Multi-contrast CS reconstruction using data-driven and model-based deep neural networks

The use of deep learning (DL) for compressed sensing (CS) have recently received increased attention. Generally, DL-CS uses single-contrast CS reconstruction (SCCS) where the single-contrast image is used as the network input. However, in clinical routine examinations, different contrast images are acquired in the same session, and CS reconstruction using multi-contrast images as the input (MCCS) has the potential to show better performance. Here, we applied DL-MCCS to brain MRI images acquired during routine examinations. We trained data-driven and model-based networks, and showed that for both cases, MCCS outperformed SCCS.