@inproceedings{21949,
  abstract     = {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. Furthermore, most existing methods do not
levarage the physical T1 decay model in optimization. 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, cross-frame 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
experiments and results is available at https://github.com/tamirshor7/T1-PILOT},
  author       = {Shor, Tamir and Freiman, Moti and Baskin, Chaim and Bronstein, Alexander},
  booktitle    = {Medical Imaging with Deep Learning},
  issn         = {2640-3498},
  keywords     = {Cardiac T1 Mapping, Trajectory Optimization and Reconstruction, PhysicsInformed Deep-Learning},
  location     = {Taipei, Taiwan},
  pages        = {1969--1982},
  publisher    = {ML Research Press},
  title        = {{T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration}},
  volume       = {315},
  year         = {2026},
}