Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's
function and condition in a non-invasive manner. Undersampling of the $k$-space
is employed to reduce the scan duration, thus increasing patient comfort and
reducing the risk of motion artefacts, at the cost of reduced image quality. In
this challenge paper, we investigate the use of a convolutional recurrent
neural network (CRNN) architecture to exploit temporal correlations in
supervised cine cardiac MRI reconstruction. This is combined with a
single-image super-resolution refinement module to improve single coil
reconstruction by 4.4\% in structural similarity and 3.9\% in normalised mean
square error compared to a plain CRNN implementation. We deploy a high-pass
filter to our $\ell_1$ loss to allow greater emphasis on high-frequency details
which are missing in the original data. The proposed model demonstrates
considerable enhancements compared to the baseline case and holds promising
potential for further improving cardiac MRI reconstruction.

研究提出了一种卷积递归神经网络（CRNN）架构，用于利用时间相关性在监督影像的心脏磁共振成像重建中。该模型结合了单图像超分辨率细化模块，相比于普通 CRNN 的实现，其在结构相似性和标准化均方误差方面，使单线圈重建提高了 4.4％和 3.9％。通过在 l1 损失中使用高通滤波器，可以更好地强调原始数据中缺失的高频细节，从而证明了该模型相较于基准案例的显著改进，并具有进一步提高心脏磁共振成像重建的潜力。