2022/01/12 by Md Ashikuzzaman, Ashikuzzaman, Md, Hassan Rivaz +1
Engineering · Medicine · #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Medical Physics (physics.med-ph) #Photoacoustic and Ultrasonic Imaging #Ultrasound Imaging and Elastography #Ultrasound and Hyperthermia Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2201.04363
openalex publication_date 2022/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Ultrasound strain imaging, which delineates mechanical properties to detect tissue abnormalities, involves estimating the time-delay between two radio-frequency (RF) frames collected before and after tissue deformation. The existing regularized optimization-based time-delay estimation (TDE) techniques suffer from at least one of the following drawbacks: 1) The regularizer is not aligned with tissue deformation physics due to taking only the first-order displacement derivative into account. 2) The L2-norm of the displacement derivatives, which oversmooths the estimated time-delay, is utilized as the regularizer. 3) The absolute value function should be approximated by a smooth function to facilitate the optimization of L1-norm. Herein, to resolve these shortcomings, we propose employing the alternating direction method of multipliers (ADMM) for optimizing a novel cost function consisting of L2-norm data fidelity term and L1-norm first- and second-order spatial continuity terms. ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high-contrast strain images with smooth background and sharp boundaries. We name our technique ADMM for totaL variaTion RegUlarIzation in ultrasound STrain imaging (ALTRUIST). In extensive simulation, phantom, and in vivo experiments, ALTRUIST substantially outperforms GLUE, OVERWIND, and L1-SOUL, three recently-published TDE algorithms, both qualitatively and quantitatively. ALTRUIST yields 118%, 104%, and 72% improvements of contrast-to-noise ratio over L1-SOUL for simulated, phantom, and in vivo liver cancer datasets, respectively. We will publish the ALTRUIST code after the acceptance of this paper at http://code.sonography.ai.