T1-PILOT：优化T1映射加速的轨迹
T1-PILOT: Optimized Trajectories for T1 Mapping Acceleration
AI总结：
T1-PILOT通过整合T1信号弛豫模型优化采样轨迹，提升T1映射的精度与加速能力。
摘要：
心脏T1映射为心肌组织成分提供了关键的定量洞察，能够评估纤维化、炎症和水肿等病理情况。然而，心脏固有动态性质对采集时间提出了严格限制，使高分辨率T1映射成为一个持续挑战。压缩感知（CS）方法通过欠采样k空间并从部分数据中重建图像来减少扫描时间，最近研究表明将欠采样模式与重建网络联合优化可显著提高性能。然而，大多数当前T1映射流程仍依赖于静态的手工设计掩码，未能充分利用加速和精度潜力。在本文中，我们引入T1-PILOT：一种端到端方法，明确将T1信号弛豫模型纳入采样-重建框架中，以指导非笛卡尔轨迹的学习、跨帧对齐和T1衰减估计。通过在CMRxRecon数据集上的广泛实验，T1-PILOT在多个基线策略（包括学习单掩码和固定径向或黄金角采样方案）中显著优于，实现更高的T1图保真度和更大的加速因子。特别是，我们观察到在现有方法中PSNR和VIF有持续提升，同时在区分更精细的心肌结构方面有显著改进。我们的结果表明，将采样轨迹与物理弛豫模型联合优化可提高定量准确性并减少采集时间。
英文摘要：
Cardiac T1 mapping provides critical quantitative insights into myocardial tissue composition, enabling the assessment of pathologies such as fibrosis, inflammation, and edema. However, the inherently dynamic nature of the heart imposes strict limits on acquisition times, making high-resolution T1 mapping a persistent challenge. Compressed sensing (CS) approaches have reduced scan durations by undersampling k-space and reconstructing images from partial data, and recent studies show that jointly optimizing the undersampling patterns with the reconstruction network can substantially improve performance. Still, most current T1 mapping pipelines rely on static, hand-crafted masks that do not exploit the full acceleration and accuracy potential. In this work, we introduce T1-PILOT: an end-to-end method that explicitly incorporates the T1 signal relaxation model into the sampling-reconstruction framework to guide the learning of non-Cartesian trajectories, crossframe alignment, and T1 decay estimation. Through extensive experiments on the CMRxRecon dataset, T1-PILOT significantly outperforms several baseline strategies (including learned single-mask and fixed radial or golden-angle sampling schemes), achieving higher T1 map fidelity at greater acceleration factors. In particular, we observe consistent gains in PSNR and VIF relative to existing methods, along with marked improvements in delineating finer myocardial structures. Our results highlight that optimizing sampling trajectories in tandem with the physical relaxation model leads to both enhanced quantitative accuracy and reduced acquisition times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
