An Enhanced Method for Full-Inversion-Based Ultrasound Elastography of The Liver

Similar to many other types of cancer, liver cancer is associated with biological changes that lead to tissue stiffening. An effective imaging technique that can be used for liver cancer detection through visualizing tissue stiffness is ultrasound elastography. In this thesis, we show the effectiveness of an enhanced method of tissue motion tracking used in quasi-static ultrasound elastography for liver cancer assessment compared to other state of the art methods. The method utilizes initial estimates of axial and lateral displacement fields obtained using conventional time delay estimation (TDE) methods in conjunction with a recently proposed strain refinement algorithm to generate enhanced versions of the axial and lateral strain images. Another primary objective of this work is to investigate the sensitivity of the proposed method to the quality of these initial displacement estimates. The proposed algorithm is founded on the tissue mechanics principles of incompressibility and strain compatibility. Tissue strain images can serve as input for full-inversion-based elasticity image reconstruction algorithm. In this work, we applied strain images generated by the proposed method in conjunction with an iterative elasticity reconstruction algorithm for full-inversion-based liver elastography. Moreover, a set of in-silico experiments were conducted to validate the assumptions used in the reconstruction technique to improve the realism of the method. Ultrasound RF data collected from a tissue-mimicking phantom and from four liver cancer patients who underwent open surgical RF thermal ablation therapy were used to evaluate the proposed method. The results showed that the proposed method produces superior results to other state of the art methods. Moreover, while there is some sensitivity to the displacement field initial estimates, overall, the proposed method is robust to the quality of the initial estimates.