A residual neural network using patient-derived anatomical priors outperforms a near-optimal classical per-bundle algorithm in multi-source CT inversion at high attenuation, reaching error ratios of 0.096 on patient data while classical methods cannot cross the single-source performance floor.
Image reconstruction and image quality evaluation for a 64-slice CT scanner with z-flying focal spot
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Neural-Network Inversion for the Temporal CT Multi-Source Bundle Problem: Per-Bundle Statistical Limits and Near-Optimal Performance
A residual neural network using patient-derived anatomical priors outperforms a near-optimal classical per-bundle algorithm in multi-source CT inversion at high attenuation, reaching error ratios of 0.096 on patient data while classical methods cannot cross the single-source performance floor.