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Abstract #3605

Unsupervised Reconstruction of Continuous Dynamic Radial Acquisitions via CNN-NUFFT Self-Consistency

Matthew Muckley*1, Tullie Murrell*2, Suvrat Booshan2, Hersh Chandarana1, Florian Knoll1, and Daniel K. Sodickson1
1Radiology, NYU School of Medicine, New York, NY, United States, 2Facebook AI Research, Menlo Park, CA, United States

The introduction of machine learning for medical image reconstruction has opened up new opportunities for reconstruction speed and subsampling; however, acquiring ground truth data is expensive or impossible in the case of dynamic imaging. Here we investigate a technique for optimizing a CNN on continuous radial data by treating the NUFFT-CNN function as an autoencoding deep image prior. Using this method, we are able to reconstruct images that increment over time frames as short as a single spoke. The technique opens up new possibilities for dynamic image reconstruction.

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