Abstract #3622

End-to-End Deep Learning Reconstruction for Ultra-Short MRF

Mingdong Fan¹, Brendan Eck², Nicole Seiberlich³, Michael Martens¹, and Robert Brown¹

¹Physics, Case Western Reserve University, Cleveland, OH, United States, ²Cardiovascular and Metabolic Sciences, Cleveland Clinic, Cleveland, OH, United States, ³Radiology, University of Michigan, Ann Arbor, Ann Arbor, MI, United States

There are two major challenges in MRF reconstruction, the aliasing artifacts that results from the largely under-sampled k-space, and the very long MRF sequence used in practice to improve the reconstruction accuracy. In this study, we propose an end-to-end deep learning based reconstruction model that aims to address the issue of the spatial aliasing artifacts and provide accurate reconstruction with ultra-short MRF signals.

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