Abstract #3486

SuperQ: 3D Super-Resolution of Quantitative Susceptibility Maps

Alexandra Grace Roberts^1,2, Yi Wang^1,2, Pascal Spincemaille², and Thanh Nguyen²

¹Electrical Engineering, Cornell University, Ithaca, NY, United States, ²Radiology, Weill Cornell Medicine, New York, NY, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Brain3D super-resolution of QSM is feasible using the VDSR and U-net architectures using a fraction of the required number of epochs as previous 2D super-resolution networks. Additionally, this method both reduces whole brain MSE and ROI MSE and increases the apparent resolution as compared to the interpolated input

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