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Abstract #4487

Movieformer: Motion-resolved 4D MRI reconstruction using a network with spatiotemporal attention

Anthony Mekhanik1, Victor Murray1, and Ricardo Otazo1,2
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

Synopsis

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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Keywords

motionreconstructionnetworkattentionresolvedtransformer