Magnetic resonance imaging (MRI) is a highly versatile and widely used clinical imaging tool. The content of MRI images is controlled by an acquisition sequence, which coordinates the timing and magnitude of the scanner hardware activations, which shape and coordinate the magnetisation within the body, allowing a coherent signal to be produced. The use of deep reinforcement learning (DRL) to control this process, and determine new and efficient acquisition strategies in MRI, has not been explored. Here, we take a first step into this area, by using DRL to control a virtual MRI scanner, and framing the problem as a game that aims to efficiently reconstruct the shape of an imaging phantom using partially reconstructed magnitude images. Our findings demonstrate that DRL successfully completed two key tasks: inducing the virtual MRI scanner to generate useful signals and interpreting those signals to determine the phantom's shape. This proof-of-concept study highlights the potential of DRL in autonomous MRI data acquisition, shedding light on the suitability of DRL for complex tasks, with limited supervision, and without the need to provide human-readable outputs.

翻译：磁共振成像（MRI）是一种高度通用且广泛使用的临床成像工具。MRI图像的内容由采集序列控制，该序列协调扫描仪硬件激活的时序与幅度，从而塑造和协调体内磁化，以产生相干信号。目前尚未有研究探索利用深度强化学习（DRL）控制这一过程，并确定MRI中新型高效采集策略。本文通过使用DRL控制虚拟MRI扫描仪，将问题建模为一种旨在利用部分重建幅度图像高效重建成像体模形状的博弈，首次涉足该领域。研究结果表明，DRL成功完成了两项关键任务：诱导虚拟MRI扫描仪产生有用信号，以及解读这些信号以确定体模形状。这项概念验证研究揭示了DRL在自主MRI数据采集中的潜力，阐明了DRL在有限监督下无需提供人类可读输出即可胜任复杂任务的适用性。