A Spline Interpolation–based Data Reconstruction Technique for Estimation of Strain Time Constant in Ultrasound Poroelastography. (January 2020)

• Record Type: Journal Article
• Authors: Islam, Md Tauhidul; Righetti, Raffaella
• Is Part Of: Ultrasonic imaging. Volume 42:Number 1(2020)
• Page Start: 5
• Page End: 14
• Publication Date: 2020-01
• Subjects: elastography -- cancer imaging -- frame reconstruction -- cubic spline -- time constant; Diagnostic ultrasonic imaging -- Methodology -- Periodicals; Ultrasonic testing -- Periodicals; Ultrasonic imaging -- Periodicals; Ultrasonography -- Periodicals; Échographie -- Méthodologie -- Périodiques; Essais par ultrasons -- Périodiques; Imagerie ultrasonore -- Périodiques; 616.07543
• DOI: 10.1177/0161734619895519
• Languages: English
• ISSNs: 0161-7346

Abstract

Ultrasound poroelastography is a cost-effective and noninvasive imaging technique, which can be used to reconstruct mechanical parameters of tissues such as Young's modulus, Poisson's ratio, interstitial permeability, and vascular permeability. To estimate interstitial permeability and vascular permeability using poroelastography, accurate estimation of the strain time constant (TC) is required. This can be a challenging task due to the nonlinearity of the exponential strain curve and noise affecting the experimental data. Due to motion artifacts caused by the sonographer, animal/patient, and/or the environment, noise affecting some strain frames can be significantly higher than the strain signal. If these frames are used for the computation of the strain TC, the resulting TC estimate can be highly inaccurate, which, in turn, can cause high errors in the reconstructed mechanical parameters. In this paper, we introduce a cubic spline–based interpolation method, which allows to use only good quality strain frames (i.e., frames with sufficiently high signal-to-noise ratio [SNR]) to estimate the strain TC. Using finite element simulations, we demonstrate that the proposed interpolation method can improve the estimation accuracy of the strain TC by 46% with respect to the case where no interpolation and filtering are used and by 37% with respect to the case where the strain frames are Kalman filtered before TC estimation (at an SNR of 30 dB). We also prove the technical feasibility of the proposed technique using in vivo experimental data. While detecting the bad frames in both simulations and experiments, we assumed the lower limit SNR to be below 10 dB. Based on our results, the proposed technique may be of great help in applications relying on the accurate assessment of the temporal behavior of strain data.