Acoustic radiation force impulse imaging (ARFI) on an IVUS circular array.

Our long-term goal is the detection and characterization of vulnerable plaque in the coronary arteries of the heart using intravascular ultrasound (IVUS) catheters. Vulnerable plaque, characterized by a thin fibrous cap and a soft, lipid-rich necrotic core is a precursor to heart attack and stroke. Early detection of such plaques may potentially alter the course of treatment of the patient to prevent ischemic events. We have previously described the characterization of carotid plaques using external linear arrays operating at 9 MHz. In addition, we previously modified circular array IVUS catheters by short-circuiting several neighboring elements to produce fixed beamwidths for intravascular hyperthermia applications. In this paper, we modified Volcano Visions 8.2 French, 9 MHz catheters and Volcano Platinum 3.5 French, 20 MHz catheters by short-circuiting portions of the array for acoustic radiation force impulse imaging (ARFI) applications. The catheters had an effective transmit aperture size of 2 mm and 1.5 mm, respectively. The catheters were connected to a Verasonics scanner and driven with pushing pulses of 180 V p-p to acquire ARFI data from a soft gel phantom with a Young's modulus of 2.9 kPa. The dynamic response of the tissue-mimicking material demonstrates a typical ARFI motion of 1 to 2 microns as the gel phantom displaces away and recovers back to its normal position. The hardware modifications applied to our IVUS catheters mimic potential beamforming modifications that could be implemented on IVUS scanners. Our results demonstrate that the generation of radiation force from IVUS catheters and the development of intravascular ARFI may be feasible.

Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output

Uncertainty quantification (UQ) is both an old and new concept. The current novelty lies in the interactions and synthesis of mathematical models, computer experiments, statistics, field/real experiments, and probability theory, with a particular emphasize on the large-scale simulations by computer models. The challenges not only come from the complication of scientific questions, but also from the size of the information. It is the focus in this thesis to provide statistical models that are scalable to massive data produced in computer experiments and real experiments, through fast and robust statistical inference.

Chapter 2 provides a practical approach for simultaneously emulating/approximating massive number of functions, with the application on hazard quantification of Soufri\`{e}re Hills volcano in Montserrate island. Chapter 3 discusses another problem with massive data, in which the number of observations of a function is large. An exact algorithm that is linear in time is developed for the problem of interpolation of Methylation levels. Chapter 4 and Chapter 5 are both about the robust inference of the models. Chapter 4 provides a new criteria robustness parameter estimation criteria and several ways of inference have been shown to satisfy such criteria. Chapter 5 develops a new prior that satisfies some more criteria and is thus proposed to use in practice.