3D Soft Tissue Stiffness Mapping Using Full-Waveform Elastography
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Solution Overview
Problem
Current ultrasound elastography methods struggle to provide accurate 3D stiffness maps of soft tissues due to limitations in wave propagation modeling, computational complexity, and reliance on non-robust cost functionals, leading to erroneous reconstructions and inability to handle heterogeneous media.
Innovation Solution
A cross-correlation-based cost functional and full-waveform modeling are employed to reconstruct 3D stiffness maps, utilizing acoustic radiation forces to generate particle velocities, and iteratively refine stiffness maps across multiple frequencies, enabling robust and efficient reconstruction of viscoelasticity variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional least-squares methods are used for stiffness reconstruction, then the computational process is simple, but the reconstruction accuracy is poor and sensitivity to initial guesses is high
Solution Approach 1:
The patent changes the mathematical formulation from traditional least-squares to cross-correlation-based cost functional, transforming the optimization problem to achieve better convergence properties and reduced sensitivity to initial guesses while maintaining computational feasibility
Solution Approach 2:
The patent implements iterative optimization with feedback mechanisms where the stiffness map is continuously refined based on the mismatch between measured and simulated wavefields, using cross-correlation to guide the optimization process toward accurate reconstructions
2Measurement precision
If full-waveform modeling is used for accurate wave propagation, then the reconstruction accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the wavefield into frequency components and processes them through multiple frequency steps, allowing the complex full-waveform inversion to be broken down into manageable frequency-dependent operations that converge more efficiently
Solution Approach 2:
The patent employs dynamic optimization where the modeling complexity is adjusted adaptively during the inversion process, starting with simpler models and progressively incorporating more complex wave propagation effects as the solution converges
3Volume of moving object
If 3D stiffness maps are reconstructed from 2D measurements, then the spatial information is improved, but the reconstruction reliability decreases
Solution Approach 1:
The patent leverages the third dimension by reconstructing 3D stiffness maps from 2D ultrasound measurements, using wave propagation physics to infer out-of-plane properties from in-plane measurements, thereby adding spatial information while maintaining reliability through physically-based modeling
4Measurement precision
If multiple frequencies are used for reconstruction, then the viscoelasticity characterization improves, but the computational time increases
Solution Approach 1:
The patent uses periodic wavefield oscillations at multiple frequencies to characterize viscoelasticity, exploiting the frequency-dependent behavior of soft tissues to extract mechanical properties more efficiently than single-frequency methods
Solution Approach 2:
The patent performs preliminary stiffness map reconstruction at lower frequencies where wave propagation is more stable, using these initial estimates to guide higher-frequency reconstructions, thereby reducing the overall computational time required for multi-frequency viscoelasticity characterization
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides accurate and computationally efficient 3D stiffness maps of soft tissues, reducing sensitivity to initial guesses and push amplitudes, and effectively handling heterogeneous media, with improved convergence and accuracy compared to traditional least-squares methods.
Implementation Method 1
The particle velocities can be generated from acoustic radiation forces
Implementation Method 2
obtain particle velocities using the ultrasound scanner, from generated mechanical waves in tissue of a subject
Data Source
AI summary
Various examples are provided related to methods and systems to reconstruct 3D image/map for the stiffness of soft tissues. In one example, a method includes obtaining particle velocities from generated mechanical waves in tissue of a subject; reconstructing a stiffness map of soft tissue in a region of interest (ROI) from the particle velocities; and generating an image of the stiffness map for rendering on a user device. In another example, a system includes an ultrasound scanner for shear wave elastography (SWE) and a computing or processing device that can obtain particle velocities; reconstruct a stiffness map of soft tissue in a ROI from the particle velocities; and generate an image of the stiffness map for rendering. The particle velocities can be generated from acoustic radiation forces produced by the ultrasound scanner.