Paper Citation Record · LEDGER

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

As of 23 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 5 inbound Pith citation observations for arXiv:2505.04522.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.04522 v1

Coverage vector

measured 72 of 72 reference resolution

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measured 77 of 77 standing notices

One-hop event checks from named stored sources.

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measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:53:04.225829Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T20:46:14.041465Z

Reference resolution

72 of 72 outbound references displayed

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External citation measurements

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Outbound references

Observation 03539d81-c6ab-4d06-8530-fec36e9a22a9 · outbound

This paper cites Review on covid-19 diagnosis mod- els based on machine learning and deep learning approaches.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Review on covid-19 diagnosis mod- els based on machine learning and deep learning approaches

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.391232Z

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Observation 802274d7-a056-4693-9baf-87b1bf60c645 · outbound

This paper cites PyTorch 2: Faster Machine Learning Through Dy- namic Python Bytecode Transformation and Graph Compi- lation.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model PyTorch 2: Faster Machine Learning Through Dy- namic Python Bytecode Transformation and Graph Compi- lation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.375773Z

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Observation 09d7c70f-b070-46ae-a19f-5017a6eab782 · outbound

This paper cites A systematic review on data scarcity problem in deep learning: solution and applications.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model A systematic review on data scarcity problem in deep learning: solution and applications

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.360738Z

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Observation 6011b01a-f068-4046-936c-b56150164f8c · outbound

This paper cites Improving image generation with better captions.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Improving image generation with better captions

Reference 4

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Observation d61c791a-39da-4967-8102-ef5780a30012 · outbound

This paper cites A vision–language foundation model for the generation of realistic chest x-ray images.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model A vision–language foundation model for the generation of realistic chest x-ray images

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:15.966125Z

Source-reported events for the cited work

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Observation ae528459-5ffd-4b18-90d4-099a63f800fa · outbound

This paper cites Video generation models as world simulators.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Video generation models as world simulators

Reference 6

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Observation aea66f9c-cb1a-4dce-bfb4-af19d9aab3d7 · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model MONAI: An open-source framework for deep learning in healthcare

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:15.976468Z

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Observation 0a20a618-e015-47f3-9367-be672f82b6b8 · outbound

This paper cites Multimodal mr synthesis via modality-invariant latent representation.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Multimodal mr synthesis via modality-invariant latent representation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.317228Z

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Observation 15acd324-5dbc-49b4-9411-995f6f2e7d96 · outbound

This paper cites Artistic style transfer with internal-external learning and contrastive learn- ing.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Artistic style transfer with internal-external learning and contrastive learn- ing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.301229Z

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Observation 658c6169-1866-4439-8c9c-fee46989c710 · outbound

This paper cites Real-world single image super-resolution: A brief review.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Real-world single image super-resolution: A brief review

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:15.991409Z

Source-reported events for the cited work

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Observation 8b2da68f-f8c7-4961-99ed-ca2738cc84d6 · outbound

This paper cites 2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model 2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction

Reference 11

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Observation 2cad4a33-f8d8-4a88-a876-417545d39d13 · outbound

This paper cites A review of medical image data augmentation techniques for deep learning appli- cations.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model A review of medical image data augmentation techniques for deep learning appli- cations

Reference 12

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Observation 0dff70d6-4fb5-4f71-aa25-1d21a556894d · outbound

This paper cites A Generalist Model for Diverse Text-Guided Medical Image Synthesis.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model A Generalist Model for Diverse Text-Guided Medical Image Synthesis

Reference 13

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Observation 8cdec841-c0c5-4246-b13e-3acc8fe7cc00 · outbound

This paper cites Ir-frestormer: Iterative refinement with fourier-based restormer for accelerated mri reconstruc- tion.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Ir-frestormer: Iterative refinement with fourier-based restormer for accelerated mri reconstruc- tion

Reference 14

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Observation cb47f1d6-4bbd-4666-acc1-e01b27ca233e · outbound

This paper cites Next- generation deep learning based on simulators and synthetic data.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Next- generation deep learning based on simulators and synthetic data

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.252561Z

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Observation 8bba7226-fc86-4486-a0cb-1a044c992ae8 · outbound

This paper cites Deepharmony: A deep learning approach to contrast harmonization across scanner changes.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Deepharmony: A deep learning approach to contrast harmonization across scanner changes

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.236991Z

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Observation cacdeddd-5479-4d2f-ab3a-86e1196da561 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Diffusion models beat gans on image synthesis

Reference 17

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Observation fd9e612e-6b8e-44c6-b2a0-b472f5e94683 · outbound

This paper cites Machine-learning-based multiple abnor- mality prediction with large-scale chest computed tomogra- phy volumes.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Machine-learning-based multiple abnor- mality prediction with large-scale chest computed tomogra- phy volumes

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.212796Z

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Observation 65a8b3c0-6b4a-4872-9ab4-8e94739d5706 · outbound

This paper cites The Llama 3 Herd of Models.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model The Llama 3 Herd of Models

Reference 19

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Observation 96f607f4-973a-4c3d-b520-b73e2707a02f · outbound

This paper cites Simulation and synthesis in medical imaging.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Simulation and synthesis in medical imaging

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.197757Z

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Observation 26a6c7ad-aaff-4895-bb2b-c7dd997dbd68 · outbound

This paper cites Make-a-scene: Scene- based text-to-image generation with human priors.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Make-a-scene: Scene- based text-to-image generation with human priors

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.182964Z

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Observation cb6bcac4-1727-4232-8f77-17f452da020f · outbound

This paper cites Data augmentation for medical imaging: A system- atic literature review.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Data augmentation for medical imaging: A system- atic literature review

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.167931Z

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Observation bed60e66-c205-4a0a-b159-b00eba0f8b9b · outbound

This paper cites Generative adversarial nets.

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

Reference 23

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Observation 302c94d0-8640-4ef7-a5f1-5fe037ec6509 · outbound

This paper cites Anatomic and molecu- lar mr image synthesis using confidence guided cnns.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Anatomic and molecu- lar mr image synthesis using confidence guided cnns

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.143437Z

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Observation 54d4a075-4ff7-4444-9cf8-8ab0f812660d · outbound

This paper cites an unresolved cited work.

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

Reference 25

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Observation 260dd85f-0137-43dc-9cc4-438364b62220 · outbound

This paper cites Maisi: Medical ai for synthetic imaging.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Maisi: Medical ai for synthetic imaging

Reference 26

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Observation 6678cb1f-252e-4ef4-a637-d3dde943dc9a · outbound

This paper cites Developing generalist foundation models from a multimodal dataset for 3d computed tomography, 2024.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Developing generalist foundation models from a multimodal dataset for 3d computed tomography, 2024

Reference 27

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Observation 2c2c85c7-46a1-4848-9a6c-bfc39f8fef09 · outbound

This paper cites Generatect: text- conditional generation of 3d chest ct volumes.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Generatect: text- conditional generation of 3d chest ct volumes

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.095958Z

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Observation 7767a82a-cce9-4de0-9a85-acf0e152a953 · outbound

This paper cites VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging

Reference 29

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Observation d5c7db14-5e83-4334-83f8-8dcdc3fd02b4 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 30

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Observation 64c0d634-f435-404b-ac9c-0cf77cde1ba5 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Denoising dif- fusion probabilistic models

Reference 31

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Observation 058033e2-f62c-4a4e-8ab0-db6b520f1993 · outbound

This paper cites Perceiver: General perception with iterative attention.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Perceiver: General perception with iterative attention

Reference 32

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Observation 8c0e04e6-3c66-4228-9d0b-96b3bfe33c4b · outbound

This paper cites Deep learning for text style transfer: A survey.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Deep learning for text style transfer: A survey

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.045122Z

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Observation b8b7fb14-40be-43fd-ac6f-ac0b2d6fcc83 · outbound

This paper cites Robust multi-modal mr image synthesis.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Robust multi-modal mr image synthesis

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:17.028589Z

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Observation a18bc873-1e1a-4af7-9dce-89e77430d52c · outbound

This paper cites End-to-end privacy pre- serving deep learning on multi-institutional medical imag- ing.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model End-to-end privacy pre- serving deep learning on multi-institutional medical imag- ing

Reference 35

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Observation cc3fa43f-342b-484f-8926-98fa7867e10e · outbound

This paper cites Diffusion models in medical imaging: A comprehensive survey.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Diffusion models in medical imaging: A comprehensive survey

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.997586Z

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Observation a57a2148-75e0-400c-b93c-bd7cb0549ab4 · outbound

This paper cites Controllable text-to-image generation.

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

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.981842Z

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Observation 06beb65d-825c-4ba8-b2c5-7d5b38698ee7 · outbound

This paper cites Deep learning as a tool for in- creased accuracy and efficiency of histopathological diagno- sis.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Deep learning as a tool for in- creased accuracy and efficiency of histopathological diagno- sis

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.966270Z

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Observation 8868c839-d5dd-4cb6-a512-550742e23617 · outbound

This paper cites Transformer for single im- age super-resolution.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Transformer for single im- age super-resolution

Reference 39

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Observation a73c1b7a-1138-4a7d-bc9f-505b7555dc68 · outbound

This paper cites An integrated iterative annotation technique for easing neural network training in medical image analysis.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model An integrated iterative annotation technique for easing neural network training in medical image analysis

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.940806Z

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Observation dc74939e-05ed-4207-ac2b-c1b1a655f542 · outbound

This paper cites Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhance- ment prospects.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhance- ment prospects

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.924344Z

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Observation 8e59496c-04f3-40c1-a061-08e6d3700756 · outbound

This paper cites Robson, Brett Marinelli, Mingqian Huang, Amish Doshi, Adam Jacobi, Chendi Cao, Katherine E.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Robson, Brett Marinelli, Mingqian Huang, Amish Doshi, Adam Jacobi, Chendi Cao, Katherine E

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.908183Z

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Observation 54da56a0-a042-4055-9968-30e62ee6f54d · outbound

This paper cites The creativity of text-to-image gen- eration.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model The creativity of text-to-image gen- eration

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.892629Z

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Observation f4278573-7d03-4e12-aae4-ad0a22c26574 · outbound

This paper cites Digitization of healthcare sector: A study on privacy and security concerns.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Digitization of healthcare sector: A study on privacy and security concerns

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.877916Z

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Observation d90058a7-ae36-46fb-9f66-d7cd88d07159 · outbound

This paper cites Towards performant and reliable undersampled mr reconstruction via diffusion model sam- pling.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Towards performant and reliable undersampled mr reconstruction via diffusion model sam- pling

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.863205Z

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Observation ee98593c-6b0b-41cb-a30a-6199f9e66e5f · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 46

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This paper cites Sampson, Shikai Li, Simone Parmeggiani, Steve Fine, Tara Fowler, Vladan Petro- vic, and Yuming Du.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Sampson, Shikai Li, Simone Parmeggiani, Steve Fine, Tara Fowler, Vladan Petro- vic, and Yuming Du

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.847256Z

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Observation de3dbfcc-5280-40d5-b13b-f89973c0ce1b · outbound

This paper cites Mirrorgan: Learning text-to-image generation by re- description.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Mirrorgan: Learning text-to-image generation by re- description

Reference 48

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Observation cec94dfd-1fc0-4022-92b7-f63117dc478b · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.822437Z

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Observation 2f3794cc-7434-4807-9e2f-a916af5d6e6a · outbound

This paper cites Zero-shot text-to-image generation.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Zero-shot text-to-image generation

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:16.179082Z

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Observation 9e210919-560e-48a7-9570-013d1779714d · outbound

This paper cites Deep learning for medical image processing: Overview, challenges and the future.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Deep learning for medical image processing: Overview, challenges and the future

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.797371Z

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Observation deb948cf-70d3-4161-aaa5-1aca205f7d07 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model High-resolution image synthesis with latent diffusion models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.781551Z

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Observation 96c5acd6-981e-49fd-b0f7-89dfe77cc751 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Photorealistic text-to-image diffusion models with deep language understanding

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:16.193473Z

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Observation 87a0439c-b9b8-4252-b64b-b691e1d25874 · outbound

This paper cites Image super- resolution via iterative refinement.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Image super- resolution via iterative refinement

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:16.198248Z

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Observation 9efded80-6e52-41ed-9641-795a02de9671 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Progressive Distillation for Fast Sampling of Diffusion Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:16.202673Z

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This paper cites Medical image synthesis for data augmentation and anonymization using generative adversarial networks.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Medical image synthesis for data augmentation and anonymization using generative adversarial networks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.744902Z

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Observation 301f335e-fe50-41db-8219-12174a47a51e · outbound

This paper cites Medical image generation using generative adversarial networks: A review.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Medical image generation using generative adversarial networks: A review

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.730311Z

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Observation ef214bbe-6f9b-44dc-a22a-2754d4d1e713 · outbound

This paper cites 3d deep learning on medical images: a review.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model 3d deep learning on medical images: a review

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.715344Z

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Observation 03b26008-c7b5-45a2-a238-faf105893d21 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Score-Based Generative Modeling through Stochastic Differential Equations

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:16.220826Z

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Observation f8bae819-7f5a-4cd8-a24a-83b4fe4ca076 · outbound

This paper cites Hierarchical amortized gan for 3d high resolution medical image synthesis.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Hierarchical amortized gan for 3d high resolution medical image synthesis

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.699619Z

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Observation c118aee5-1eda-4c96-9f9f-f49265d183f8 · outbound

This paper cites Guest editorial annotation-efficient deep learning: the holy grail of medical imaging.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Guest editorial annotation-efficient deep learning: the holy grail of medical imaging

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.684358Z

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Observation fa133fc3-31e0-47d7-a71a-bfe4ebf5ea28 · outbound

This paper cites To- talsegmentator: robust segmentation of 104 anatomic struc- tures in ct images.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model To- talsegmentator: robust segmentation of 104 anatomic struc- tures in ct images

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:16.234939Z

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Observation fcca6695-fa33-445c-a2c3-8de44d21c697 · outbound

This paper cites Measurement-conditioned denoising diffusion probabilistic model for under-sampled medical image reconstruction.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Measurement-conditioned denoising diffusion probabilistic model for under-sampled medical image reconstruction

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.659555Z

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Observation 4c30d4aa-fe54-4796-99ec-3f40fadcd38d · outbound

This paper cites Medsyn: Text-guided anatomy-aware synthesis of high-fidelity 3d ct images.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Medsyn: Text-guided anatomy-aware synthesis of high-fidelity 3d ct images

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.643517Z

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Observation 7ca90021-0d46-45ec-9652-84924008df12 · outbound

This paper cites Unsupervised mr- to-ct synthesis using structure-constrained cyclegan.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Unsupervised mr- to-ct synthesis using structure-constrained cyclegan

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.627928Z

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Observation fb11ffbb-9eb1-46d8-a49a-72374b92f74b · outbound

This paper cites Image Synthesis under Limited Data: A Survey and Taxonomy.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Image Synthesis under Limited Data: A Survey and Taxonomy

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:16.253354Z

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This paper cites Text-to-image Diffusion Models in Generative AI: A Survey.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Text-to-image Diffusion Models in Generative AI: A Survey

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:16.257914Z

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Observation 18ae4746-8597-4d1a-8528-8c776cdfeb9b · outbound

This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T23:30:16.262504Z

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Observation 1d219fdd-e658-433b-b79b-9ae20c861fa6 · outbound

This paper cites Bridging 2d and 3d segmentation networks for computation- efficient volumetric medical image segmentation: An empir- ical study of 2.5 d solutions.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Bridging 2d and 3d segmentation networks for computation- efficient volumetric medical image segmentation: An empir- ical study of 2.5 d solutions

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.613224Z

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Observation e7e76124-5386-4fc4-9a7c-62b21f63e119 · outbound

This paper cites Deep generative modeling for scene synthesis via hybrid represen- tations.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Deep generative modeling for scene synthesis via hybrid represen- tations

Reference 70

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Observation 0df3d2de-9a9b-4063-8773-27cf5be05488 · outbound

This paper cites Whole brain segmentation and labeling from ct using synthetic mr images.

Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model Whole brain segmentation and labeling from ct using synthetic mr images

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.587821Z

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Observation 8dd2f8e6-8f92-4785-a5c9-13435f986a2d · outbound

This paper cites role":"system.

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

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:30:16.572180Z

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Pith citing papers

Observation e1eb6dd3-8d8b-4a89-8c29-f3720c389615 · inbound

ShapeKit cites this paper.

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

Reference 10

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Observation 45894187-db6b-4928-bafe-e62f4531c9b5 · inbound

Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling cites this paper.

Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:36:04.877255Z

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Observation 42f96403-a48f-428d-a03b-25d9e91a2f63 · inbound

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study cites this paper.

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:25.677620Z

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Observation 47427c3a-3a4d-4a92-8baf-6ad666972e4d · inbound

MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts cites this paper.

MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T20:46:14.042767Z

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Observation 3b0bed85-19f8-4713-98c1-fab45d800a1c · inbound

Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy cites this paper.

Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model

Reference 6

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