Learning Implicit Neural Representation with Explicit Physical Priors for Accelerated Quantitative $$$\text{T}_{1\rho}$$$ Mapping

Motivation: Magnetic resonance $$$\text {T}_{1\rho}$$$ 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 $$$\text {T}_{1\rho}$$$-weighted images. Goal(s): This study proposes an unsupervised implicit neural representation (INR) framework for precise $$$\text{T}_{1\rho}$$$ map generation. Approach: A subject-specific unsupervised method that learns an implicit neural representation of the $$$\text {T}_{1\rho}$$$-weighted images, simultaneously capturing the relationships among $$$\text {T}_{1\rho}$$$-weighted images and multi-channel $$$k$$$-space data. Results: LINEAR achieves 14-fold acceleration with high accuracy in $$$\text {T}_{1\rho}$$$ map generation, outperforming state-of-the-art unsupervised and traditional methods in artifact suppression and error reduction. Impact: This study enables accelerated, high-quality $$$\text {T}_{1\rho}$$$ mapping, improving diagnostic efficiency and providing a foundation for future advancements in rapid quantitative imaging, with potential applications across diverse clinical and research fields.