Abstract

Ultrasound is the second most frequently used medical imaging modality that is inexpensive, non-invasive, portable, and fast. As a real-time imaging system, ultrasound can also be used for tracking motion patterns to complement anatomical images. Temporal tracking of tissue motion, a non-trivial task, plays a pivotal role in many diagnostic applications of ultrasound, such as elastography and super-resolution ultrasound. Elastography is a non-invasive medical imaging technique that estimates tissue elastic properties to detect abnormalities in an organ. Ultrasound radio-frequency (RF) data can be tracked to compute tissue strain (which is a surrogate for elasticity) using energy-based algorithms. A continuity constraint along with the data similarity is imposed to obtain a unique solution to the displacement estimation problem. Existing energy-based methods consider only amplitude similarity to formulate the data function, which makes the displacement estimation process sensitive to outliers. In addition, they exploit the L2-norm of the first-order spatial derivative of the displacement field to construct the regularizer. This regularization scheme is not entirely consistent with the mechanics of tissue deformation while perturbed by an external force. Moreover, the L2-norm often over-penalizes the displacement discontinuity. Consequently, state-of-the-art techniques estimate noisy strain images with low target-background contrast and blurry inclusion edges. Another well known limitation of the existing displacement tracking techniques is their poor lateral estimation ability.

Ultrasound localization microscopy (ULM) is a promising medical imaging modality that systematically leverages the advantages of contrast-enhanced ultrasound (CEUS) to surpass the diffraction barrier and delineate the microvascular map. Localization and tracking of intravenously injected microbubble (MB) contrast agents, two significant steps of ULM, facilitate generating the vascular map and the velocity distribution, respectively. The existing MB tracking algorithms predominantly incorporate template-matching and bipartite graph-based cost table minimization, disregarding the immense potential of an energy-based analytic framework.

Herein, we propose six novel energy-based strain imaging techniques (Chapters 2 to 6) and one analytic optimization-based MB tracking algorithm (Chapter 7) to resolve the aforementioned issues of the existing techniques. All seven algorithms developed herein exhibit promising performance in synthetic and real experiments.