Physics-guided Self-supervised Implicit Neural Representation For Accelerated T1ρ Mapping.

Quantitative T1ρ mapping has shown promise in clinical and research studies. However, it suffers from long scan times. Deep learning-based techniques have been successfully applied in accelerated quantitative MR parameter mapping. However, most methods require fully-sampled training dataset, which is impractical in the clinic. In this study, a novel scan-specific self-supervised method based on the implicit neural representation is proposed to reconstruct T1ρ-weighted images and generate T1ρ map from highly undersampled k-space data, which only takes spatiotemporal coordinates as the input. Specifically, the proposed method learns an implicit neural representation of the MR images guided by the physical model of T1ρ mapping and two explicit priors: the signal relaxation prior and the self-consistency of k-t space data prior. The proposed method was verified using both retrospective and prospective undersampled k-space data. Experiment results demonstrate that it achieves a high acceleration factor up to 14, and outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error.