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Abstract #0807

Phase2Phase: Reconstruction of free-breathing MRI into multiple respiratory phases using deep learning without a ground truth

Cihat Eldeniz1, Weijie Gan1, Sihao Chen1, Jiaming Liu1, Ulugbek S. Kamilov1, and Hongyu An1
1Washington University in St. Louis, Saint Louis, MO, United States

Radial MRI can be used for reconstructing multiple respiratory phases with retrospective binning. However, short acquisitions suffer from significant streaking artifacts. Compressed sensing (CS)-based methods are commonly used; nevertheless, CS is computational intensive and the image quality depends on the regularization parameters. We hereby propose a deep learning method that does not need an artifact-free target during training. The method can reconstruct high-quality volumes with ten respiratory phases, even for acquisitions close to 1 minute in length. The method outperforms CS for the same acquisition duration and can yield slightly better results than Unet3D trained using a surrogate ground truth.

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