Performance Analysis of a New Real-Time Elastographic Time Constant Estimator

Abstract

New elastographic techniques such as poroelastography and viscoelasticity imaging aim at imaging the temporal mechanical behavior of tissues. These techniques usually involve the use of curve fitting methods being applied to noisy data to estimate new elastographic parameters. As of today, however, current elastographic implementations of poroelastography and viscoelasticity imaging methods are in general too slow and not optimized for clinical applications. Furthermore, image quality performance of these new elastographic techniques is still largely unknown due to a paucity of data and the lack of systematic studies that analyze their performance limitations. In this paper, we propose a new elastographic time constant (TC) estimator, which is based on the use of the least square error (LSE) curve-fitting method and the Levenberg-Marquardt (LM) optimization rule as applied to noisy elastographic data obtained from a material in a creep-type experiment. The algorithm is executed on a massively parallel general purpose graphics processing unit (GPGPU) to achieve real-time performance. The estimator's performance is analyzed using simulations. Experimental results obtained from poroelastic phantoms are presented as a proof of principle of the new estimator's technical applicability on real experimental data. The results of this study demonstrate that the newly proposed elastographic estimator can produce highly accurate and sensitive elastographic TC estimates in real-time and at high signal-to-noise ratios.

Effect of Permeability on the Performance of Elastographic Imaging Techniques

“…The individual values of the mechanical parameters used for the simulation models were chosen based on previous phantom experiments [6], [25], given a paucity of data regarding the poroelastic behavior of tissues in vivo. This should not detract from the major findings of the image quality analysis resented in this paper, which depends primarily on the underlying contrast between background and target rather than their individual values of permeability.…”

“…Linear poroelastic materials properties were assigned to the inclusion and bi-layer models as specified in Table I. The baseline material parameters were chosen based on previous experimental work [6], [25]. It is important to note that in the investigation of the and the CTE, what is relevant is the contrast between background and target rather than the absolute values of the underlying mechanical parameters.…”

“…Each EPR elastogram was computed as the ratio between a lateral strain elastogram and corresponding axial strain elastogram [5]. The axial strain time constant (TC) was estimated from the exponential increase in the mean axial strain and the EPR TC was estimated from the exponential decrease in the mean EPR using curve fitting algorithms developed in our laboratory [25].…”

“…Poroelastography methods typically aim at imaging the axial strain distribution and/or the effective Poisson's ratio (EPR) distribution and their temporal evolutions experienced by a tissue under sustained compression [3], [4], [8], [9], [25]. In these techniques, the tissue is typically modeled as a poroelastic material.…”

“…This work was later experimentally corroborated using controlled phantoms [5]. More recently, techniques have been proposed to compute various temporal elastographic parameters from axial strain and EPR temporal curves obtained from poroelastic media under compression [6], [8]- [10], [25]. While fundamental for the development of this new imaging field, the majority of these studies report data on homogeneous materials or simplistic nonhomogeneous phantoms [6], with only limited applicability to real tissues.…”