64 research outputs found
LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping
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
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