Spatio-Temporal Reconstruction Neural Network for 3D MR fingerprinting of the Prostate

Magnetic Resonance Fingerprinting (MRF) is a technology that computes T1, T2 parameters from time-evaluated signals. However, long scanning time in obtaining fully-sampled data is a challenging point while reducing the sampling rate results in poor reconstructed data quality. Here, we propose a spatio-temporal deep learning network for reconstruction from the under-sampled MRF data. According to the retrospective reconstructed results, the proposed method could produce the T1 and T2 maps of high fidelity similar to the fully-sampled ground-truth.