We prospectively evaluate the generalized performance of the Deep Learning Reconstruction (DLR) method on 55 datasets acquired from 16 different anatomies. For each pulse sequence in each of the 16 anatomies, DLR and 3 predicate methods were reconstructed for randomized blinded review by 3 radiologists based on 8 scoring criteria plus a force-ranking. DLR was scored statistically higher than all 3 predicate methods in 92% of the pairwise comparisons in terms of overall image quality, clinically relevant anatomical/pathological features, and force-ranking. This work demonstrates that DLR generalizes to various anatomies and is frequently preferred over existing methods by experienced readers.
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