Scan-Specific Unsupervised Highly Accelerated Non-Cartesian CEST Imaging Using Implicit Neural Representation and Explicit Sparse Prior

Objective: Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique. CEST imaging usually requires a long scan time, and reducing acquisition time is highly desirable for clinical applications. Methods: A novel scan-specific unsupervised deep learning algorithm is proposed to accelerate steady-state pulsed CEST imaging with golden-angle stack-of-stars trajectory using hybrid-feature hash encoding implicit neural representation. Additionally, imaging quality is further improved by using the explicit prior knowledge of low rank and weighted joint sparsity in the spatial and Z-spectral domain of CEST data. Results: In the retrospective acceleration experiment, the proposed method outperforms other state-of-the-art algorithms (TDDIP, LRTES, kt-SLR, NeRP, CRNN, and PBCS) for the in vivo human brain dataset under various acceleration rates. In the prospective acceleration experiment, the proposed algorithm can still obtain results close to the fully-sampled images. Conclusion and Significance: The hybrid-feature hash encoding implicit neural representation combined with explicit sparse prior (INRESP) can efficiently accelerate CEST imaging. The proposed algorithm achieves reduced error and improved image quality compared to several state-of-the-art algorithms at relatively high acceleration factors. The superior performance and the training database-free characteristic make the proposed algorithm promising for accelerating CEST imaging in various applications.