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REVIEW 4 major objections 5 minor 30 references

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

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A new taxonomy organizes knowledge-guided 3D CT generation into a three-axis design space and identifies the field's dominant recipe: geometric masks with in-process modulation in single-stage latent diffusion.

desk verdict Useful taxonomy, overclaimed design space: the paper's own concessions undercut the orthogonality premise, but the framework is still worth a serious referee. read the letter →

arxiv 2608.09992 v1 pith:BRSCULWN submitted 2026-08-07 eess.IV cs.AIcs.CVcs.LG

Francesca Pia Panaccione , Eugenio Lomurno , Matteo Matteucci This is my paper · ORCID
classification eess.IVcs.AIcs.CVcs.LG
keywords knowledge-guidedgeneration3DCTsynthesisconditioningtaxonomylatentdiffusiondesignspaceanalysismedicalimagegeometricmaskstext-to-CT
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that the recent literature on knowledge-guided 3D CT generation has been organized by architecture or application, obscuring the true design choices, and that the field deserves a conditioning-centric view. It proposes the first conditioning-centric taxonomy, factorizing every method along three orthogonal axes: the type of external knowledge (K), the knowledge integration paradigm (I), and the generative architecture (A). Together these axes define a design space K × I × A in which each method occupies a well-specified tuple, turning a heterogeneous collection of papers into a quantifiable distribution. The authors use this space to identify the prevailing paradigm—geometric segmentation masks with in-process integration in single-stage latent diffusion, the tuple (K2, I2, A1)—and to reveal both dominant couplings (text with inference-time guidance) and structural gaps (demographic attributes in cascaded pipelines only). If the taxonomy is correct, it gives the community a shared vocabulary for comparing methods and a principled way to pick research directions that fill real gaps rather than proliferate one more variant.

What carries the argument

The load-bearing object is the taxonomy itself: a three-faceted factorization of conditioning design into axis K (external knowledge: K1 textual, K2 geometric, K3 exemplar, K4 attribute/categorical), axis I (integration: I1 alignment, I2 model-based, I3 inference-time, I4 joint distribution modeling), and axis A (architecture: A1 single-stage latent, A2 multi-stage cascaded, A3 spatially-autoregressive, A4 fixed-transform domain). Each method gets a tuple (k, i, a), and the literature becomes a distribution over the product space K × I × A. This machinery does the analytic work: it turns 'which method does what' into 'which cells are occupied, which are empty, and which configurations co-occur,' which is exactly what lets the authors claim a prevailing paradigm and enumerate research directions.

What would settle it

A published 3D CT generator that combines free-text clinical descriptions with a fixed-transform wavelet diffusion backbone and reports competitive synthesis quality would falsify the paper's claim that the K1–A4 cell is structurally impossible. A detailed ablation showing that such a system fails for the geometry-motivated reason the paper cites would instead corroborate it.

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Extended reading notes

Core claim

The paper's central discovery is that the entire recent literature on knowledge-guided 3D CT generation, surveyed across 2023–2025, can be positioned in a single interpretable design space defined by three independent axes: external knowledge type (K: textual, geometric, exemplar, attribute/categorical), knowledge integration paradigm (I: pre-generative alignment, model-based injection, inference-time guidance, joint distribution modeling), and generative architecture (A: single-stage latent, cascaded, spatially-autoregressive, fixed-transform). Within this space, the authors show that the field has converged on a dominant configuration—K2 geometric masks, I2 model-based integration, A1 single-stage latent diffusion—exemplified by a cluster of segmentation-mask-conditioned latent diffusion models. They further show that other configurations are not evenly scattered: textual knowledge pairs with inference-time guidance, demographic attributes appear only in cascaded pipelines, and certain cells are empty for what the authors argue are mechanistic reasons rather than oversight. The discovery is descriptive, not prescriptive: the taxonomy does not rank methods, but it converts a scattered set of papers into a quantifiable distribution with visible centers and gaps.

Load-bearing premise

The three axes are orthogonal and independent, meaning every combination of knowledge type, integration paradigm, and architecture is in principle realizable and that empty cells are genuine research opportunities rather than artifacts of the way the space was carved up.

Editorial extensions

If this is right

  • New methods can be positioned in the field with a single tuple: the design space replaces vague 'diffusion-based' or 'text-guided' labels with an explicit (K, I, A) coordinate.
  • The dominant cell (K2, I2, A1) defines a baseline that future mask-conditioned generators will be compared against.
  • Demographic conditioning (K4) is flagged as the most undervalued knowledge type, since it needs no segmentation or learned encoder and yet appears in only 6% of methods.
  • The paper's gap analysis suggests that hybrid integrations (I1 with I2, or I2 with I3) and text-plus-structure combinations (K1+K3, K1+K4) are the most promising unexplored configurations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The taxonomy's orthogonality claim is testable: over a larger corpus, one could measure whether a method's K category predicts its I and A categories; the paper's own numbers already hint at strong statistical dependence, which would mean some 'gaps' are actually forbidden cells.
  • The same factorization could be lifted to other volumetric modalities—MRI, PET, ultrasound—yielding a cross-modality map of conditioning strategies that would let researchers see which design choices are domain-specific and which are universal.
  • The paper stops at description; a natural next step is a prospective registry where new contributions self-report their (K, I, A) tuple, turning the taxonomy into a living tool that tracks the field's evolution in real time.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a conditioning-centric taxonomy for knowledge-guided 3D CT generation, organizing methods along three axes: external knowledge type (K), knowledge integration paradigm (I), and generative architecture (A). It claims these axes are orthogonal, defines a Cartesian design space K×I×A, classifies 25 methods in Table 5, and identifies the triplet (K2, I2, A1) — geometric masks with in-process modulation in single-stage latent diffusion — as the prevailing paradigm. The paper also derives research directions from supposedly underexplored regions of the design space. The central contribution is a descriptive organizational framework plus a quantitative trend analysis.

Significance. If the taxonomy is sound, it would provide a useful common vocabulary for a rapidly growing literature, and the open-source repository and interactive tool would support community adoption. The paper explicitly aims to move beyond architecture- or application-centered surveys, and the detailed Table 5 with per-method conditioning sources and mechanisms is a valuable reference asset. However, the load-bearing claims about orthogonality and about gaps as genuine opportunities are weakened by internal inconsistencies in the paper's own data. The descriptive framework is still potentially salvageable, but the quantitative trend analysis and the gap-based research directions require substantial revision to be trustworthy.

major comments (4)
  1. [§3.1, §3.3, §5] The paper's central premise is that the three axes are independent and form a Cartesian design space (§3.1: "three independent axes"), and that empty cells are therefore candidates for future work. This is contradicted inside the paper. §3.3 (Axis I) states that the I2 mechanism "depends on the structural properties of k" (non-spatial vs. spatially aligned), so K and I are not independent by the taxonomy's own definitions. §5 then concedes that K4–I3 is "redundant" and K1–A4 is "mechanistically inapplicable". These are not empirical gaps but logical consequences of the definitions. Consequently, the underexplored cells identified in §6 cannot be read as genuine opportunities without a realizability analysis that distinguishes structural impossibilities from empirical gaps. The paper should either relax the orthogonality claim or add an explicit feasibility map over the K×I×A space.
  2. [Table 5 vs §4/§5] There is a direct inconsistency in the classification of GenerateCT. Table 5 lists GenerateCT as K1, I1, I2, A2, with conditioning mechanism "Cross-attention, CFG". However, §4 states that "A strong coupling between K1 and I3 is observed (e.g., GenerateCT, Report2CT, Text2CT, TRACE)", and §5 claims that "textual conditioning (K1) pairs exclusively with classifier-free guidance (I3)" citing GenerateCT among others. Since Table 5 does not assign I3 to GenerateCT, the §5 "exclusively" claim is false: several K1 methods in Table 5 (GenerateCT I1,I2; Text-to-CT I1,I2; CTFlow I2) do not use I3. This discrepancy affects the reported K1–I3 coupling and the design-space conclusions. The authors should reconcile the table with the prose and qualify the claimed exclusivity.
  3. [§4, Figure 2] The category percentages are computed over multi-label entries, which makes the stated shares ambiguous and potentially misleading. The text reports K2=44%, K3=28%, K1=22%, K4=6%, which sum to exactly 100%. But many methods carry multiple labels (e.g., MedSyn K1,K2; TRACE K1,K2; Surf2CT K2,K4; Lung-DDPM K2,K3), so a method-based count would produce a sum above 100%. The paper does not specify whether percentages are normalized by the total number of labels or by the number of methods. Also, the phrase "accounts for 6% of existing methods" for K4 is inconsistent with the fact that two of 25 methods (Cascaded-3D, Surf2CT) carry K4, which would be 8%. The quantitative trend analysis and the "prevailing paradigm" conclusion depend on these counts, so the counting rule must be stated and applied consistently.
  4. [§4, Table 5 overall] The survey does not describe the literature search and selection procedure that produced the 25 methods in Table 5. There is no statement of databases, search terms, inclusion/exclusion criteria, or screening process. Given the paper's claim to provide a "comprehensive overview" and to quantify distributions over the design space, the absence of a reproducible selection protocol makes the sample potentially non-representative and the percentages non-reproducible. The authors should add a methodology subsection or at least a clear description of how the method set was assembled and why it is complete as of the stated cutoff.
minor comments (5)
  1. [§4, Figure 2] The text refers to "Figure 2 (top-center)" for both the knowledge integration trends and the architectural trends; the top-center panel appears to show the I axis, while the architectural distribution should be the top-right panel. Please correct the figure references.
  2. [§5] In the third design pattern, the paper writes that fixed-transform approaches are "currently inapplicable to abstract conditioning (K1, K2) lacking inherent spatial structure". This contradicts the definition of K2 as geometric knowledge (organ segmentation masks, anatomical layouts), which is inherently spatial. The intended claim likely refers to K1 and K4; please fix the category labels.
  3. [Table 1] The last column header "Qnt. Analysis" is abbreviated in a way that may confuse readers; consider writing "Quantitative analysis".
  4. [Table 5] Some cells contain obvious OCR-like artifacts (e.g., "V oxel-aligned Semantic Maps" for MedGen3D), which should be cleaned up for a camera-ready version.
  5. [§2] The formalization writes p_θ(x|k) with k ∈ K, but later §3.1 writes k ⊆ K. This inconsistency in notation (element vs. subset) should be harmonized, since multi-label methods imply that k is a set of category labels.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a descriptive literature taxonomy with no fitted parameters, no predictive derivation, and no load-bearing self-citation.

full rationale

The paper is a survey that organizes existing methods into a K x I x A design space. There is no mathematical derivation whose conclusion is equivalent to its assumptions, and no parameter is fitted to data and then renamed as a prediction. The only self-citation is [Lomurno and Matteucci, 2025] in the introduction, used as general motivation that synthetic data can address data-sharing limitations; this claim is not load-bearing for the taxonomy's structure or for any later design-space conclusion. The taxonomy's categories are defined independently of the trends they are used to describe: Axis K, I, and A are defined in Section 3.2–3.4 before the literature is classified in Section 4, so the observed co-occurrences are empirical summaries of the surveyed methods rather than constructed consequences of the definitions. The paper's own Section 5 concedes that some gaps are mechanistic rather than empirical (e.g., K4-I3 'redundant', K1-A4 'mechanistically inapplicable'), which is an honest acknowledgment of non-orthogonality rather than a circular step. The limitations section also explicitly disclaims quantitative comparison and notes the absence of standardized validation, further supporting that the contribution is organizational, not predictive. No circularity is found.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The taxonomy itself is a new conceptual framework, not a physical model, so it introduces no free parameters or invented entities. The load-bearing premises are the orthogonality and exhaustiveness of its categories, and the representativeness of the surveyed methods.

assumptions (3)
  • domain assumption The three axes K, I, and A are orthogonal and independent, so any combination is in principle realizable.
    Introduced in Section 3.1 as 'three orthogonal dimensions' and needed for the design-space gap analysis. The paper's own trend observations in Section 4 show strong couplings, so this assumption is not independently established.
  • domain assumption The four categories per axis are exhaustive for all knowledge-guided 3D CT generation methods.
    The paper does not prove coverage. New methods might not fit any of K1-K4, I1-I4, A1-A4, yet the taxonomy is claimed to cover the literature.
  • domain assumption The 25 selected methods are representative of the literature.
    No systematic search or inclusion criteria are described, so trend percentages in Figure 2 and Table 5 depend on this sample.

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Cite this review

Pith. "Pith review of Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy." pith.science (2026). https://pith.science/paper/BRSCULWN

@misc{pith2026260809992,
  author       = {Pith},
  title        = {Pith review of: Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BRSCULWN}},
  note         = {Machine review of arXiv:2608.09992}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2608.09992 by the authors.

Figure 1
Figure 1. Conceptual representation of the proposed taxon [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Statistical distribution of methods across the taxonomy. Top row: frequency of each category in axes [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

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Works this paper leans on

30 extracted references · 20 canonical work pages

  1. [1]

    Radiology report conditional 3d ct generation with multi encoder latent diffusion model.arXiv preprint arXiv:2509.14780,

    [Amirrajabet al., 2025 ] Sina Amirrajab, Zohaib Salahuddin, Sheng Kuang, Henry C Woodruff, and Philippe Lam- bin. Radiology report conditional 3d ct generation with multi encoder latent diffusion model.arXiv preprint arXiv:2509.14780,

    arXiv 2025

  2. [4]

    Condi- tional diffusion models for semantic 3d brain mri synthe- sis.IEEE Journal of Biomedical and Health Informatics, 28(7):4084–4093,

    [Dorjsembeet al., 2024 ] Zolnamar Dorjsembe, Hsing-Kuo Pao, Sodtavilan Odonchimed, and Furen Xiao. Condi- tional diffusion models for semantic 3d brain mri synthe- sis.IEEE Journal of Biomedical and Health Informatics, 28(7):4084–4093,

    work page 2024

  3. [5]

    cwdm: Conditional wavelet diffusion models for cross-modality 3d medical image synthesis.arXiv preprint arXiv:2411.17203,

    [Friedrichet al., 2024a ] Paul Friedrich, Alicia Durrer, Ju- lia Wolleb, and Philippe C Cattin. cwdm: Conditional wavelet diffusion models for cross-modality 3d medical image synthesis.arXiv preprint arXiv:2411.17203,

    arXiv

  4. [6]

    Text2ct: Towards 3d ct volume generation from free-text descriptions using diffusion model.arXiv preprint arXiv:2505.04522,

    [Guoet al., 2025a ] Pengfei Guo, Can Zhao, Dong Yang, Yu- fan He, Vishwesh Nath, Ziyue Xu, Pedro RAS Bassi, Zongwei Zhou, Benjamin D Simon, Stephanie Anne Har- mon, et al. Text2ct: Towards 3d ct volume generation from free-text descriptions using diffusion model.arXiv preprint arXiv:2505.04522,

    arXiv

  5. [7]

    Developing gener- alist foundation models from a multimodal dataset for 3d computed tomography.arXiv preprint arXiv:2403.17834,

    [Hamamciet al., 2024b ] Ibrahim Ethem Hamamci, Sezgin Er, Chenyu Wang, Furkan Almas, Ayse Gulnihan Simsek, Sevval Nil Esirgun, Irem Dogan, Omer Faruk Durugol, Benjamin Hou, Suprosanna Shit, et al. Developing gener- alist foundation models from a multimodal dataset for 3d computed tomography.arXiv preprint arXiv:2403.17834,

    arXiv

  6. [8]

    Medgen3d: A deep genera- tive framework for paired 3d image and mask generation

    [Hanet al., 2023 ] Kun Han, Yifeng Xiong, Chenyu You, Pooya Khosravi, Shanlin Sun, Xiangyi Yan, James S Duncan, and Xiaohui Xie. Medgen3d: A deep genera- tive framework for paired 3d image and mask generation. InInternational Conference on Medical Image Comput- ing and Computer-Assisted Intervention, pages 759–769. Springer,

    work page 2023

  7. [10]

    De- noising diffusion probabilistic models for 3d medical im- age generation.Scientific Reports, 13(1):7303,

    [Khaderet al., 2023 ] Firas Khader, Gustav Müller-Franzes, Soroosh Tayebi Arasteh, Tianyu Han, Christoph Haar- burger, Maximilian Schulze-Hagen, Philipp Schad, Sandy Engelhardt, Bettina Baeßler, Sebastian Foersch, et al. De- noising diffusion probabilistic models for 3d medical im- age generation.Scientific Reports, 13(1):7303,

    work page 2023

  8. [11]

    Gener- ating synthetic data for medical imaging.Radiology, 312(3):e232471,

    [Koetzieret al., 2024 ] Lennart R Koetzier, Jie Wu, Domenico Mastrodicasa, Aline Lutz, Matthew Chung, W Adam Koszek, Jayanth Pratap, Akshay S Chaudhari, Pranav Rajpurkar, Matthew P Lungren, et al. Gener- ating synthetic data for medical imaging.Radiology, 312(3):e232471,

    work page 2024

Show all 30 references
  1. [12]

    Med-lvdm: Medical latent variational diffusion model for medical im- age translation.Biomedical Signal Processing and Con- trol, 106:107735,

    [Kuiet al., 2025 ] Xiaoyan Kui, Bo Liu, Zanbo Sun, Qinsong Li, Min Zhang, Wei Liang, and Beiji Zou. Med-lvdm: Medical latent variational diffusion model for medical im- age translation.Biomedical Signal Processing and Con- trol, 106:107735,

    2025

  2. [13]

    3d brain and heart vol- ume generative models: a survey.ACM Computing Sur- veys, 56(6):1–37,

    [Liuet al., 2024 ] Yanbin Liu, Girish Dwivedi, Farid Bous- said, and Mohammed Bennamoun. 3d brain and heart vol- ume generative models: a survey.ACM Computing Sur- veys, 56(6):1–37,

    2024

  3. [14]

    Federated knowledge recycling: Privacy- preserving synthetic data sharing.Pattern Recognition Letters, 191:124–130,

    [Lomurno and Matteucci, 2025] Eugenio Lomurno and Mat- teo Matteucci. Federated knowledge recycling: Privacy- preserving synthetic data sharing.Pattern Recognition Letters, 191:124–130,

    2025

  4. [15]

    3d latent diffusion model for mr-only radiotherapy: Accurate and consistent synthetic ct generation.Diagnostics, 15(23):3010,

    [Mahdiet al., 2025 ] Mohammed A Mahdi, Mohammed Al- Shalabi, Ehab T Alnfrawy, Reda Elbarougy, Muham- mad Usman Hadi, and Rao Faizan Ali. 3d latent diffusion model for mr-only radiotherapy: Accurate and consistent synthetic ct generation.Diagnostics, 15(23):3010,

    2025

  5. [16]

    Text-to-ct generation via 3d latent diffusion model with contrastive vision-language pretraining.arXiv preprint arXiv:2506.00633,

    [Molinoet al., 2025 ] Daniele Molino, Camillo Maria Caruso, Filippo Ruffini, Paolo Soda, and Valerio Guarrasi. Text-to-ct generation via 3d latent diffusion model with contrastive vision-language pretraining.arXiv preprint arXiv:2506.00633,

    2025 arXiv

  6. [17]

    Land: Lung and nodule diffusion for 3d chest ct synthesis with anatomical guidance.arXiv preprint arXiv:2510.18446,

    [Oliveraset al., 2025 ] Anna Oliveras, Roger Marí, Rafael Redondo, Oriol Guardià, Ana Tost, Bhalaji Nagarajan, Carolina Migliorelli, Vicent Ribas, and Petia Radeva. Land: Lung and nodule diffusion for 3d chest ct synthesis with anatomical guidance.arXiv preprint arXiv:2510.18446,

    2025

  7. [18]

    Synthetic ct generation from mri using 3d transformer-based denoising diffusion model.Medical Physics, 51(4):2538–2548,

    [Panet al., 2024 ] Shaoyan Pan, Elham Abouei, Jacob Wynne, Chih-Wei Chang, Tonghe Wang, Richard LJ Qiu, Yuheng Li, Junbo Peng, Justin Roper, Pretesh Pa- tel, et al. Synthetic ct generation from mri using 3d transformer-based denoising diffusion model.Medical Physics, 51(4):2538–2548,

    2024

  8. [19]

    Medlord: A medical low-resource diffusion model for high-resolution 3d ct image synthesis

    [Seyfarthet al., 2025 ] Marvin Seyfarth, Salman Ul Hassan Dar, Isabelle Ayx, Matthias Alexander Fink, Stefan O Schoenberg, Hans-Ulrich Kauczor, and Sandy Engelhardt. Medlord: A medical low-resource diffusion model for high-resolution 3d ct image synthesis. InInternational Work...

    2025

  9. [20]

    Trace: Temporally reliable anatomically-conditioned 3d ct gen- eration with enhanced efficiency

    [Shaoet al., 2025 ] Minye Shao, Xingyu Miao, Haoran Duan, Zeyu Wang, Jingkun Chen, Yawen Huang, Xian Wu, Jingjing Deng, Yang Long, and Yefeng Zheng. Trace: Temporally reliable anatomically-conditioned 3d ct gen- eration with enhanced efficiency. InInternational Con- ference on...

    2025

  10. [21]

    Mr to ct synthesis using 3d la- tent diffusion

    [Tappet al., 2024 ] Austin Tapp, Abhijeet Parida, Can Zhao, Van Lam, Natasha Lepore, Syed Muhammad Anwar, and Marius George Linguraru. Mr to ct synthesis using 3d la- tent diffusion. In2024 IEEE International Symposium on Biomedical Imaging (ISBI), pages 1–5. IEEE,

    2024

  11. [22]

    Nodmaisi: Nodule- oriented medical ai for synthetic imaging.arXiv preprint arXiv:2512.18038,

    [Tusharet al., 2025 ] Fakrul Islam Tushar, Ehsan Samei, Cynthia Rudin, and Joseph Y Lo. Nodmaisi: Nodule- oriented medical ai for synthetic imaging.arXiv preprint arXiv:2512.18038,

    2025

  12. [23]

    [Wuet al., 2025 ] Jing Wu, Yuli Wang, Zhusi Zhong, Weihua Liao, Natalia Trayanova, Zhicheng Jiao, and Harrison X Bai

    under review. [Wuet al., 2025 ] Jing Wu, Yuli Wang, Zhusi Zhong, Weihua Liao, Natalia Trayanova, Zhicheng Jiao, and Harrison X Bai. Vision-language foundation model for 3d medical imaging.npj Artificial Intelligence, 1(1):17,

    2025

  13. [24]

    Medsyn: text-guided anatomy-aware synthe- sis of high-fidelity 3-d ct images.IEEE Transactions on Medical Imaging, 43(10):3648–3660,

    [Xuet al., 2024 ] Yanwu Xu, Li Sun, Wei Peng, Shuyue Jia, Katelyn Morrison, Adam Perer, Afrooz Zandifar, Shyam Visweswaran, Motahhare Eslami, and Kayhan Bat- manghelich. Medsyn: text-guided anatomy-aware synthe- sis of high-fidelity 3-d ct images.IEEE Transactions on Medical I...

    2024

  14. [25]

    Surf2ct: Cascaded 3d flow matching mod- els for torso 3d ct synthesis from skin surface.arXiv preprint arXiv:2505.22511,

    [Yoonet al., 2025a ] Siyeop Yoon, Yujin Oh, Pengfei Jin, Sifan Song, Matthew Tivnan, Dufan Wu, Xiang Li, and Quanzheng Li. Surf2ct: Cascaded 3d flow matching mod- els for torso 3d ct synthesis from skin surface.arXiv preprint arXiv:2505.22511,

    arXiv

  15. [26]

    Ct synthesis with conditional diffusion models for abdominal lymph node segmentation

    [Yuet al., 2024 ] Yongrui Yu, Hanyu Chen, Zitian Zhang, Qiong Xiao, Wenhui Lei, Linrui Dai, Yu Fu, Hui Tan, Guan Wang, Peng Gao, et al. Ct synthesis with conditional diffusion models for abdominal lymph node segmentation. arXiv preprint arXiv:2403.17770,

    2024

  16. [27]

    Maisi-v2: Accelerated 3d high-resolution medical im- age synthesis with rectified flow and region-specific con- trastive loss.arXiv preprint arXiv:2508.05772,

    [Zhaoet al., 2025 ] Can Zhao, Pengfei Guo, Dong Yang, Yucheng Tang, Yufan He, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, and Daguang Xu. Maisi-v2: Accelerated 3d high-resolution medical im- age synthesis with rectified flow and region-specific con- trastive ...

    2025

  17. [28]

    3d wavelet latent diffusion model for whole-body mr-to-ct modality translation.arXiv preprint arXiv:2507.11557,

    [Zhenget al., 2025 ] Jiaxu Zheng, Meiman He, Xuhui Tang, Xiong Wang, Tuoyu Cao, Tianyi Zeng, Lichi Zhang, and Chenyu You. 3d wavelet latent diffusion model for whole-body mr-to-ct modality translation.arXiv preprint arXiv:2507.11557,

    2025 arXiv

  18. [29]

    Generative artificial intelligence in medical imag- ing: Foundations, progress, and clinical translation.arXiv preprint arXiv:2508.09177,

    [Zhouet al., 2025 ] Xuanru Zhou, Cheng Li, Shuqiang Wang, Ye Li, Tao Tan, Hairong Zheng, and Shanshan Wang. Generative artificial intelligence in medical imag- ing: Foundations, progress, and clinical translation.arXiv preprint arXiv:2508.09177,

    2025 arXiv

  19. [30]

    Genera- tive enhancement for 3d medical images.arXiv preprint arXiv:2403.12852, 2024

    [Zhuet al., 2024 ] Lingting Zhu, Noel Codella, Dongdong Chen, Zhenchao Jin, Lu Yuan, and Lequan Yu. Genera- tive enhancement for 3d medical images.arXiv preprint arXiv:2403.12852, 2024

    2024 arXiv

  20. [2023]

    Lung-ddpm: Semantic layout- guided diffusion models for thoracic ct image synthesis

    [Jianget al., 2025 ] Yifan Jiang, Yannick Lemaréchal, Josée Bafaro, Jessica Abi-Rjeile, Philippe Joubert, Philippe De- sprés, and Venkata Manem. Lung-ddpm: Semantic layout- guided diffusion models for thoracic ct image synthesis. arXiv preprint arXiv:2502.15204,

    2025 arXiv

  21. [2024]

    Towards generalizable tumor synthesis

    [Chenet al., 2024 ] Qi Chen, Xiaoxi Chen, Haorui Song, Zhiwei Xiong, Alan Yuille, Chen Wei, and Zongwei Zhou. Towards generalizable tumor synthesis. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11147–11158,

    2024

  22. [2025]

    Deep generative models for 3d content creation: A com- prehensive survey of architectures, challenges, and emerg- ing trends

    [Chen and Ramsey, 2024] Kaiqi Chen and Libby Ramsey. Deep generative models for 3d content creation: A com- prehensive survey of architectures, challenges, and emerg- ing trends

    2024

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