Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy

arXiv:2608.09992 · eess.IV, cs.AI, cs.CV, cs.LG · Submitted 2026-08-07 · Read on arXiv

Francesca Pia Panaccione, Eugenio Lomurno, Matteo Matteucci

eess.IV, cs.AI, cs.CV, cs.LG

Submitted: 2026-08-07

Updated: 2026-08-12

Comments: Accepted to IJCAI-ECAI 2026, Survey Track

Code: https://github.com/eugeniolomurno/3D-CT-taxonomy

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

Terminology

• Alignment — 13× in this paper · explained in 1 episode
• Diffusion Model — 12× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Segmentation Masks — 5× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Mask Generation — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• D Computed Tomography — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Multimodal Integration — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Radiology Report Conditional 3D CT Generation with Multi Encoder Latent diffusion Model
• cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis
• Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model
• Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
• Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
• From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generation
• LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance
• NodMAISI: Nodule-Oriented Medical AI for Synthetic Imaging
• Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface
• CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation
• MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss
• 3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation
• Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation
• Generative Enhancement for 3D Medical Images

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