LINEAR: Learning Implicit Neural Representation With Explicit Physical Priors for Accelerated Quantitative T1rho Mapping

Abstract: Quantitative T1rho 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 subject-specific unsupervised method based on the implicit neural representation is proposed to reconstruct T1rho-weighted images from highly undersampled k-space data, which only takes spatiotemporal coordinates as the input. Specifically, the proposed method learned a implicit neural representation of the MR images driven by two explicit priors from the physical model of T1rho mapping, including the signal relaxation prior and 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 LINEAR 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.