title: Improving Displacement Signal-to-Noise Ratio for Low-Signal Radiation Force Elasticity Imaging Using Bayesian Techniques.
abstract: Radiation force-based elasticity imaging is currently being investigated as a possible diagnostic modality for a number of clinical tasks, including liver fibrosis staging and the characterization of cardiovascular tissue. In this study, we evaluate the relationship between peak displacement magnitude and image quality and propose using a Bayesian estimator to overcome the challenge of obtaining viable data in low displacement signal environments. Displacement data quality were quantified for two common radiation force-based applications, acoustic radiation force impulse imaging, which measures the displacement within the region of excitation, and shear wave elasticity imaging, which measures displacements outside the region of excitation. Performance as a function of peak displacement magnitude for acoustic radiation force impulse imaging was assessed in simulations and lesion phantoms by quantifying signal-to-noise ratio (SNR) and contrast-to-noise ratio for varying peak displacement magnitudes. Overall performance for shear wave elasticity imaging was assessed in ex vivo chicken breast samples by measuring the displacement SNR as a function of distance from the excitation source. The results show that for any given displacement magnitude level, the Bayesian estimator can increase the SNR by approximately 9 dB over normalized cross-correlation and the contrast-to-noise ratio by a factor of two. We conclude from the results that a Bayesian estimator may be useful for increasing data quality in SNR-limited imaging environments.
authors.affiliation: Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
authors.forename: Douglas M
authors.initials: DM
authors.lastname: Dumont
authors.affiliation: Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
authors.forename: Kristy M
authors.initials: KM
authors.lastname: Walsh
authors.affiliation: Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA. Electronic address: brett.c.byram@vanderbilt.edu.
authors.forename: Brett C
authors.initials: BC
authors.lastname: Byram
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002645:Chickens; D003198:Computer Simulation; D054459:Elasticity Imaging Techniques / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
keywords: Bayes; Displacement estimation; Elasticity imaging; Elastography; Medical imaging; Shear wave; Ultrasound