Deep Learning Prediction of Multi-channel ESPIRiT Maps for Calibrationless MR Image Reconstruction

We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained with a hybrid loss function using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. The proposed model robustly predicted ESPIRiT maps from uniformly-undersampled k-space brain and cardiac MR data, yielding highly comparable performance to reconstruction using to acquired reference ESPIRiT maps. Our proposed method presents a general strategy for calibrationless parallel imaging reconstruction through learning from coil and protocol specific data.