Year: 2023
Type: conference-paper
Abstract: In fluorine-19 (19F) cellular MRI, detection of multiple cell targets requires the ability to unmix images corresponding to different tracer molecules with different chemical shifts. The resulting chemical shift artifacts of conventional Cartesian sampling are well-defined, appearing as ‘ghost images’ along the readout direction. However, a key challenge with radial sampling is that frequency offsets lead to nonlinear smearing artifacts throughout the image. Thus, proper modeling of forward sensing operators is crucial, as the successful deconvolution of artifacts relies on the joint design of acquisition scheme and sampling pattern. In this work, we aim to address these perspectives through the lens of radial spectral deconvolution. Our goal is to develop suitable modeling of the radial chemical shift artifacts using Radon transform, which will guide our design of sensing operators that favor specific multi-spectral imaging tasks. To effectively unmix the component images under low SNR regime, we will further exploit physics-informed learning-based unrolling strategies that enable simultaneous artifact removal and weak signal detection, both of particular interest in19F MRI. particular interest in19F MRI. 1
Authors: Jiawen Chen, Eric T. Ahrens, Piya Pal
Institution: University of California San Diego
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G7417287805, EXTREME DATA REDUCTION AND RELIABLE INFORMATION RETRIEVAL BY HARNESSING LOW-DIMENSIONAL STRUCTURES IN HIGH-DIMENSIONAL DATASETS, Metallo-fluorocarbon nanoemulsion for PET detection of cancer inflammation, Imaging tumor-associated macrophages in head and neck squamous cell carcinoma using fluorine-19 MRI
OpenAlex ID: W4393395445