An iterative hyperelastic parameters reconstruction for breast cancer assessment

Abstract

In breast elastography, breast tissues usually undergo large compressions resulting in significant geometric and structural changes, and consequently nonlinear mechanical behavior. In this study, an elastography technique is presented where parameters characterizing tissue nonlinear behavior is reconstructed. Such parameters can be used for tumor tissue classification. To model the nonlinear behavior, tissues are treated as hyperelastic materials. The proposed technique uses a constrained iterative inversion method to reconstruct the tissue hyperelastic parameters. The reconstruction technique uses a nonlinear finite element (FE) model for solving the forward problem. In this research, we applied Yeoh and Polynomial models to model the tissue hyperelasticity. To mimic the breast geometry, we used a computational phantom, which comprises of a hemisphere connected to a cylinder. This phantom consists of two types of soft tissue to mimic adipose and fibroglandular tissues and a tumor. Simulation results show the feasibility of the proposed method in reconstructing the hyperelastic parameters of the tumor tissue.

Efficient Sensitivity Based Reconstruction Technique to Accomplish Breast Hyperelastic Elastography

“…The inadequacy of the displacement-based techniques, for instance, two methods proposed by Mehrabian and Samani [59–61] and Hajhashemkhani and Hematiyan [17, 18], necessitates the improvement of the tactics or the introduction of novel strategies for the hyperelastic elastography. The main defects of the aforementioned methods (as some of them have been assessed in [62]) are as follows:the dependency of the defined coefficient matrix in the former method to the precise displacement measurements of a great number of adjacent points inside the medium and the reliance of the latter method to the displacement values of some boundary points of the tumor, which might not be accurately extracted from the recorded data, e.g., the US RF signals or images,the necessity to have initial knowledge of the tumor in order to (a) consider proper initial guesses for the hyperelastic parameters to initiate the algorithms and (b) be assured of converging to the main hyperelastic parameters, specifically on account of the defined criteria to stop the algorithms,the requisite to employ appropriate regularization methods and parameters, for example, as indicated by Mehrabian and Samani, the truncated singular value decomposition (SVD), Tikhonov regularization, and Wiener filtering techniques [59–61],…”

Deformable Registration for Longitudinal Breast MRI Screening

“…A numerical simulation study was conducted to simulate deformation of breast tissue between MRI scans at two consecutive visits. A mechanical model of the breast tissue was constructed using finite element (FE) analysis [20][21][22]. The objective of this numerical study was to develop a realistic deformation model that could be used in evaluating registration accuracy and optimizing parameters.…”

Non‐iterative beamforming based on Huygens principle for multistatic ultrawide band radar: application to breast imaging

“…Elastography imaging was used to locate the masses. Elastography is a non‐invasive method in which stiffness or strain images of soft tissues are used to detect or classify tumours [33]. Since of higher stiffness, the tumour deforms less than the surrounding tissue when the sample is stressed either through palpitation on the surface or through applying a medium intensity ultrasound pulse locally [34].…”