Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

Abstract

Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound or MR images) and known external forces. Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation.

Imaging the dynamics of cardiac fiber orientation in vivo using 3D Ultrasound Backscatter Tensor Imaging

“…Optical methods such as optical coherence tomography 17 and two-photon microtomy 18 can also map the fiber directions at the microscopic level but remain limited to superficial ex vivo tissue and to small regions of interest. In ultrasound imaging, several methods have been proposed to quantify the anisotropy of various physical parameters linked to the fiber orientation, including the ultrasonic attenuation 19, 20 , the integrated backscattered intensity 19–22 and the myocardial stiffness using Elastic Tensor Imaging (ETI) 23 . However, none of these methods has been yet implemented for the time-resolved, 3D mapping of the myocardial fibers orientation.…”

Quantitative evaluation of atrial radio frequency ablation using intracardiac shear‐wave elastography

“…In the field of cardiac imaging, the technique has already been successfully implemented to measure quantitatively the temporal variation of stiffness occurring in the myocardium in vivo during the cardiac cycle 34 and the fiber orientation of the different layers of the cardiac muscle. 35 An intracardiac ultrasound approach was proposed by Hollender et al 36 in vivo to assess the stiffness of the interventricular septum using a shear-wave velocimetry technique and ARFI for the characterization of infarcts in porcine heart in vivo. 37 In this study, we have developed a novel, intracardiac, ultrasound-catheter-based SWE technique to image the extent of ablation lesions in vivo.…”

Bayesian estimation of non-rigid mechanical parameters using temporal sequences of deformation samples

“…Following the wide adoption of ultrasound, elastography [2] emerged, which estimates relative elasticity properties by measuring both the deformation of the tissue via ultrasound images and explicitly measuring the external force using special devices. In the last decade or so, numerical methods, such as inverse Finite Element Methods (FEM) [3], [4], were proposed to estimate mechanical properties of deformable body without the measurement of the displacement field. But, these techniques are generally limited to quasi-static deformation process.…”