Depth Evaluation of Soft Tissue Mimicking Phantoms Using Surface Acoustic Waves

Li, C.; Li, S.; Wei, Cheng; Wang, Ke; Huang, Zhihong

doi:10.1016/j.phpro.2015.03.029

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Agar phantom was employed to validate the proposed inversion models in this study. The general protocol for producing the agar phantom has been described in detail in our previous study [12]. Three two-layer agar phantoms with different top-layer thicknesses and one three-layer agar phantom were produced.…”

Section: A Sample Preparation 1) Multi-layered Agar Phantommentioning

confidence: 99%

A Weighted Average Phase Velocity Inversion Model for Depth-Resolved Elasticity Evaluation in Human Skin In-Vivo

Zhou¹,

Feng²,

et al. 2021

IEEE Trans. Biomed. Eng.

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In current surface acoustic wave (SAW) elastography field, wavelength-depth inversion model is a straightforward and widely used inversion model for depth-resolved elasticity profile reconstruction. However, the elasticity directly evaluated from the wavelength-depth relationship is biased. Thus, a new inversion model, termed weighted average phase velocity (WAPV) inversion model, is proposed to provide depth-resolved Young's modulus estimate with better accuracy. Methods: The forward model for SAW phase velocity dispersion curve generation was derived from the numerical simulations of SAWs in layered materials, and inversion was implemented by matching the measured phase velocity dispersion curve to the one generated from the forward model using the least squares fitting. Three two-layer agar phantoms with different top-layer thicknesses and one three-layer agar phantom were tested to validate the proposed inversion model. Then the model was demonstrated on human skin at various sites (palm, forearm and back of hand) in-vivo. Results: In multi-layered agar phantoms, depth-resolved elasticity estimates provided by the model have a maximal total inversion error of 15.2% per sample after inversion error compensation. In in-vivo human skin, the quantified bulk Young's moduli (palm: 212 ± 78 kPa; forearm: 32 ± 11 kPa and back of hand: 29 ± 8 kPa) are comparable to the reference values in the literature. Conclusion:The WAPV inversion model can provide accurate depth-resolved Young's modulus estimates in layered biological soft tissues. Significance: The proposed model can predict depth-resolved elasticity in layered biological soft tissues with a reasonable accuracy which traditional wavelength-depth inversion model cannot provide.

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Section: A Sample Preparation 1) Multi-layered Agar Phantommentioning

confidence: 99%

A Weighted Average Phase Velocity Inversion Model for Depth-Resolved Elasticity Evaluation in Human Skin In-Vivo

Zhou¹,

Feng²,

et al. 2021

IEEE Trans. Biomed. Eng.

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High‐intensity‐focused ultrasound and phase‐sensitive optical coherence tomography for high resolution surface acoustic wave elastography

Zhou

Huang

et al. 2017

Journal of Biophotonics

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Elastography has the ability of quantitatively evaluating the mechanical properties of soft tissue; thus it is helpful for diagnosis and treatment monitoring of many diseases, for example, skin diseases. Surface acoustic waves (SAWs) have been proven to be a non-invasive, non-destructive method for accurate characterization of tissue elastic properties. Current SAW elastography using high-energy laser pulse or mechanical shaker still have some problems. In order to improve SAW elastography in medical application, a new technique was proposed in this paper, which combines high-intensity-focused ultrasound as a SAWs impulse inducer and phase-sensitive optical coherence tomography as a SAWs detector. A 2% agar-agar phantom and ex-vivo porcine skin were tested. The data were processed by a new algorithm based on the Fourier analysis. The results show that the proposed method has the capability of quantifying the elastic properties of soft tissue-mimicking materials. The lateral resolution of the elastogram has been significantly improved and the different layers in heterogeneous material could also been distinguished. Our improved technique of SAW elastography has a large potential to be widely applied in clinical use for skin disease diagnosis and treatment monitoring.

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VP-net: an end-to-end deep learning network for elastic wave velocity prediction in human skin in vivo using optical coherence elastography

Zhang,

Liao,

Feng

et al. 2024

Front. Bioeng. Biotechnol.

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IntroductionAcne vulgaris, one of the most common skin conditions, affects up to 85% of late adolescents, currently no universally accepted assessment system. The biomechanical properties of skin provide valuable information for the assessment and management of skin conditions. Wave-based optical coherence elastography (OCE) quantitatively assesses these properties of tissues by analyzing induced elastic wave velocities. However, velocity estimation methods require significant expertise and lengthy image processing times, limiting the clinical translation of OCE technology. Recent advances in machine learning offer promising solutions to simplify velocity estimation process.MethodsIn this study, we proposed a novel end-to-end deep-learning model, named velocity prediction network (VP-Net), aiming to accurately predict elastic wave velocity from raw OCE data of in vivo healthy and abnormal human skin. A total of 16,424 raw phase slices from 1% to 5% agar-based tissue-mimicking phantoms, 28,270 slices from in vivo human skin sites including the palm, forearm, back of the hand from 16 participants, and 580 slices of facial closed comedones were acquired to train, validate, and test VP-Net.ResultsVP-Net demonstrated highly accurate velocity prediction performance compared to other deep-learning-based methods, as evidenced by small evaluation metrics. Furthermore, VP-Net exhibited low model complexity and parameter requirements, enabling end-to-end velocity prediction from a single raw phase slice in 1.32 ms, enhancing processing speed by a factor of ∼100 compared to a conventional wave velocity estimation method. Additionally, we employed gradient-weighted class activation maps to showcase VP-Net’s proficiency in discerning wave propagation patterns from raw phase slices. VP-Net predicted wave velocities that were consistent with the ground truth velocities in agar phantom, two age groups (20s and 30s) of multiple human skin sites and closed comedones datasets.DiscussionThis study indicates that VP-Net could rapidly and accurately predict elastic wave velocities related to biomechanical properties of in vivo healthy and abnormal skin, offering potential clinical applications in characterizing skin aging, as well as assessing and managing the treatment of acne vulgaris.

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Depth Evaluation of Soft Tissue Mimicking Phantoms Using Surface Acoustic Waves

Cited by 3 publications

References 13 publications

A Weighted Average Phase Velocity Inversion Model for Depth-Resolved Elasticity Evaluation in Human Skin In-Vivo

A Weighted Average Phase Velocity Inversion Model for Depth-Resolved Elasticity Evaluation in Human Skin In-Vivo

High‐intensity‐focused ultrasound and phase‐sensitive optical coherence tomography for high resolution surface acoustic wave elastography

VP-net: an end-to-end deep learning network for elastic wave velocity prediction in human skin in vivo using optical coherence elastography

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