The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that the information lost in k-space from the acceleration mask makes it difficult to reconstruct an image similar to the quality of a fully sampled image. Multiple deep learning-based structures have been proposed for MRI reconstruction using CS, both in the k-space and image domains as well as using unrolled optimization methods. However, the drawback of these structures is that they are not fully utilizing the information from both domains (k-space and image). Herein, we propose a deep learning-based attention hybrid variational network that performs learning in both the k-space and image domain. We evaluate our method on a well-known open-source MRI dataset and a clinical MRI dataset of patients diagnosed with strokes from our institution to demonstrate the performance of our network. In addition to quantitative evaluation, we undertook a blinded comparison of image quality across networks performed by a subspecialty trained radiologist. Overall, we demonstrate that our network achieves a superior performance among others under multiple reconstruction tasks.

翻译：压缩感知（CS）数据重建在加速磁共振成像（MRI）中的应用仍是一个具有挑战性的问题。这是因为加速掩模导致的k空间信息丢失使得重建图像难以达到全采样图像的质量水平。目前，已有多种基于深度学习的方法被提出用于CS-MRI重建，包括在k空间域和图像域上的处理，以及使用展开优化方法。然而，这些结构的缺点在于未能充分利用k空间和图像两个域的信息。为此，我们提出了一种基于深度学习的注意力混合变分网络，该网络在k空间域和图像域中同时进行学习。我们使用一个著名的开源MRI数据集以及来自本机构的临床中风患者MRI数据集来评估我们的方法，以展示我们网络的性能。除定量评估外，我们还进行了由亚专科培训放射科医生实施的网络间图像质量盲法比较。总体而言，我们证明我们的网络在多种重建任务中均能达到优于其他方法的性能。