VoxelSynth3D: Interpretable Volumetric Image-Domain Metal Artifact Reduction with a Paired Synthetic CLINIC Benchmark

arXiv:2610.01512 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-10

Comments: 7 pages, 7 figures. Accepted for publication at BHI 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Ablation — 10× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Trace — 3× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• New York University Abu Dhabi — 1× in this paper
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes

Sources

• InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction
• Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
• Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction

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