基于深度强化学习的血管内超声成像加速方法
Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning
AI总结：
针对血管内超声导管物理通道有限的信息瓶颈问题，提出基于深度强化学习、结合actor-critic方法与Gumbel top-$K$采样的逐帧自适应采集框架，在保证图像质量的同时提升成像帧率。
摘要：
血管内超声（IVUS）通过生成从血管内部采集的超声切片序列，为血管疾病治疗提供了独特视角。但与传统手持超声不同，纤细的导管仅能为尖端换能器阵列的信号传输预留少量物理通道空间。为持续提升图像质量与帧率，我们提出采用深度强化学习来解决当前的物理信息瓶颈问题。这一思路借鉴了磁共振成像（MRI）领域的宝贵经验：在该领域中，基于学习的采集方案已在保证图像质量的前提下，显著提升了图像采集速度。为高效加速IVUS成像，我们提出了一个利用深度强化学习的框架，通过actor-critic方法和Gumbel top-$K$采样，实现逐帧的最优自适应采集策略。
英文摘要：
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.
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