LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging
<|reference_start|>LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging: 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.<|reference_end|>
@article{aggarwal2024lsst:,
    title={LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR
  Imaging},
    author={Hemant Kumar Aggarwal, Sudhanya Chatterjee, Dattesh Shanbhag, Uday
  Patil, K.V.S. Hari},
    journal={arXiv preprint arXiv:2409.07457},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07457},
    primaryClass={eess.IV cs.AI cs.CV}
}