Motivation: Magnetic resonance T₁ mapping provides critical insights into tissue properties for early disease detection, but its clinical use is hindered by long scan times needed for acquiring multiple T₁-weighted images. Goal (s): This study proposes an unsupervised implicit neural representation (INR) framework for precise T₁ map generation. Approach: A subject-specific unsupervised method that learns an implicit neural representation of the T₁-weighted images, simultaneously capturing the relationships among T₁-weighted images and multi-channel k-space data. Results: LINEAR achieves 14-fold acceleration with high accuracy in T₁ map generation, outperforming state-of-the-art unsupervised and traditional methods in artifact suppression and error reduction. Impact: This study enables accelerated, high-quality T₁ mapping, improving diagnostic efficiency and providing a foundation for future advancements in rapid quantitative imaging, with potential applications across diverse clinical and research fields.
Xie et al. (Tue,) studied this question.