Deep Complex Neural Network for Undersampling Spiral Artefact Removal in Diffusion Tensor Cardiovascular Magnetic Resonance with In-vivo Study

Motivation: Diffusion Tensor Cardiovascular Magnetic Resonance (DT-CMR) is hindered by low resolution and long acquisitions. Spiral trajectories could be efficient with effective removal of artefacts from undersampled images. Goal(s): To remove artefacts from highly accelerated spiral in-vivo DT-CMR acquisitions using a novel deep learning method. Approach: We proposed a Residual U-Net based Complex-valued Edge Attention Network (CEAN) to remove undersampling artefacts. Training with and without transfer learning were explored. Results: CEAN with transfer learning outperformed other networks, achieving the lowest Mean Absolute Error (MAE) for DT-CMR parameters and preserving diffusion encoding information, suggesting future potentials in accelerating clinical DT-CMR studies. Impact: This work will allow the acquisition and reconstruction of highly accelerated STEAM spiral DT-CMR, aided by the proposed deep Complex-valued Edge Attention Network. Further developments will allow increases in spatial resolution to facilitate clinical translation of DT-CMR.