Evidence map›Paper›PMID 29666974›Full record

ArticleInternational journal of computer assisted radiology and surgery2018

FEM-based elasticity reconstruction using ultrasound for imaging tissue ablation.

Corin F Otesteanu, Valery Vishnevsky, Orcun Goksel

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Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Corin F OtesteanuComputer-Assisted Applications in Medicine Group, Swiss Federal Institute of Technology (ETH Zurich), 8092, Zurich, Switzerland. corino@vision.ee.ethz.ch.
Valery VishnevskyComputer-Assisted Applications in Medicine Group, Swiss Federal Institute of Technology (ETH Zurich), 8092, Zurich, Switzerland.
Orcun GokselComputer-Assisted Applications in Medicine Group, Swiss Federal Institute of Technology (ETH Zurich), 8092, Zurich, Switzerland.

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 150620
6 · The paper itself

Abstract

purposeSuccess 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.

methodsWe 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.

resultsOur 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 [Formula: see text] for [Formula: see text] reconstruction grid.

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

Indexed as

Imaging, Three-DimensionalPhantoms, ImagingSurgery, Computer-AssistedAblation TechniquesAnimalsCattleDisease Models, AnimalElasticityFinite Element AnalysisLiverLiver DiseasesUltrasonographyLesion monitoringOptimizationUltrasound elastography

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.