T1-PILOT: Optimized Trajectories for T1 Mapping Acceleration

TL;DR

T1-PILOT introduces an end-to-end framework that optimizes sampling trajectories for cardiac T1 mapping by integrating the T1 relaxation model, resulting in higher fidelity images and faster scans compared to traditional methods.

Contribution

The paper presents a novel method that jointly optimizes sampling trajectories and reconstruction using the T1 relaxation model, surpassing static and heuristic sampling schemes.

Findings

Significantly improved T1 map fidelity at higher acceleration factors.

Consistent gains in PSNR and VIF over baseline methods.

Enhanced delineation of myocardial structures.

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. 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.