mlsanityData to 7 Oct 2026

LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging

arXiv 2409.07457 ↗First posted 8 Aug 2024eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

Single-shot magnetic resonance (MR) imaging acquires the entire k-space data in a single shot and it has various applications in whole-body imaging. However, the long acquisition time for the entire k-space in single-shot fast spin echo (SSFSE) MR imaging poses a challenge, as it introduces T2-blur in the acquired images. This study aims to enhance the reconstruction quality of SSFSE MR images by (a) optimizing the trajectory for measuring the k-space, (b) acquiring fewer samples to speed up the acquisition process, and (c) reducing the impact of T2-blur. The proposed method adheres to physics constraints due to maximum gradient strength and slew-rate available while optimizing the trajectory within an end-to-end learning framework. Experiments were conducted on publicly available fastMRI multichannel dataset with 8-fold and 16-fold acceleration factors. An experienced radiologist's evaluation on a five-point Likert scale indicates improvements in the reconstruction quality as the ACL fibers are sharper than comparative methods.

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References · 13

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
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  5. 2019Learning-based Optimization of the Under-sampling Pattern in MRIcited by 32
  6. 2021B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRIcited by 29
  7. 2019Self-Supervised Deep Active Accelerated MRIcited by 24
  8. 2019PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRIcited by 24
  9. 2019Learning the Sampling Pattern for MRIcited by 21
  10. 2018Learning-Based Compressive MRIcited by 15
  11. 2018OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRIcited by 11
  12. 2018SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstructioncited by 10
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  1. 2017Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRIcited by 2

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