Abstract #0391

Undersampling reconstruction of ferumoxytol-enhanced cardiac cine MRI using a spatiotemporal neural network

Chang Gao^1,2, Zhengyang Ming^1,2, Kim-Lien Nguyen^1,2, Xiaodong Zhong³, and John Paul Finn^1,2

¹Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States, ²Department of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States, ³MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionFerumoxytol can support high quality SGE cardiac cine with blood-myocardial CNR similar to SSFP cine, but free of off-resonance artifact. Much work is ongoing to accelerate the acquisition of multi-slice, breath held cardiac cine. Deep learning-based reconstruction methods can accelerate the image acquisition and reconstruction but needs a large amount of data to train. When compared with compressed sensing and low rank reconstructions, our network showed sharper images and higher consistency with the reference and significantly better quantitative evaluation metrics. We showed that a network trained with non-contrast images could generalize to accelerated ferumoxytol-enhanced cardiac cine MRI with 10x acceleration.

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