Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning framework to optimize and accelerate intravascular ultrasound imaging by adaptively selecting acquisition parameters, inspired by MRI acceleration techniques.

Contribution

It presents a novel deep reinforcement learning approach for adaptive IVUS image acquisition, addressing physical channel limitations and improving imaging speed.

Findings

Achieved accelerated IVUS imaging with maintained image quality.

Developed an adaptive acquisition policy using actor-critic methods.

Demonstrated effectiveness through simulation results.

Abstract

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.