LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging
This work addresses image quality issues in whole-body MR imaging for medical applications, representing an incremental improvement over existing methods.
The study tackled the problem of T2-blur and long acquisition times in single-shot MR imaging by optimizing k-space trajectories and reducing samples, resulting in sharper ACL fibers as evaluated by a radiologist with 8-fold and 16-fold acceleration factors.
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.