Using a Deep Learning Prior for Accelerating Hyperpolarized 13C Magnetic Resonance Spectroscopic Imaging on Synthetic Cancer Datasets

Motivation: Hyperpolarized (HP) 13C magnetic resonance spectroscopic imaging (MRSI) is efficient and reliable in assessing the aggressiveness of tumors and their response to treatments. Goal(s): To incorporate a deep learning prior with k-space data fidelity for accelerating HP 13C MRSI. Approach: Singular maps were generated from synthetic phantom datasets simulated by two-site exchange models and used to train the deep learning prior, which was then employed with the fidelity term to reconstruct the undersampled k-space data. Results: The proposed method was demonstrated feasibility and generalizability on varied synthetic cancer datasets, and showed improved accuracy in value and location of tumors compared to other methods. Impact: The proposed model could be considered as a general framework that extended the application of deep learning to MRSI reconstruction, which could be applied in varied cancer datasets.