Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach
<|reference_start|>Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach: 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.<|reference_end|>
@article{hashmi2024accelerating,
    title={Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven
  Approach},
    author={Anam Hashmi, Julia Dietlmeier, Kathleen M. Curran and Noel E. O'Connor},
    journal={arXiv preprint arXiv:2404.06941},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06941},
    primaryClass={eess.IV cs.CV}
}