Feasibility of automatic patient-specific sequence optimization with deep reinforcement learning

For MRI-guided interventions, tumor contrast and visibility are crucial. However, the tumor tissue parameters can significantly vary among subjects, with a range of T1, T2, and proton density values that may cause sub-optimal image quality when scanning with population-optimized protocols. Patient-specific sequence optimization could significantly increase image quality, but manual parameter optimization is infeasible due to the high number of parameters. Here, we propose to perform automatic patient-specific sequence optimization by applying deep reinforcement learning and reaching near-optimal SNR and CNR with minimal additional acquisitions.