Control of a simulated MRI scanner with deep reinforcement learning
Simon Walker-Samuel
Magnetic resonance imaging (MRI) is a highly versatile and widely used
clinical imaging tool. The content of MRI images is controlled by an
acquisition sequence, which coordinates the timing and magnitude of the scanner
hardware activations, which shape and coordinate the magnetisation within the
body, allowing a coherent signal to be produced. The use of deep reinforcement
learning (DRL) to control this process, and determine new and efficient
acquisition strategies in MRI, has not been explored. Here, we take a first
step into this area, by using DRL to control a virtual MRI scanner, and framing
the problem as a game that aims to efficiently reconstruct the shape of an
imaging phantom using partially reconstructed magnitude images. Our findings
demonstrate that DRL successfully completed two key tasks: inducing the virtual
MRI scanner to generate useful signals and interpreting those signals to
determine the phantom's shape. This proof-of-concept study highlights the
potential of DRL in autonomous MRI data acquisition, shedding light on the
suitability of DRL for complex tasks, with limited supervision, and without the
need to provide human-readable outputs.