Investigating elastic properties of soft biological tissues

Estimation of Strain Elastography from Ultrasound Radio-Frequency Data by Utilizing Analytic Gradient of the Similarity Metric

Most strain imaging techniques follow a pipeline strategy: in the first step, tissue displacement is estimated from radio-frequency (RF) frames, and in the second step, a spatial derivative operation is applied. There are two main issues that arise from this framework. First, the gradient operation amplifies noise, and therefore, smoothing techniques have to be adopted. Second, strain estimation does not exploit the original RF data. It rather relies solely on the noisy displacement field. In this paper, a novel technique is proposed that utilizes both the displacement field and the RF frames to accurately obtain the strain estimates. The normalized cross correlation (NCC) metric between two corresponding windows around the samples of the pre- and post-compressed images is employed to generate a dissimilarity measurement. The derivative of NCC with respect to the strain is analytically derived using the chain rule. This allows an efficient minimization of the dissimilarity metric with respect to the strain using the gradient descent optimization technique. The effectiveness of the proposed method is investigated through simulation data, phantom experiments, and in vivo patient data. The experimental results show that exploiting the information in RF data significantly improves the strain estimates.

Full 3-D transverse oscillations: a method for tissue motion estimation

We present a new method to estimate 4-D (3-D + time) tissue motion. The method used combines 3-D phase based motion estimation with an unconventional beamforming strategy. The beamforming technique allows us to obtain full 3-D RF volumes with axial, lateral, and elevation modulations. Based on these images, we propose a method to estimate 3-D motion that uses phase images instead of amplitude images. First, volumes featuring 3-D oscillations are created using only a single apodization function, and the 3-D displacement between two consecutive volumes is estimated simultaneously by applying this 3-D estimation. The validity of the method is investigated by conducting simulations and phantom experiments. The results are compared with those obtained with two other conventional estimation methods: block matching and optical flow. The results show that the proposed method outperforms the conventional methods, especially in the transverse directions.

Computational elastography from standard ultrasound image sequences by global trust region optimization

A new approach is proposed to estimate the spatial distribution of shear modulus of tissues in-vivo. An image sequence is acquired using a standard medical ultrasound scanner while varying the force applied to the handle. The elastic properties are then recovered simultaneously with the inter-frame displacement fields using a computational procedure based on finite element modeling and trust region constrained optimization. No assumption about boundary conditions is needed. The optimization procedure is global, taking advantage of all available images. The algorithm was tested on phantom, as well as on real clinical images.

Assessment of vulnerable plaque composition by matching the deformation of a parametric plaque model to measured plaque deformation

Intravascular ultrasound (IVUS) elastography visualizes local radial strain of arteries in so-called elastograms to detect rupture-prone plaques. However, due to the unknown arterial stress distribution these elastograms cannot be directly interpreted as a morphology and material composition image. To overcome this limitation we have developed a method that reconstructs a Young's modulus image from an elastogram. This method is especially suited for thin-cap fibroatheromas (TCFAs), i.e., plaques with a media region containing a lipid pool covered by a cap. Reconstruction is done by a minimization algorithm that matches the strain image output, calculated with a parametric finite element model (PFEM) representation of a TCFA, to an elastogram by iteratively updating the PFEM geometry and material parameters. These geometry parameters delineate the TCFA media, lipid pool and cap regions by circles. The material parameter for each region is a Young's modulus, EM, EL, and EC, respectively. The method was successfully tested on computer-simulated TCFAs (n = 2), one defined by circles, the other by tracing TCFA histology, and additionally on a physical phantom (n = 1) having a stiff wall (measured EM = 16.8 kPa) with an eccentric soft region (measured EL = 4.2 kPa). Finally, it was applied on human coronary plaques in vitro (n = 1) and in vivo (n = 1). The corresponding simulated and measured elastograms of these plaques showed radial strain values from 0% up to 2% at a pressure differential of 20, 20, 1, 20, and 1 mmHg respectively. The used/reconstructed Young's moduli [kPa] were for the circular plaque EL = 50/66, EM = 1500/1484, EC = 2000/2047, for the traced plaque EL = 25/1, EM = 1000/1148, EC = 1500/1491, for the phantom EL = 4.2/4 kPa, EM = 16.8/16, for the in vitro plaque EL = n.a./29, EM = n.a./647, EC = n.a./1784 kPa and for the in vivo plaque EL = n.a./2, EM = n.a./188, Ec = n.a./188 kPa.

A Hybrid Position/Force Control Approach for Identification of Deformation Models of Skin and Underlying Tissues

In this paper, the focus is on the design of two biomechanical models representing the skin as well as the underlying tissues behavior and properties during a robotized harvesting process. The first model is quasi-static (i.e., without considering velocity) in the pressure direction of the tool: it is principally issued from the work of d'Aulignac et al. and some interesting properties are exhibited from it. The second model is new and takes into account velocity and lubrication in the motion direction of the tool. The goal of this study is to improve skin harvesting process in robotized reconstructive surgery, by automatically selecting the force applied on the donor area and tuning the gain factors of the control law prior to harvesting. It requires extracting relevant parameters such as skin thickness and stiffness, friction coefficient, etc. that characterize the biomechanical properties of the skin and underlying tissues of each patient and of different harvesting surfaces on a given patient (thigh, skull, buttocks, ...). Due to the surgical constraint, the in vivo procedure should be performed in the operating room before starting the operation with the robot itself thanks to a suitable hybrid position/force controller. In this paper, a survey about soft tissue modeling is presented. Mathematical models are discussed along with identification protocols, and two models are chosen that meet our requirements. Finally, experimental results are presented on foam and human skin.