mlsanityData to 7 Oct 2026

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model

arXiv 2505.04522 ↗First posted 7 May 2025eess.IV cs.CVcited by 13

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Generating 3D CT volumes from descriptive free-text inputs presents a transformative opportunity in diagnostics and research. In this paper, we introduce Text2CT, a novel approach for synthesizing 3D CT volumes from textual descriptions using the diffusion model. Unlike previous methods that rely on fixed-format text input, Text2CT employs a novel prompt formulation that enables generation from diverse, free-text descriptions. The proposed framework encodes medical text into latent representations and decodes them into high-resolution 3D CT scans, effectively bridging the gap between semantic text inputs and detailed volumetric representations in a unified 3D framework. Our method demonstrates superior performance in preserving anatomical fidelity and capturing intricate structures as described in the input text. Extensive evaluations show that our approach achieves state-of-the-art results, offering promising potential applications in diagnostics, and data augmentation.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 40 of its 70 references that are in the map. First posted on arXiv 7 May 2025.

Builds on

40 of 70 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024MAISI: Medical AI for Synthetic Imaging13 Sep
  2. 20242.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction12 Jun
  3. 2023GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes25 May
  4. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov
  5. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling8 Mar
  6. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction5 Mar

Other references · 34

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2024The Llama 3 Herd of Modelscited by 14,192
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  7. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  8. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  9. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  10. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  11. 2021Perceiver: General Perception with Iterative Attentioncited by 1,089
  12. 2024Movie Gen: A Cast of Media Foundation Modelscited by 614
22 more
  1. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 597
  2. 2023BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairscited by 506
  3. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  4. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  5. 2023Text-to-image Diffusion Models in Generative AI: A Surveycited by 264
  6. 2019Controllable Text-to-Image Generationcited by 187
  7. 2019MirrorGAN: Learning Text-to-image Generation by Redescriptioncited by 176
  8. 2020Deep Learning for Text Style Transfer: A Surveycited by 161
  9. 2021Transformer for Single Image Super-Resolutioncited by 150
  10. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 146
  11. 2017Deep Learning for Medical Image Processing: Overview, Challenges and Futurecited by 104
  12. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  13. 20203D Deep Learning on Medical Images: A Reviewcited by 76
  14. 2022The Creativity of Text-to-Image Generationcited by 73
  15. 2020Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumescited by 66
  16. 2021Real-World Single Image Super-Resolution: A Brief Reviewcited by 65
  17. 2018Deep Generative Modeling for Scene Synthesis via Hybrid Representationscited by 63
  18. 2020Hierarchical Amortized Training for Memory-efficient High Resolution 3D GANcited by 60
  19. 2023MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Imagescited by 41
  20. 2020Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutionscited by 30
  21. 2018Iterative annotation to ease neural network training: Specialized machine learning in medical image analysiscited by 21
  22. 2023Image Synthesis under Limited Data: A Survey and Taxonomycited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 8.

13 papers.

Sort:

By area:

  1. 2025Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Datacited by 13
  2. 2025Radiology Report Conditional 3D CT Generation with Multi Encoder Latent diffusion Modelcited by 3
  3. 2025See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancementcited by 3
  4. 2026Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Studycited by 1
  5. 2026Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generationcited by 1
  6. 2026Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomycited by 1
  7. 2025ShapeKitcited by 0
  8. 2025Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidancecited by 0
  9. 2026CSEval: A Framework for Evaluating Clinical Semantics in Text-to-Image Generationcited by 0
  10. 2026Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesiscited by 0
  11. 2026PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancementcited by 0
  12. 2026Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modelingcited by 0
1 more
  1. 2026MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Promptscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generation
  2. 2026Sketch2CT: Multimodal Diffusion for Structure-Aware 3D Medical Volume Generation
  3. 2026Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation
  4. 2024GuideGen: A Text-Guided Framework for Paired Full-torso Anatomy and CT Volume Generation
  5. 2025Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imaging
  6. 2026Curriculum-Driven 3D CT Report Generation via Language-Free Visual Grafting and Zone-Constrained Compression
  7. 2025TextDiffSeg: Text-guided Latent Diffusion Model for 3d Medical Images Segmentation
  8. 2026Beyond the Embedding Bottleneck: Adaptive Retrieval-Augmented 3D CT Report Generation