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19 June 2024 Full field elastography using deep learning approach
Maud Legrand, Nina Dufour, Emmanuel Martins Seromenho, Nadia Bahlouli, Amir Nahas
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Proceedings Volume PC13010, Tissue Optics and Photonics III; PC130100F (2024) https://doi.org/10.1117/12.3016397
Event: SPIE Photonics Europe, 2024, Strasbourg, France
Abstract
Throughout the history of medicine, assessing stiffness through palpation has served as an indicator to gauge tissue health. Within our research team, we are advancing an innovative approach for full-field optical elastography, rooted in noise correlation analysis. This method leverages the relationship between the correlation function of a diffuse shear wave field and the time reversal of the shear wave field. By examining the correlation function, we then have access to an estimation of the shear wave speed, directly linked to tissue stiffness. Recent findings using this approach have shown great promise. However, in most cases, only the elasticity is quantified, despite the availability of additional information, such as viscosity, also present in the correlation function. In this paper, we introduce our initial outcomes in integrating noise correlation with artificial intelligence. More specifically, we employ a U-NET-based architecture to process noise correlation data.
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Maud Legrand, Nina Dufour, Emmanuel Martins Seromenho, Nadia Bahlouli, and Amir Nahas "Full field elastography using deep learning approach", Proc. SPIE PC13010, Tissue Optics and Photonics III, PC130100F (19 June 2024); https://doi.org/10.1117/12.3016397
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KEYWORDS
Elastography
Deep learning
Correlation function
Education and training
Data processing
Elasticity
Image processing
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