Cardiac cine magnetic resonance imaging (MRI) is one of the important means to assess cardiac functions and vascular abnormalities. Mitigating artifacts arising during image reconstruction and accelerating cardiac cine MRI acquisition to obtain high-quality images is important. A novel end-to-end deep learning network is developed to improve cardiac cine MRI reconstruction. First, a U-Net is adopted to obtain the initial reconstructed images in k-space. Further to remove the motion artifacts, the motion-guided deformable alignment (MGDA) module with second-order bidirectional propagation is introduced to align the adjacent cine MRI frames by maximizing spatial-temporal information to alleviate motion artifacts. Finally, the multi-resolution fusion (MRF) module is designed to correct the blur and artifacts generated from alignment operation and obtain the last high-quality reconstructed cardiac images. At an 8$\times$ acceleration rate, the numerical measurements on the ACDC dataset are structural similarity index (SSIM) of 78.40%$\pm$.57%, peak signal-to-noise ratio (PSNR) of 30.46$\pm$1.22dB, and normalized mean squared error (NMSE) of 0.0468$\pm$0.0075. On the ACMRI dataset, the results are SSIM of 87.65%$\pm$4.20%, PSNR of 30.04$\pm$1.18dB, and NMSE of 0.0473$\pm$0.0072. The proposed method exhibits high-quality results with richer details and fewer artifacts for cardiac cine MRI reconstruction on different accelerations.

翻译：心脏电影磁共振成像（MRI）是评估心脏功能与血管异常的重要手段之一。减轻图像重建过程中产生的伪影并加速心脏电影MRI采集以获得高质量图像至关重要。本文开发了一种新颖的端到端深度学习网络以改进心脏电影MRI重建。首先，采用U-Net在k空间获取初始重建图像。为进一步消除运动伪影，引入了具有二阶双向传播的运动引导可变形对齐（MGDA）模块，通过最大化时空信息来对齐相邻的电影MRI帧，从而减轻运动伪影。最后，设计了多分辨率融合（MRF）模块，以校正对齐操作产生的模糊和伪影，并获取最终高质量的心脏重建图像。在8倍加速率下，于ACDC数据集上的数值测量结果为：结构相似性指数（SSIM）78.40%±0.57%，峰值信噪比（PSNR）30.46±1.22 dB，归一化均方误差（NMSE）0.0468±0.0075。在ACMRI数据集上，结果为：SSIM 87.65%±4.20%，PSNR 30.04±1.18 dB，NMSE 0.0473±0.0072。所提方法在不同加速率下的心脏电影MRI重建中，展现出具有更丰富细节和更少伪影的高质量结果。