T1-PILOT: Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration

Tamir Shor, Moti Freiman, Chaim Baskin, Alex M. Bronstein

Keywords: Cardiac T1 Mapping, Trajectory Optimization and Reconstruction, Physics Informed Deep-Learning.

TL;DR: Model for T1 Mapping acceleration based on physical decay model and per-sample learned acquisition.

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.

Primary Subject Area: Image Acquisition and Reconstruction

Secondary Subject Area: Application: Radiology

Registration Requirement: Yes

Reproducibility: https://github.com/tamirshor7/T1-PILOT

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

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Submission Number: 152