Abstract #1168

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

Maarten Terpstra^1,2, Sjors Verschuren^1,2, Tom Bruijnen^1,2, Matteo Maspero^1,2, and Cornelis van den Berg^1,2

¹Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands, ²Computational Imaging Group for MR Diagnostics & Therapy, University Medical Center Utrecht, Utrecht, Netherlands

Synopsis

Keywords: Pulse Sequence Design, Machine Learning/Artificial Intelligence, Autonomous sequence optimizationFor 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.

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