优化轨迹与采样，提升单次采集MRI成像质量。
LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging
- 基于物理约束的端到端学习优化采集轨迹。
- 在8倍和16倍加速下，图像清晰度显著优于对比方法。
- 适合需要快速高质量MRI重建的临床应用。
单次采集磁共振（MR）成像在一次激发中获取整个k空间数据，适用于全身成像。然而，单次快速自旋回波（SSFSE）MR成像中完整k空间的长时间采集会引入T2模糊，影响图像质量。本研究通过（a）优化k空间采集轨迹，（b）减少采样点数以加快采集速度，（c）降低T2模糊影响，来提升SSFSE图像重建质量。所提方法在最大梯度强度和变化率的物理约束下，采用端到端学习框架进行轨迹优化。在公开的fastMRI多通道数据集上，分别以8倍和16倍加速因子进行实验。放射科医生在五级李克特量表上的评估显示，该方法重建图像中前交叉韧带（ACL）纤维更清晰，优于对比方法。
原文摘要 · Abstract (English)
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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