Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction

TL;DR

This paper introduces a novel method for optimizing k-space sampling patterns and temporal feature fusion in multi-echo MRI, significantly enhancing image reconstruction quality for quantitative susceptibility mapping.

Contribution

It proposes a sampling pattern optimization method based on LOUPE-ST and a recurrent temporal feature fusion block integrated into a deep ADMM network, advancing multi-echo MRI reconstruction.

Findings

Improved reconstruction quality in experiments.

Enhanced signal evolution modeling during reconstruction.

Effective optimization of sampling patterns for quantitative imaging.

Abstract

Quantitative imaging in MRI usually involves acquisition and reconstruction of a series of images at multi-echo time points, which possibly requires more scan time and specific reconstruction technique compared to conventional qualitative imaging. In this work, we focus on optimizing the acquisition and reconstruction process of multi-echo gradient echo pulse sequence for quantitative susceptibility mapping as one important quantitative imaging method in MRI. A multi-echo sampling pattern optimization block extended from LOUPE-ST is proposed to optimize the k-space sampling patterns along echoes. Besides, a recurrent temporal feature fusion block is proposed and inserted into a backbone deep ADMM network to capture the signal evolution along echo time during reconstruction. Experiments show that both blocks help improve multi-echo image reconstruction performance.