Ultrasound estimation of strain time constant and vascular permeability in tumors using a CEEMDAN and linear regression-based method. Academic Article uri icon

abstract

  • Ultrasound poroelastography focuses on the estimation of the spatio-temporal mechanical behavior of tissues using data often corrupted with non-stationary noise. The cumulative strain calculated from prolonged temporal acquisition of RF data can face the problem of aggregate noise. This noise can significantly affect the accuracy of curve fitting techniques necessary to estimate the clinically significant strain Time Constant (TC) and related parameters. We present a new technique, which decomposes the non-linear temporal behavior of the differential strain to extract the monotonic decaying trend by using the time-domain and data-driven Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm. A linear regression scheme is then used to obtain the slope of the transformed non-linear trend, which carries information about the strain TC. Assessment of Vascular Permeability (VP), a transport parameter indicative of tumor growth, requires accurate strain TC estimations. Finite Element (FE), ultrasound simulations and in vivo experiments are used to investigate the performance of the proposed technique. Based on the simulation analysis, the average Percentage Relative Error (PRE) values of our method are 4.15% (for TC estimation) and 5.00% (for VP estimation) at 20dB SNR level for different Percentage of Good Frames (PGF) (i.e., 20%, 50%, 75%, and 100%). These PRE values are substantially lower than those obtained using other conventional elastographic techniques. Our proposed method could become a new data-adaptive tool for analyzing the non-linear time-dependent response of complex tissues such as cancers.

published proceedings

  • Comput Biol Med

author list (cited authors)

  • Khan, M., & Righetti, R.

complete list of authors

  • Khan, Md Hadiur Rahman||Righetti, Raffaella

publication date

  • January 1, 2022 11:11 AM