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. Code for reproducing all results will be made publicly available upon publication.

翻译：心脏T1映射为心肌组织成分提供了关键的定量信息，使得评估纤维化、炎症和水肿等病理变化成为可能。然而，心脏固有的动态特性对采集时间施加了严格限制，使得高分辨率T1映射成为一个持续的挑战。压缩感知方法通过欠采样k空间并从部分数据重建图像，缩短了扫描时间；近期研究表明，将欠采样模式与重建网络联合优化可显著提升性能。尽管如此，当前大多数T1映射流程仍依赖于静态、手工设计的采样掩码，未能充分利用加速与精度的潜力。本研究提出T1-PILOT：一种端到端方法，将T1信号弛豫模型显式地整合到采样-重建框架中，以指导非笛卡尔轨迹学习、跨帧对齐和T1衰减估计。通过在CMRxRecon数据集上的大量实验，T1-PILOT显著优于多种基线策略（包括学习的单掩码方案以及固定的径向或黄金角采样方案），在更高加速因子下实现了更优的T1图保真度。特别地，我们观察到相较于现有方法，PSNR和VIF指标持续提升，同时心肌细微结构的描绘能力显著改善。我们的结果表明，结合物理弛豫模型优化采样轨迹，既能提高定量精度，又能缩短采集时间。所有结果的复现代码将在论文发表后公开提供。