Abstract

One of the main limitations of ultrasound imaging is that image quality and interpretation depend on the skill of the user and the experience of the clinician. Quantitative ultrasound (QUS) methods provide objective, system-independent estimates of tissue properties such as acoustic attenuation and backscattering properties of tissue, which are valuable as objective tools for both diagnosis and intervention. Accurate and precise estimation of these properties requires correct compensation for intervening tissue attenuation. Prior attempts to estimate intervening-tissues attenuation based on minimizing cost functions that compared backscattered echo data to models have resulted in limited precision and accuracy. The first contribution of this thesis is that we incorporate the prior information of piece-wise continuity of QUS parameters as an L2 norm regularization term into our cost function to overcome these limitations. We further propose to calculate this cost function using Dynamic Programming (DP), a computationally efficient optimization algorithm that finds the global optimum. Our results on tissue-mimicking phantoms show that DP substantially outperforms a state-of-the-art method in terms of both estimation bias and variance.

The second contribution of this thesis is that to further improve the accuracy and precision of this DP method, we propose to use L1 norm instead of L2 norm as the regularization term in our cost function and optimize the function using DP. Our results show that DP with L1 regularization reduces bias of attenuation and backscatter parameters even further compared to DP with L2 norm. Besides, we employ DP to estimate the QUS parameters of a new phantom with large scatterer size and compare the results of LSq, L2 norm DP and L1 norm DP. Our results show that L1 norm DP outperforms L2 norm DP, which itself outperforms LSq. In the future, the contributions of this thesis can potentially be used for finding imaging biomarkers associated with different types of pathology and help clinicians obtain an objective assessment of intrinsic tissue properties.