Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning
This addresses the challenge of improving image quality and frame rate for vascular disease treatment, but it is incremental as it adapts methods from MRI to IVUS.
The paper tackles the physical information bottleneck in intravascular ultrasound (IVUS) imaging by using deep reinforcement learning to optimize acquisition policies, achieving accelerated imaging with competitive quality.
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.