Quantitative Neurologic and
|Date||Wednesday March 19, 2014 at 1:00 PM|
|Location||24-132 Center for the Health Sciences, Morton Conference Room|
|Speaker||Moses Wilks, Doctoral Graduate Student, Department of Biomathematics|
|Sponsoring Dept||UCLA Biomathematics|
|Abstract||Positron Emission Tomography (PET) is an inherently quantitative
tool for measuring in vivo biological phenomena. However, there are still many
barriers, both practical and structural, to robust quantifi cation of data in clinical
and pre-clinical settings.
First, I present methods for improving quantifi cation of neurologic PET in Alzheimer’s disease imaging. Due to the variability in patient anatomy and disease state, it is diffi cult to accurately compare homologous structures between subjects. Here we examine methods of image normalization and automatic image analysis that allow for greatly reduced variance in data measurement. We show that through these methods, both the diagnostic and prognostic utility of the data can be greatly improved.
Additionally, we address the structural barriers to quantifi cation in oncologic PET in radio-labeled custom antibodies. These large, high-affi nity, tracers have been shown, both in silico and in vivo, to display high degrees of heterogeneous binding in target tissues. Due to this phenomenon, classical ODE models of tracer kinetics are no longer valid. We develop and test a new set of non-linear PDE models to accurately represent tracer activity in vivo. We show that the use of classical ODE models will result in high levels of parameter estimate bias, and the new PDE models can accurately fi t both in silico and in vivo data with the inclusion of Bayesian priors.
Doctoral Committee: Henry Huang, D.Sc. (Chair), Jorge R. Barrio, Ph.D., Elliot M. Landaw, M.D., Ph.D., Kenneth Lange, Ph.D., Anna M. Wu, Ph.D.