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

arXiv: https://arxiv.org/abs/2608.09992 · HTML: https://mlsanity.com/paper/2608.09992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 25 of 25)

- [Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis](https://mlsanity.com/paper/2305.18453/) (2023)
- [Towards Generalizable Tumor Synthesis](https://mlsanity.com/paper/2402.19470/) (2024)
- [GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes](https://mlsanity.com/paper/2305.16037/) (2023)
- [MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images](https://mlsanity.com/paper/2310.03559/) (2023)
- [MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation](https://mlsanity.com/paper/2304.04106/) (2023)
- [Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model](https://mlsanity.com/paper/2305.19467/) (2023)
- [MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss](https://mlsanity.com/paper/2508.05772/) (2025)
- [cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis](https://mlsanity.com/paper/2411.17203/) (2024)
- [Generative Enhancement for 3D Medical Images](https://mlsanity.com/paper/2403.12852/) (2024)
- [Deep Generative Models for 3D Medical Image Synthesis](https://mlsanity.com/paper/2410.17664/) (2024)
- [Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model](https://mlsanity.com/paper/2505.04522/) (2025)
- [TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency](https://mlsanity.com/paper/2507.00802/) (2025)
- [Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation](https://mlsanity.com/paper/2508.09177/) (2025)
- [Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing](https://mlsanity.com/paper/2407.20830/) (2024)
- [Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis](https://mlsanity.com/paper/2502.15204/) (2025)
- [CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation](https://mlsanity.com/paper/2403.17770/) (2024)
- [CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis](https://mlsanity.com/paper/2508.12900/) (2025)
- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance](https://mlsanity.com/paper/2510.18446/) (2025)
- [MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis](https://mlsanity.com/paper/2503.13211/) (2025)
- [3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation](https://mlsanity.com/paper/2507.11557/) (2025)
- [Radiology Report Conditional 3D CT Generation with Multi Encoder Latent diffusion Model](https://mlsanity.com/paper/2509.14780/) (2025)
- [NodMAISI: Nodule-Oriented Medical AI for Synthetic Imaging](https://mlsanity.com/paper/2512.18038/) (2025)
- [Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics](https://mlsanity.com/paper/2505.22489/) (2025)
- [Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface](https://mlsanity.com/paper/2505.22511/) (2025)

## Built on by (most cited first, 1 of 1)

- [Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data](https://mlsanity.com/paper/2608.13256/) (2026)

## Abstract (arXiv)

Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of samples with explicit constraints on semantic content, structural properties, and variability. In 3D Computed Tomography (CT), such control is essential for clinical applications, including data augmentation, privacy-preserving data sharing, and the simulation of specific anatomical or pathological scenarios. While research on conditional 3D CT generation has expanded rapidly, the diversity of existing approaches makes systematic comparison difficult and obscures fundamental design choices. In this survey, we propose a conditioning-centric taxonomy that organizes the literature along three orthogonal dimensions: the type of external knowledge (K), the knowledge integration paradigm (I), and the generative architecture (A). This factorization defines an explicit design space (K x I x A) that provides a unified perspective on prior work. Using this framework, we systematize existing methods, identify dominant trends and recurring design patterns, and highlight underexplored regions of the design space that point toward promising directions for future research.