocorrelation function [16,17,46,47].…”

Section: Applications Of Noise Correlation and Time Reversal Elastogrmentioning

confidence: 99%

Shear Wave Elastography Based on Noise Correlation and Time Reversal

et al. 2021

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Shear wave elastography (SWE) relies on the generation and tracking of coherent shear waves to image the tissue's shear elasticity. Recent technological developments have allowed SWE to be implemented in commercial ultrasound and magnetic resonance imaging systems, quickly becoming a new imaging modality in medicine and biology. However, coherent shear wave tracking sets a limitation to SWE because it either requires ultrafast frame rates (of up to 20 kHz), or alternatively, a phase-lock synchronization between shear wave-source and imaging device. Moreover, there are many applications where coherent shear wave tracking is not possible because scattered waves from tissue’s inhomogeneities, waves coming from muscular activity, heart beating or external vibrations interfere with the coherent shear wave. To overcome these limitations, several authors developed an alternative approach to extract the shear elasticity of tissues from a complex elastic wavefield. To control the wavefield, this approach relies on the analogy between time reversal and seismic noise cross-correlation. By cross-correlating the elastic field at different positions, which can be interpreted as a time reversal experiment performed in the computer, shear waves are virtually focused on any point of the imaging plane. Then, different independent methods can be used to image the shear elasticity, for example, tracking the coherent shear wave as it focuses, measuring the focus size or simply evaluating the amplitude at the focusing point. The main advantage of this approach is its compatibility with low imaging rates modalities, which has led to innovative developments and new challenges in the field of multi-modality elastography. The goal of this short review is to cover the major developments in wave-physics involving shear elasticity imaging using a complex elastic wavefield and its latest applications including slow imaging rate modalities and passive shear elasticity imaging based on physiological noise correlation.

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