mlsanityData to 14 Oct 2026

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

arXiv 2211.00725 ↗First posted 1 Nov 2022eess.IV cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 18 of its 58 references that are in the map. First posted on arXiv 1 Nov 2022.

Builds on

18 of 58 references.

References · 18

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6 more
  1. 2018OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRIcited by 11
  2. 2020Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentationcited by 9
  3. 2021Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Networkcited by 8
  4. 2020Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility Mappingcited by 4
  5. 2021Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstructioncited by 4
  6. 2020Probabilistic Dipole Inversion for Adaptive Quantitative Susceptibility Mappingcited by 3

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Cited by year

Citing papers per year: 2023: 3; 2024: 0; 2025: 3.

6 papers.

  1. 2023DeDA: Deep Directed Accumulatorcited by 5
  2. 2025Unsupervised Deformable Image Registration with Structural Nonparametric Smoothingcited by 3
  3. 2025VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT Registrationcited by 2
  4. 2025UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentationcited by 2
  5. 2023High-pass filtered fidelity-imposed network edit (HP-FINE) for robust quantitative susceptibility mapping from high-pass filtered phasecited by 0
  6. 2023RimSet: Quantitatively Identifying and Characterizing Chronic Active Multiple Sclerosis Lesion on Quantitative Susceptibility Mapscited by 0