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An H∞ Strategy for Strain Estimation in Ultrasound Elastography Using Biomechanical Modeling Constraint

Overview of attention for article published in PLOS ONE, September 2013
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Title
An H∞ Strategy for Strain Estimation in Ultrasound Elastography Using Biomechanical Modeling Constraint
Published in
PLOS ONE, September 2013
DOI 10.1371/journal.pone.0073093
Pubmed ID
Authors

Zhenghui Hu, Heye Zhang, Jinwei Yuan, Minhua Lu, Siping Chen, Huafeng Liu

Abstract

The purpose of ultrasound elastography is to identify lesions by reconstructing the hardness characteristics of tissue reconstructed from ultrasound data. Conventional quasi-static ultrasound elastography is easily applied to obtain axial strain components along the compression direction, with the results inverted to represent the distribution of tissue hardness under the assumption of constant internal stresses. However, previous works of quasi-static ultrasound elastography have found it difficult to obtain the lateral and shear strain components, due to the poor lateral resolution of conventional ultrasound probes. The physical nature of the strain field is a continuous vector field, which should be fully described by the axial, lateral, and shear strain components, and the clinical value of lateral and shear strain components of deformed tissue is gradually being recognized by both engineers and clinicians. Therefore, a biomechanical-model-constrained filtering framework is proposed here for recovering a full displacement field at a high spatial resolution from the noisy ultrasound data. In our implementation, after the biomechanical model constraint is integrated into the state-space equation, both the axial and lateral displacement components can be recovered at a high spatial resolution from the noisy displacement measurements using a robust H∞ filter, which only requires knowledge of the worst-case noise levels in the measurements. All of the strain components can then be calculated by applying a gradient operator to the recovered displacement field. Numerical experiments on synthetic data demonstrated the robustness and effectiveness of our approach, and experiments on phantom data and in-vivo clinical data also produced satisfying results.

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Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 24%
Student > Master 5 17%
Student > Bachelor 3 10%
Professor 3 10%
Researcher 3 10%
Other 6 21%
Unknown 2 7%
Readers by discipline Count As %
Engineering 15 52%
Medicine and Dentistry 8 28%
Computer Science 1 3%
Psychology 1 3%
Unknown 4 14%