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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