Investigating the impact of spatial priors on the performance of model-based IVUS elastography

Abstract

This paper describes methods that provide pre-requisite information for computing circumferential stress in modulus elastograms recovered from vascular tissue—information that could help cardiologists detect life-threatening plaques and predict their propensity to rupture. The modulus recovery process is an ill-posed problem; therefore additional information is needed to provide useful elastograms. In this work, prior geometrical information was used to impose hard or soft constraints on the reconstruction process. We conducted simulation and phantom studies to evaluate and compare modulus elastograms computed with soft and hard constraints versus those computed without any prior information. The results revealed that (1) the contrast-to-noise ratio of modulus elastograms achieved using the soft prior and hard prior reconstruction methods exceeded those computed without any prior information; (2) the soft prior and hard prior reconstruction methods could tolerate up to 8 % measurement noise; and (3) the performance of soft and hard prior modulus elastogram degraded when incomplete spatial priors were employed. This work demonstrates that including spatial priors in the reconstruction process should improve the performance of model-based elastography, and the soft prior approach should enhance the robustness of the reconstruction process to errors in the geometrical information.

Quantitative sparse array vascular elastography: the impact of tissue attenuation and modulus contrast on performance

“…25,26 However, in vascular imaging, additional constraints (i.e., geometric) are required to produce stable modulus elastograms. [32][33][34][35] Geometrically constrained inversion methods can tolerate higher noticeable amounts of measurement noise than those with just smoothness constraints. 33,35 We performed a pair-wise Welch's t-test on the results reported in Fig.…”

“…[32][33][34][35] Geometrically constrained inversion methods can tolerate higher noticeable amounts of measurement noise than those with just smoothness constraints. 33,35 We performed a pair-wise Welch's t-test on the results reported in Fig. 7, which revealed that the modulus elastograms reconstructed with the three imaging methods were not significantly different (p > 0.05).…”

“…(2) may not provide unique modulus elastograms 30 because the inverse elasticity problem is ill-posed; however, including geometric information in the modulus recovery process will transform the ill-posed problem to a well-posed one. [31][32][33][34][35] We included geometric information in the image reconstruction process by minimizing the following objective function:…”

“…More specifically, the regularization parameter was set to 7.2 × 10 −9 , 5 × 10 −10 , and 1.2 × 10 −9 when reconstruction was performed using displacements measured with CLA, SA, and PW imagings, respectively. We used the L-curve method 35,43 to select the optimum regularization parameter objectively.…”

“…We used a highly controlled and repeatable process to fabricate all phantoms. 35,45,46 More specifically, we placed two off-center rods (3.12-mm diameters) in a cylindrical mold (12-mm diameter by 120-mm long). One rod was circular whereas the other was semicircular.…”