Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
Motivation: Investigate utility of self-attention deep learning to exploit global temporal information in motion-resolved 4D MR imaging.
Goal(s): Design a novel hybrid convolutional-attention network to reconstruct motion-resolved 4D images without explicit k-space data consistency.
Approach: A hybrid Unet-style 4D reconstruction network was developed to incorporate windowed multiscale spatiotemporal multihead self-attention. Training and testing were performed on free-breathing data acquired on patients with abdominal tumors.
Results: Spatiotemporal attention successfully captured motion in multiple dimensions with improved image quality relative to state-of-the-art XD-GRASP reconstruction.
Impact: Self-attention deep learning mechanism can combine long-range spatial learning and global temporal learning to augment capabilities of convolutional networks for improved motion-resolved 4D MRI of mobile tumors.
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