Fast 3D Variable-FOV Reconstruction for Parallel Imaging with Localized Sensitivities

TL;DR

This paper introduces a novel 3D variable-FOV reconstruction method for parallel imaging that improves artifact suppression and noise conditioning, enabling faster and higher-quality reconstructions for both Cartesian and non-Cartesian data.

Contribution

It proposes a generalized variable-FOV PILS reconstruction approach that separates k-space into annuli based on sampling density, enhancing reconstruction quality and speed.

Findings

Better artifact suppression than gridding and PILS

Improved noise conditioning over ESPIRiT

Faster, high-quality 3D reconstructions

Abstract

Several successful iterative approaches have recently been proposed for parallel-imaging reconstructions of variable-density (VD) acquisitions, but they often induce substantial computational burden for non-Cartesian data. Here we propose a generalized variable-FOV PILS reconstruction 3D VD Cartesian and non-Cartesian data. The proposed method separates k-space into non-intersecting annuli based on sampling density, and sets the 3D reconstruction FOV for each annulus based on the respective sampling density. The variable-FOV method is compared against conventional gridding, PILS, and ESPIRiT reconstructions. Results indicate that the proposed method yields better artifact suppression compared to gridding and PILS, and improves noise conditioning relative to ESPIRiT, enabling fast and high-quality reconstructions of 3D datasets.