A novel tissue mechanics‐based method for improved motion tracking in quasi‐static ultrasound elastography

Abstract

Purpose Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasi‐static ultrasound elastography (USE) can potentially be effective in cancer diagnosis. USE techniques developed for stiffness image reconstruction use noisy displacement data to obtain the stiffness images. In this study, we propose a technique to substantially improve the accuracy of the displacement data computed through ultrasound tissue motion tracking techniques, especially in the lateral direction. Methods The proposed technique uses mathematical constraints derived from fundamental tissue mechanics principles to regularize displacement and strain fields obtained using Global Ultrasound Elastography (GLUE) and Second‐Order Ultrasound Elastography (SOUL) methods. The principles include a novel technique to enforce (1) tissue incompressibility using 3D Boussinesq model and (2) deformation compatibility using the compatibility differential equation. The technique was validated thoroughly using metrics pertaining to Signal‐to‐Noise‐Ratio (SNR), Contrast‐to‐Noise‐Ratio (CNR) and Normalized Cross Correlation (NCC) for four tissue‐mimicking phantom models and two clinical breast ultrasound elastography cases. Results The results show substantial improvement in the displacement and strain images generated using the proposed technique. The tissue‐mimicking phantom study results indicate that the proposed method is superior in improving image quality compared to the GLUE and SOUL techniques as it shows an average axial strain SNR and CNR improvement of 44% and 63%, and lateral strain SNR and CNR improvement of 130% and 435%, respectively. The results of the phantom study also indicate higher accuracy of displacement images obtained using the proposed technique, including improvement ranges of 7–84% and 26–140% for axial and lateral displacement images, respectively. For the clinical cases, the results indicate average improvement of 48% and 64% in SNR and CNR, respectively, in the axial strain images, and average improvement of 40% and 41% in SNR and CNR, respectively, in the lateral strain images. Conclusion The proposed method is very effective in producing improved estimate of tissue displacement and strain images, especially with the lateral displacement and strain where the improvement is highly remarkable. While the method shows promise for clinical applications, further investigation is necessary for rigorous assessment of the method's performance in the clinic.

Comparative Study of Ultrasound Tissue Motion Tracking Techniques for Effective Breast Ultrasound Elastography

“…Unfortunately, 2D USE introduces complications due to the simplification of a 3D problem into a 2D counterpart, leading to compromise in generated USE image quality. Recently, a 2D tissue motion tracking method was developed which has been shown to produce high quality displacement field [19]. This method starts with an initial rough estimate of the displacement field and applies regularization derived from an approximate 3D tissue mechanical model, leading to accurate estimation of both axial and lateral tissue displacement fields.…”

“…The method presented in [19] has three major steps: Laplacian filtering, incompressibility enforcement, and compatibility enforcement. The first is the simplest: a Laplacian operator is applied to reduce the noise in the displacement image.…”

“…Such development, however, may involve unjustifiably extensive computational processing. One exception to this is the strain refinement algorithm (STREAL), which follows tissue-mechanics-based regularization to improve the estimate of tissue lateral displacements with very high performance and low computational cost [19,20]. This method requires a rough initial estimate of the displacement field that can be obtained using previously developed techniques [7,13,21,22].…”

Alternating direction method of multipliers for displacement estimation in ultrasound strain elastography

“…Second‐order regularization has also been incorporated in ref. [53] as a post‐processing step to refine the estimated displacement fields. Another work 54 has penalized the second‐order displacement derivatives for developing a direct strain estimation framework for vascular elastography.…”

In-Plane Wave Propagation Analysis of Human Breast Lesions Using a Higher-Order Nonlocal Model and Deep Learning

“…It was found that the presence of cancer in human breast tissue leads to a significant increase in the elasticity modulus, which could be clinically beneficial in the detection of breast cancer. Kheirkhah et al [29] used mathematical constraints obtained based on the principles of tissue mechanics for the regularization of displacements and strains in ultrasound elastography, incorporating factors such as tissue incompressibility and deformation compatibility. Poul et al [30] studied different rheological models of viscoelasticity for elastography imaging applications including the Kelvin-Voigt, standard linear solid, and Kelvin-Voigt fractional derivative models.…”