UC San Diego

Fluorine-19 MRI is an emerging technique offering specific detection of labeled cells in vivo. Lengthy acquisition times and modest signal-to-noise ratio (SNR) makes 3D spin-density weighted 19F imaging challenging. Importantly, recent innovations in molecular 19F tracer design and image acquisition-reconstruction methods have achieved significant leaps in 19F MRI sensitivity, and integration of these new materials and methods into studies can result in > 10-fold improvement in detection sensitivity. These developments may help unlock the full potential of clinical 19F MRI for cellular imaging applications, under the considerations of systematic measurement-algorithmic signal processing pillar.

The overarching goal of this dissertation is to investigate joint acquisition-reconstruction imaging techniques with enhanced detection through the lens of 19F cellular MRI. This in- cludes theoretical signal processing development perspective, pulse sequence programming, and reconstruction software workflow implementation on the MRI scanner. We start with the introduction of sensitivity analysis dependent on the LOD and theoretical MR signal modeling. Given that 19F-MRI generally involves the detection and localization of low concentrations of 19F-containing compounds, many signal averages are required to achieve acceptable SNR. Since sensitivity is governed by SNR/time, we focus on integrating spin physics and/or chemical domain knowledge into the systematic pulse sequence and reconstruction algorithm co-design for accelerated 19F MRI. To further enhance the sensitivity of paramagnetic metallo-PFC (MPFC), we describe a compressed sensing scheme, implemented using a novel ultrafast 3D radial pulse sequence, with data reconstructed via a hand-crafted sparsity-promoting algorithm. Combined with recent innovations in physics-driven deep learning, we exploit deep unrolling of radial chemical shift deconvolution arisen from 19F multiple cell targets detection tasks. Unlike typical 1H water-fat separation, large chemical frequency shifts in 19F cellular MRI provides new avenue to model the chemcial offsets and enable artifact-free separation under adverse data scenarios. Finally, we explore generative priors for a preliminary study in cellular MRI detection under high compression and low SNR regime and shed light on future research directions involving active sensing and self-supervised reconstruction for cellular MRI applications. All the techniques studied in this dissertation can be readily generalize to similar X-nuclei applications such as 23Na without any particular restrictions.