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 chem...Show MoreMetadata
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 in 19F MRI. particular interest in 19 F MRI. 1
Date of Conference: 29 October 2023 - 01 November 2023
Date Added to IEEE Xplore: 01 April 2024
ISBN Information: