Doctor of Philosophy, Case Western Reserve University, 2024, EECS - Electrical Engineering

Quantitative Magnetic Resonance Imaging (qMRI) is a powerful tool for detecting biochemical abnormalities without harmful ionizing radiation and invasive procedure, which can significantly enhance early disease diagnosis and progression monitoring compared to standard morphological MRI. Osteoarthritis is a disease significantly impacting joint function, mobility, and quality of life, often leading to chronic pain and reduced physical activity. Despite the significant impact, the disease lacks sensitive biomarker that can detect and track the disease progression from an early stage which can help improve patient outcomes and develop effective treatment for the disease. qMRI, due to its sensitivity to biochemical properties, provide multiple candidates that may serve as imaging biomarkers for OA. Among them, T2 and T1ρ have advantage of not requiring special coil nor contrast agent. However, challenges need to be addressed to expand the accessibility of the technique and successful translation to be used in large scale clinical trials and in clinical practice. In this study, novel quantitative T2 and T1ρ acquisition techniques were developed to enhance reliability and enable faster acquisition.

Committee: Xiaojuan Li (Advisor); Mark Griswold (Committee Member); Xin Yu (Committee Member); Cenk Cavusoglu (Committee Chair) Subjects: Biomedical Engineering; Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2016, Biomedical Engineering

The research presented in this work seeks to develop, validate, and deploy practical techniques for improving diagnosis of cardiovascular disease. In the philosophy of biomedical engineering, we seek to identify an existing medical problem having significant societal and economic effects and address this problem using engineering approaches. Cardiovascular disease is the leading cause of mortality in the United States, accounting for more deaths than any other major cause of death in every year since 1900 with the exception of the year 1918. Cardiovascular disease is estimated to account for almost one-third of all deaths in the United States, with more than 2150 deaths each day, or roughly 1 death every 40 seconds. In the past several decades, a growing array of imaging modalities have proven useful in aiding the diagnosis and evaluation of cardiovascular disease, including computed tomography, single photon emission computed tomography, and echocardiography. In particular, cardiac magnetic resonance imaging is an excellent diagnostic tool that can provide within a single exam a high quality evaluation of cardiac function, blood flow, perfusion, viability, and edema without the use of ionizing radiation. The scope of this work focuses on the application of engineering techniques for improving imaging using cardiac magnetic resonance with the goal of improving the utility of this powerful imaging modality. Dynamic cine imaging, or the capturing of movies of a single slice or volume within the heart or great vessel region, is used in nearly every cardiac magnetic resonance imaging exam, and adequate evaluation of cardiac function and morphology for diagnosis and evaluation of cardiovascular disease depends heavily on both the spatial and temporal resolution as well as the image quality of the reconstruction cine images. This work focuses primarily on image reconstruction techniques utilized in cine imaging; however, the techniques discussed are also relevant t (open full item for complete abstract)

Committee: Orlando P. Simonetti PhD (Advisor); Lee C. Potter PhD (Committee Member); Rizwan Ahmad PhD (Committee Member); Jun Liu PhD (Committee Member) Subjects: Applied Mathematics; Electrical Engineering; Health; Health Care; Medical Imaging; Medicine; Radiology; Scientific Imaging