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Abstract #4781

Synthetic Banding for bSSFP Data Augmentation

Michael Anthony Mendoza1, Nicholas McKibben1, Grayson Tarbox1, and Neal K Bangerter1,2

1Electrical Engineering, Brigham Young University, Provo, UT, United States, 2Bioengineering, Imperial College London, London, United Kingdom

Balanced Steady State Free Precession (bSSFP) MRI is a highly-efficient MRI pulse sequence but suffers from banding artifacts caused by its high sensitivity to magnetic field inhomogeneity. Many algorithms exist that can effectively remove these banding artifacts, typically by requiring multiple phase-cycled acquisitions, which increase scan time. While some of the algorithms can suppress banding to some degree with two sets of phase-cycled acquisitions, much more accurate band suppression is typically achieved with at least four phase-cycled acquisitions. In this work, we present a deep learning method for synthesizing additional phase-cycled images from a set of at least two phase-cycled images that can then be used with existing band reduction techniques in order to reduce scan time.

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