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Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

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Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging.

Cameron Hoerig^1,2, Jamshid Ghaboussi³, Michael F Insana^1,2

¹Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 United States of America.

Physics in Medicine and Biology

|February 12, 2020

Summary

This summary is machine-generated.

We developed a 3D ultrasonic elastography method (AutoP) that learns material properties without prior models. This technique accurately maps 3D elastic properties using sparse force-displacement data, improving Young

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Area of Science:

Medical Imaging
Biophysics
Materials Science

Background:

Quantitative quasi-static ultrasonic elastography (QUSE) typically relies on model-based inverse methods.
Existing methods often require assumptions about geometric and constitutive material models, limiting their applicability.

Purpose of the Study:

To present a 3D extension of the Autoprogressive Method (AutoP) for quantitative ultrasonic elastography.
To demonstrate the feasibility of recovering 3D linear-elastic material property distributions without prior model assumptions.

Main Methods:

Developed a 3D Autoprogressive Method (AutoP) utilizing sparse force-displacement measurements.
Employed a Cartesian neural network constitutive model (CaNNCM) integrated with finite element analyses.
Introduced a novel regularization term to enhance stress distribution learning.

Main Results:

Successfully recovered the 3D linear-elastic material property distribution of gelatin phantoms.
Demonstrated that multiple force-displacement measurement sets improve accuracy and 3D property estimation.
Showed that decreasing transducer contact area enhances sensitivity to force variations.

Conclusions:

The 3D AutoP method effectively learns material properties without geometric or constitutive model assumptions.
Acquiring diverse force-displacement data and optimizing transducer contact are crucial for accurate 3D elastography.
This approach advances quantitative ultrasonic elastography for material property mapping.