Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography
Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction,...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
31.05.2023
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Online Access | Get full text |
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Summary: | Tracking the displacement between the pre- and post-deformed radio-frequency
(RF) frames is a pivotal step of ultrasound elastography, which depicts tissue
mechanical properties to identify pathologies. Due to ultrasound's poor ability
to capture information pertaining to the lateral direction, the existing
displacement estimation techniques fail to generate an accurate lateral
displacement or strain map. The attempts made in the literature to mitigate
this well-known issue suffer from one of the following limitations: 1) Sampling
size is substantially increased, rendering the method computationally and
memory expensive. 2) The lateral displacement estimation entirely depends on
the axial one, ignoring data fidelity and creating large errors. This paper
proposes exploiting the effective Poisson's ratio (EPR)-based mechanical
correspondence between the axial and lateral strains along with the RF data
fidelity and displacement continuity to improve the lateral displacement and
strain estimation accuracies. We call our techniques MechSOUL
(Mechanically-constrained Second-Order Ultrasound eLastography) and L1-MechSOUL
(L1-norm-based MechSOUL), which optimize L2- and L1-norm-based penalty
functions, respectively. Extensive validation experiments with simulated,
phantom, and in vivo datasets demonstrate that MechSOUL and L1-MechSOUL's
lateral strain and EPR estimation abilities are substantially superior to those
of the recently-published elastography techniques. We have published the MATLAB
codes of MechSOUL and L1-MechSOUL at http://code.sonography.ai. |
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DOI: | 10.48550/arxiv.2305.20059 |