Locally optimized correlation-guided Bayesian adaptive regularization for ultrasound strain imaging.

Locally optimized correlation-guided Bayesian adaptive regularization for ultrasound strain imaging.
Phys Med Biol. 2020 Feb 06;:
Authors: Mukaddim RA, Meshram NH, Varghese T
Abstract
Ultrasound strain imaging utilizes radio-frequency (RF) ultrasound echo signals to estimate the relative elasticity of tissue under deformation. Due to the diagnostic value inherent in tissue elasticity, ultrasound strain imaging has found widespread clinical and preclinical applications. Accurate displacement estimation using pre and post-deformation RF signals is a crucial first step to derive high quality strain tensor images. Incorporating regularization into the displacement estimation framework is a commonly employed strategy to improve estimation accuracy and precision. In this work, we propose an adaptive variation of the iterative Bayesian regularization scheme utilizing RF similarity metric signal-to-noise ratio previously proposed by our group. The regularization scheme is incorporated into a two-dimensional multi-level block matching (BM) algorithm for motion estimation. Adaptive nature of our algorithm is attributed to the dynamic variation of iteration number based on the normalized cross-correlation (NCC) function quality and a similarity measure between pre-deformation and motion compensated post-deformation RF signals. The proposed method is validated for either quasi-static and cardiac elastography or strain imaging applications using ...

Source: Physics in Medicine and Biology - February 5, 2020 Category: Physics Authors: Mukaddim RA, Meshram NH, Varghese T Tags: Phys Med Biol Source Type: research