  Intravascular ultrasound (IVUS) offers a unique perspective in the treatment
of vascular diseases by creating a sequence of ultrasound-slices acquired from
within the vessel. However, unlike conventional hand-held ultrasound, the thin
catheter only provides room for a small number of physical channels for signal
transfer from a transducer-array at the tip. For continued improvement of image
quality and frame rate, we present the use of deep reinforcement learning to
deal with the current physical information bottleneck. Valuable inspiration has
come from the field of magnetic resonance imaging (MRI), where learned
acquisition schemes have brought significant acceleration in image acquisition
at competing image quality. To efficiently accelerate IVUS imaging, we propose
a framework that utilizes deep reinforcement learning for an optimal adaptive
acquisition policy on a per-frame basis enabled by actor-critic methods and
Gumbel top-$K$ sampling.
