Active MR k-space Sampling with Reinforcement Learning

Active CT Reconstruction with a Learned Sampling Policy

Computed tomography (CT) is a widely-used imaging technology that assists clinical decision-making with high-quality human body representations. To reduce the radiation dose posed by CT, sparse-view and limited-angle CT are developed with preserved image quality. However, these methods are still stuck with a fixed or uniform sampling strategy, which inhibits the possibility of acquiring a better image with an even reduced dose. In this paper, we explore this possibility via learning an active sampling policy that optimizes the sampling positions for patient-specific, high-quality reconstructio…

时间医学影像序列采样的强化学习

Reinforcement Learning for Sampling on Temporal Medical Imaging Sequences

本文提出用双重深度Q学习和REINFORCE算法学习时间医学影像序列的最优采样策略，以加速MRI重建，并验证了强化学习在发现预训练重建器网络最优采样模式上的有效性。

基于强化学习的自适应k空间径向采样用于心脏MRI

Adaptive k-space Radial Sampling for Cardiac MRI with Reinforcement Learning

本文提出一种基于强化学习的双分支框架，结合交叉注意力与解剖感知奖励，优化心脏MRI径向k空间采样轨迹，在多个加速因子下提升重建质量。

Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning

In this paper, we present an active vision method using a deep reinforcement learning approach for a humanoid soccer-playing robot. The proposed method adaptively optimises the viewpoint of the robot to acquire the most useful landmarks for self-localisation while keeping the ball into its viewpoint. Active vision is critical for humanoid decision-maker robots with a limited field of view. To deal with an active vision problem, several probabilistic entropy-based approaches have previously been proposed which are highly dependent on the accuracy of the self-localisation model. However, in this…

基于主动采样模式与学习重建的三维单像素成像

3D Single-pixel imaging with active sampling patterns and learning based reconstruction

本文提出一种基于主动采样模式与深度学习重建的三维单像素成像方法，通过设计模式进行定向扫描采样并利用深度学习实现低采样率下的高精度三维重建。