Magnetic Resonance Imaging (MRI) has long been considered to be among "the
gold standards" of diagnostic medical imaging. The long acquisition times,
however, render MRI prone to motion artifacts, let alone their adverse
contribution to the relative high costs of MRI examination. Over the last few
decades, multiple studies have focused on the development of both physical and
post-processing methods for accelerated acquisition of MRI scans. These two
approaches, however, have so far been addressed separately. On the other hand,
recent works in optical computational imaging have demonstrated growing success
of concurrent learning-based design of data acquisition and image
reconstruction schemes. Such schemes have already demonstrated substantial
effectiveness, leading to considerably shorter acquisition times and improved
quality of image reconstruction. Inspired by this initial success, in this
work, we propose a novel approach to the learning of optimal schemes for
conjoint acquisition and reconstruction of MRI scans, with the optimization
carried out simultaneously with respect to the time-efficiency of data
acquisition and the quality of resulting reconstructions. To be of a practical
value, the schemes are encoded in the form of general k-space trajectories,
whose associated magnetic gradients are constrained to obey a set of predefined
hardware requirements (as defined in terms of, e.g., peak currents and maximum
slew rates of magnetic gradients). With this proviso in mind, we propose a
novel algorithm for the end-to-end training of a combined
acquisition-reconstruction pipeline using a deep neural network with
differentiable forward- and back-propagation operators. We demonstrate its
effectiveness on image reconstruction and image segmentation tasks, reporting
substantial improvements in terms of acceleration factors as well as the
quality of these tasks.

探讨了 MRI 扫描加速和重建的优化方案，提出使用深度学习和 k 空间轨迹的联合方案，用于同时优化数据获取的时间效率和图像重建的质量，并证明其对于图像重建和分割任务，具有显著的加速因素和质量改进。