Non-invasive imaging of Young’s modulus and Poisson’s ratio in cancers in vivo

Abstract

Objective: Alterations of Young's modulus (YM) and Poisson's ratio (PR) in biological tissues are often early indicators of the onset of pathological conditions. Knowledge of these parameters has been proven to be of great clinical significance for the diagnosis, prognosis and treatment of cancers. Currently, however, there are no non-invasive modalities that can be used to image and quantify these parameters in vivo without assuming incompressibility of the tissue, an assumption that is rarely justified in human tissues. Methods: In this paper, we develop a new method to simultaneously reconstruct YM and PR of a tumor and of its surrounding tissues, irrespective of the boundary conditions and the shape of the tumor based on ellipsoidal approximation. This new non-invasive method allows the generation of high spatial resolution YM and PR maps from axial and lateral strain data obtained via ultrasound elastography. The method was validated using finite element (FE) simulations and controlled experiments performed on phantoms with known mechanical properties. The clinical feasibility of the developed method was also demonstrated in an orthotopic mouse model of breast cancer. Results: Our results from simulations and controlled experiments demonstrate that the proposed reconstruction technique is accurate and robust. Conclusion: Availability of the proposed technique could address the clinical need of a non-invasive modality capable of imaging, quantifying and monitoring the mechanical properties of tumors with high spatial resolution and in real time. Significance: This technique can have a significant impact on the clinical translation of elasticity imaging methods.

Non-Invasive Imaging of Normalized Solid Stress in Cancers in Vivo

“…σ a RR and σ a θθ can be computed using Eshelby's theory by knowledge of the applied stress, geometry of the tumor and Young's modulus and Poisson's ratio of the tumor and background normal tissue [22]. In elastography experiments, we normally assume the axisymmetry of the material and strain components, which allows us to use cylindrical coordinates for analysis and presentation of strain and stress data [27]- [30]. The formulas for computation of the stress components in spherical coordinates from cylindrical coordinates can be found in Appendix A.…”

“…In a creep compression protocol, a constant pressure is applied on the sample. In our experiments, we applied a uniaxial pressure of 1-4 kPa for one minute [27].…”

“…σ c RR and were calculated using the theories developed in [21], [26]. The borders of the cancers were segmented on the in vivo axial strain elastograms [27], as these borders are not always evident in the B-mode images. The Young's modulus and Poisson's ratio of tumors and normal tissues were computed using the method described in [27].…”

A New Poroelastography Method to Assess the Solid Stress Distribution in Cancers

“…Axial and lateral displacement and strain elastograms are generated from the RF data. Since a poroelastic material behaves as a linear elastic solid at steady state (i.e., when the tissue is fully relaxed) [23], the steady state poroelastographic axial and lateral strains can be used to estimate linear elastic parameters, such as the Young's modulus and the Poisson's ratio [24].…”

A Spline Interpolation–based Data Reconstruction Technique for Estimation of Strain Time Constant in Ultrasound Poroelastography

“…The proposed spline interpolation method was tested on experimental data, acquired in a previous study. Details of the in vivo experiments have can be found in Islam et al 26 The experimental data were acquired in the context of a cancer animal study (Houston Methodist Research Institute, ACUC-approved protocol # AUP-0614-0033). As for the simulations, 'bad' frames were identified as those with SNR below 10 dB according to the method proposed by Chandrasekhar et al 27 Figure 1 shows the final element method results obtained from noise-free data, noisy data of 60 dB SNR (no filtering, no interpolation), Kalman-filtered data, and spline-interpolated data, when 75% of the strain frames have high SNR.…”