In deep reinforcement learning (DRL), software agents based on deep neural networks are used to explore environments in order to maximise a reward (e.g. score in a video game). Here, DRL was used to control a virtual MRI scanner and actively interpret acquired data. An environment was constructed in which correctly determining the shape of a phantom was rewarded with a high score, and penalised by increasing acquisition time. Following training, the algorithm had learnt to acquire sparse images, assigning TE, TR and flip angles that enabled it to act as an edge detector and deduce shape with 99.8% accuracy.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords