Dynamic Magnetic Resonance Imaging (MRI) is known to be a powerful and reliable technique for the dynamic imaging of internal organs and tissues, making it a leading diagnostic tool. A major difficulty in using MRI in this setting is the relatively long acquisition time (and, hence, increased cost) required for imaging in high spatio-temporal resolution, leading to the appearance of related motion artifacts and decrease in resolution. Compressed Sensing (CS) techniques have become a common tool to reduce MRI acquisition time by subsampling images in the k-space according to some acquisition trajectory. Several studies have particularly focused on applying deep learning techniques to learn these acquisition trajectories in order to attain better image reconstruction, rather than using some predefined set of trajectories. To the best of our knowledge, learning acquisition trajectories has been only explored in the context of static MRI. In this study, we consider acquisition trajectory learning in the dynamic imaging setting. We design an end-to-end pipeline for the joint optimization of multiple per-frame acquisition trajectories along with a reconstruction neural network, and demonstrate improved image reconstruction quality in shorter acquisition times. The code for reproducing all experiments is accessible at https://github.com/tamirshor7/MultiPILOT.

翻译：动态磁共振成像（Dynamic MRI）被认为是一种对内器官和组织进行动态成像的强大且可靠的技术，因而成为重要的诊断工具。在此应用中，MRI的一大难点在于实现高时空分辨率成像所需的较长采集时间（从而增加成本），这会导致相关运动伪影的出现和分辨率的下降。压缩感知（CS）技术已成为一种常用手段，通过沿特定采集轨迹对k空间中的图像进行亚采样来缩短MRI采集时间。多项研究特别专注于应用深度学习技术学习这些采集轨迹，而非使用预设轨迹集，以实现更好的图像重建。据我们所知，采集轨迹学习仅在静态MRI背景下被探讨。在本研究中，我们考虑动态成像场景下的采集轨迹学习。我们设计了一种端到端流水线，用于联合优化多个逐帧采集轨迹与重建神经网络，并在更短采集时间内展现出改进的图像重建质量。复现所有实验的代码可在https://github.com/tamirshor7/MultiPILOT获取。