Accelerated Dynamic Magnetic Resonance Imaging from Spatial-Subspace Reconstructions (SPARS)

TL;DR

This paper introduces SPARS, a novel MRI reconstruction method that achieves high spatial and temporal resolution from minimal data, outperforming existing techniques in accuracy and speed.

Contribution

The paper presents the concept of spatial subspace reconstructions (SPARS), enabling high-resolution dynamic MRI from as little as one radial spoke per frame.

Findings

SPARS outperforms GRASP and GRASP-Pro in accuracy.

SPARS achieves faster reconstruction times.

SPARS maintains high spatial and temporal resolution with minimal data.

Abstract

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) ideally requires a high spatial and high temporal resolution, but hardware limitations prevent acquisitions from simultaneously achieving both. Existing image reconstruction techniques can artificially create spatial resolution at a given temporal resolution by estimating data that is not acquired, but, ultimately, spatial details are sacrificed at very high acceleration rates. The purpose of this paper is to introduce the concept of spatial subspace reconstructions (SPARS) and demonstrate its ability to reconstruct high spatial resolution dynamic images from as few as one acquired radial spoke per dynamic frame. Briefly, a low-temporal-high-spatial resolution organization of the acquired raw data is used to estimate a spatial subspace in which the high-temporal-high-spatial ground truth data resides. This subspace is then used to estimate entire images from single k-space spokes. In both simulated and human in-vivo data, the proposed SPARS reconstruction method outperformed standard GRASP and GRASP-Pro reconstruction, providing a shorter reconstruction time and yielding higher accuracy from both a spatial and temporal perspective.