  Cine cardiac magnetic resonance (CMR) imaging is recognised as the benchmark
modality for the comprehensive assessment of cardiac function. Nevertheless,
the acquisition process of cine CMR is considered as an impediment due to its
prolonged scanning time. One commonly used strategy to expedite the acquisition
process is through k-space undersampling, though it comes with a drawback of
introducing aliasing effects in the reconstructed image. Lately, deep
learning-based methods have shown remarkable results over traditional
approaches in rapidly achieving precise CMR reconstructed images. This study
aims to explore the untapped potential of attention mechanisms incorporated
with a deep learning model within the context of the CMR reconstruction
problem. We are motivated by the fact that attention has proven beneficial in
downstream tasks such as image classification and segmentation, but has not
been systematically analysed in the context of CMR reconstruction. Our primary
goal is to identify the strengths and potential limitations of attention
algorithms when integrated with a convolutional backbone model such as a U-Net.
To achieve this, we benchmark different state-of-the-art spatial and channel
attention mechanisms on the CMRxRecon dataset and quantitatively evaluate the
quality of reconstruction using objective metrics. Furthermore, inspired by the
best performing attention mechanism, we propose a new, simple yet effective,
attention pipeline specifically optimised for the task of cardiac image
reconstruction that outperforms other state-of-the-art attention methods. The
layer and model code will be made publicly available.
