Spatiotemporal Analysis of Functional Dynamic Imaging Data
By: Cyrus Bobak Amoozegar
Publisher: [publisher not identified]
Published: 2014
Place of publication: [New York, N.Y.?]
Language: eng
Description: Technological advances in image acquisition speeds and new contrast agents, in both clinical and basic research settings, have enabled entirely new approaches to functional imaging in living systems. Analysis of dynamic and multidimensional data requires very different approaches to the classical segmentation and visualization tools developed for purely structural or anatomical imaging. This thesis details the development of two different spatiotemporal analysis approaches for high-speed in-vivo dynamic optical imaging. Optical imaging is a diverse, versatile, and generally inexpensive modality that can take advantage of a wide range of endogenous and exogenous sources of optical contrast within living tissue. While light scattering can limit resolution and sensitivity of imaging in deeper tissues, optical imaging is well suited for small animal studies where it can be used for studies of physiology and disease processes, for pharmaceutical development and as a test-bed for translation to clinical applications.

In the first part of this work, we present and apply spatiotemporal analysis techniques which we define as `dynamic contrast enhancement' methods. We apply these methods to in-vivo whole body small animal molecular optical imaging to demonstrate that dynamic analysis can be used for longitudinal assessment of organ function. We then demonstrate the equivalence of our approach to dynamic contrast enhanced magnetic resonance imaging. This optical technique could allow for better informed drug development and longitudinal toxicity evaluation. This technique could also serve as a platform for the development of functional imaging methods using dynamic MRI. We then apply spatiotemporal analysis techniques to high speed optical hemodynamic imaging data acquired on the exposed rodent cortex. The purpose of this work is to develop a mechanistically-based spatiotemporal model of neurovascular coupling, in order to better understand the basis of functional magnetic resonance imaging data in the human brain.

Our results also provide new insights into potential links between neurovascular disruption and disease pathophysiology in the brain.
Notes: Department: Biomedical Engineering.

Thesis advisor: Elizabeth M.C. Hillman.

Thesis (Ph.D.)--Columbia University, 2014.