Keywords: AI/ML Image Reconstruction, Diffusion Tensor Imaging
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.
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