时间医学影像序列采样的强化学习
Reinforcement Learning for Sampling on Temporal Medical Imaging Sequences
- Amazon（亚马逊）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
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。
AI中文摘要：
加速磁共振成像要么采用傅里叶域子采样，要么采用更好的重建算法，以在减少测量次数的同时仍生成高质量的医学图像。在给定固定重建协议的情况下，确定最优采样策略通常具有组合复杂性。在这项工作中，我们应用双重深度Q学习和REINFORCE算法来学习动态图像重建的采样策略。我们考虑时间序列格式的数据，重建方法是一个预训练的自编码器类型神经网络。我们提供了一个概念验证，表明强化学习算法能够有效发现预训练重建器网络（即环境中的动态）背后的最优采样模式。用于复现实验的代码可在 https://github.com/zhishenhuang/RLsamp 找到。
英文摘要：
Accelerated magnetic resonance imaging resorts to either Fourier-domain subsampling or better reconstruction algorithms to deal with fewer measurements while still generating medical images of high quality. Determining the optimal sampling strategy given a fixed reconstruction protocol often has combinatorial complexity. In this work, we apply double deep Q-learning and REINFORCE algorithms to learn the sampling strategy for dynamic image reconstruction. We consider the data in the format of time series, and the reconstruction method is a pre-trained autoencoder-typed neural network. We present a proof of concept that reinforcement learning algorithms are effective to discover the optimal sampling pattern which underlies the pre-trained reconstructor network (i.e., the dynamics in the environment). The code for replicating experiments can be found at https://github.com/zhishenhuang/RLsamp.
