TOWARDS HIGH RESOLUTION IN STIMULATION, SENSING, AND CHARACTERIZATION OF NEURAL TISSUE – APPLICATIONS IN GIGAHERTZ ULTRASONIC NEURAL INTERFACES AND MAGNETIC RESONANCE IMAGING OF BRAIN TISSUE FOR QUANTITATIVE SUSCEPTIBILITY MAPPING

Abstract

Achieving resolution improvements towards biological and medical applications requires technological advancements in device fabrication, imaging, and computation. This dissertation presents major field advancements in improving resolution in two essential areas. The first involves major advancements in ultrasonic neural interfaces through the development of the Gigahertz (GHz) ultrasonic neural interface. Operating at ultrahigh frequencies of 1.5 GHz allows for lateral resolutions down to 1 micron in aqueous medium, which is the next regime in resolution for ultrasonic neural interfaces. The first reported stimulation of human neural cells with GHz ultrasonics is detailed, and the first co-stimulatory high density electrical and GHz ultrasonic chip-scale devices are fabricated and introduced. Cellular bioeffects of these costimulatory devices operating at GHz frequencies are detailed at the level of chromatin structure and neurite growth characteristics. Further, a high resolution GHz ultrasonic acoustic impedance sensor is characterized for the use of sensing ionic content and flux in both biological and nonbiological medium. The second major set of advancements is in Quantitative Susceptibility Mapping (QSM) algorithm development. QSM allows one to obtain the magnetic susceptibility distribution of tissue from Magnetic Resonance Imaging field input data. Because this inverse deconvolution problem is ill-posed, propagating non-localized artifacts cause poor feature resolution. Algorithm improvements are introduced that utilize a novel spatially adaptive regularization that suppress these artifacts and improve feature resolution in high field 3T MRI clinical datasets with millimeter voxel dimensions. Further, ex vivo human brain tissue is imaged at high field 3T and ultrahigh field 7T MRI at sub-millimeter voxel dimensions. The first algorithm to use multi-contrast information in spatially adaptive regularization is introduced and shown to improve reconstruction in ex vivo human brain specimens. This algorithm is validated using stained tissue sections imaged at sub-micron resolution. Following this, QSM is reconstructed for specimens of the human ex vivo brainstem, and regions of interest are quantified towards one of the first comprehensive characterizations of magnetic susceptibility of the human brainstem. This dissertation introduces major field advancements in both computation through algorithm development and device engineering that improve resolution and introduce new discoveries in both biology and medicine.