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FEM-based elasticity reconstruction using ultrasound for imaging tissue ablation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Success of ablation treatment depends on the accurate placement of the target ablation focus and the complete destruction of the pathological tissue. Thus, monitoring the formation, location, and size of the ablated lesion is essential. As ablated tissue gets stiffer, an option for ablation monitoring is ultrasound elastography, for imaging the tissue mechanical properties. Reconstruction of elasticity distribution can be achieved by solving an inverse problem from observed displacements, based on a deformable tissue model, commonly discretized by the finite element method (FEM). However, available reconstruction techniques are prone to noise and may achieve suboptimal accuracy.

Methods

We propose a novel inverse problem formulation and elasticity reconstruction method, in which both the elasticity parameters and the model displacements are estimated as independent parameters of an unconstrained optimization problem. Total variation regularization of spatial elasticity distribution is introduced in this formulation, providing robustness to noise.

Results

Our approach was compared to state of the art direct and iterative harmonic elastography techniques. We employed numerical simulation studies using various noise and inclusion contrasts, given multiple excitation frequencies. Compared to alternatives, our method leads to a decrease in RMSE of up to 50% and an increase in CNR of up to 11 dB in numerical simulations. The methods were also compared on an ex vivo bovine liver sample that was locally subjected to ablation, for which improved lesion delineation was obtained with our proposed method. Our method takes \(\sim 4\,\hbox {s}\) for \(20\times 20\) reconstruction grid.

Conclusions

We present a novel FEM problem formulation that improves reconstruction accuracy and inclusion delineation compared to currently available techniques.

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Notes

  1. M. Schmidt. minFunc: unconstrained differentiable multivariate optimization in Matlab. http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html, 2005.

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Acknowledgements

This work was funded by Swiss National Science Foundation (SNSF).

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Authors and Affiliations

  1. Computer-Assisted Applications in Medicine Group, Swiss Federal Institute of Technology (ETH Zurich), 8092, Zurich, Switzerland

    Corin F. Otesteanu, Valery Vishnevsky & Orcun Goksel

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  1. Corin F. Otesteanu
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  2. Valery Vishnevsky
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  3. Orcun Goksel
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Correspondence to Corin F. Otesteanu.

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Otesteanu, C.F., Vishnevsky, V. & Goksel, O. FEM-based elasticity reconstruction using ultrasound for imaging tissue ablation. Int J CARS 13, 885–894 (2018). https://doi.org/10.1007/s11548-018-1714-x

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  • DOI: https://doi.org/10.1007/s11548-018-1714-x

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