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Abstract #0571

Inception-CS: Deep Learning For Sparse MR Reconstruction in Glioma Patients

Peter D Chang1, Michael Z Liu2, Daniel S Chow3, Melissa Khy3, Christopher G Filippi4, Janine Lupo1, and Christopher Hess1

1University of California San Francisco, San Francisco, CA, United States, 2Columbia University Medical Center, New York, NY, United States, 3University of California Irvine, Irvine, CA, United States, 4Hofstra Northwell School of Medicine, Manhasset, NY, United States

Sparse MR image reconstruction through deep learning represents a promising novel solution with early results suggesting improved performance compared to standard techniques. However, given that neural networks reconstruct using a learned manifold of rich image priors, it is unclear how the algorithm will perform when exposed to pathology not present during network training. In this study we: (1) present a novel Inception-CS architecture for reconstruction using extensive residual Inception-v4 modules; (2) demonstrate state-of-the-art reconstruction performance in glioma patients however only when representative pathology is available during algorithm training.

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