Physical Sciences and Mathematics Commons^™

Positron Emission Tomography In Oncology And Environmental Science, Samantha Delaney

Dissertations, Theses, and Capstone Projects

The last half century has played witness to the onset of molecular imaging for the clinical assessment of physiological targets. While several medical imaging modalities allow for the visualization of the functional and anatomical properties of humans and living systems, few offer accurate quantitation and the ability to detect biochemical processes with low-administered drug mass doses. This limits how physicians and scientists may diagnose and treat medical issues, such as cancer, disease, and foreign agents.

A promising alternative to extant invasive procedures and suboptimal imaging modalities to assess the nature of a biological environment is the use of positron emission …

The Development Of Novel Radioimmunoconjugates For The Pet Imaging And Radioimmunotherapy Of Cancer, Samantha M. Sarrett

Dissertations, Theses, and Capstone Projects

Antibodies have long played a vital role in nuclear medicine for both the diagnosis and therapy of various malignancies. The role and development of antibodies in nuclear medicine can be broadly separated into three different categories: 1) bioconjugation strategies, 2) immunoPET imaging, and 3) radioimmunotherapy. This dissertation will attempt to comprehensively cover each of these categories through a series of studies, protocols, and reviews. For the bioconjugation strategies, we will describe the development of a novel site-selective bioconjugation strategy using an innovative lysine-targeting reagent, PFP-bisN₃, to prepare [⁸⁹Zr]Zr-^SSKDFO-pertuzumab for visualizing HER2+ breast cancer. Further, …

Leveraging Antibodies For Positron Emission Tomography And Near-Infrared Fluorescence Imaging Of Cancer, Kimberly C. Fung

Dissertations, Theses, and Capstone Projects

The high specificity and affinity of antibodies make them attractive for developing drugs to diagnose and treat cancer. The overarching goal of this work is to explore the synthesis and use of antibody-based imaging agents in preclinical models of cancer. This work can be described as two-fold. In the first part, we investigated how the use of a glycans-specific bioconjugation strategy affects Fc gamma RI binding and why it results in improved in vivo performance of immunoconjugates. To do so, we used the clinically relevant positron emission tomography (PET) imaging agent, ⁸⁹Zr-DFO-pertuzumab, in mouse models of human breast cancer. …

A Robust Deep Model For Improved Classification Of Ad/Mci Patients, Feng Li, Loc Tran, Kim-Han Thung, Shuiwang Ji, Dinggang Shen, Jiang Li

Electrical & Computer Engineering Faculty Publications

Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of a particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight coadaptation, which is a typical cause of overfitting in deep learning. …

Functional Generalized Linear Models With Images As Predictors, Philip T. Reiss, R. Todd Ogden

Philip T. Reiss

Functional principal component regression (FPCR) is a promising new method for regressing scalar outcomes on functional predictors. In this paper we present a theoretical justification for the use of principal components in functional regression. FPCR is then extended in two directions: from linear to the generalized linear modeling, and from univariate signal predictors to high-resolution image predictors. We show how to implement the method efficiently by adapting generalized additive model technology to the functional regression context. A technique is proposed for estimating simultaneous confidence bands for the coefficient function; in the neuroimaging setting, this yields a novel means to identify …