LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping

TL;DR

LARO is a novel framework that optimizes both the sampling pattern and reconstruction process to accelerate quantitative susceptibility mapping, reducing scan time while maintaining high image quality.

Contribution

The paper introduces a new deep learning-based framework that jointly optimizes acquisition sampling and reconstruction for faster QSM imaging.

Findings

Optimized sampling pattern improves image quality.

Recurrent feature fusion enhances signal redundancy capture.

Framework generalizes well across different pathologies.

Abstract

Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM. Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Next, this optimized sampling pattern was implemented in an mGRE sequence using Cartesian fan-beam k-space segmenting and ordering for prospective scans. Furthermore, we propose to insert a recurrent temporal feature fusion module into the reconstruction network to capture signal redundancies along echo time. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions. Generalization experiments show that LARO is robust on the test data with new pathologies and different sequence parameters. Our code is available at https://github.com/Jinwei1209/LARO.git.