Evidence map›Paper›PMID 40735600›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2025

A Conditional Generative Diffusion Model of Trabecular Bone with Tunable Microstructure.

X Wang, G Shi, A Sivakumar, T Ye, A Sylvester, J W Stayman, W Zbijewski

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In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. A Generative Model of Lung CT Conditioned on Radiomics Features.Proceedings of SPIE--the International Society for Optical Engineering · 2026
    Article
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

X WangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.
G ShiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.
A SivakumarDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.
T YeDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.
A SylvesterCenter for Functional Anatomy and Evolution, Johns Hopkins University, Baltimore, MD USA 21205.
J W StaymanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.
W ZbijewskiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA 21205.

Funding

Quantitative bone radiomics using Ultra-High Resolution CTR01EB029446 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI ZBIJEWSKI, WOJCIECH BARTOSZ · 2021 to 2024
$1.7M
NIBIB NIH HHS R01 EB029446
6 · The paper itself

Abstract

Purpose: We developed a generative model capable of producing synthetic trabecular bone that can be precisely tuned to achieve specific structural characteristics, such as bone volume fraction (BV/TV), trabecular thickness (Tb.Th), and spacing (Tb.Sp). Methods: The generative model is based on Diffusion Transformers (DiT), a latent diffusion approach employing a transformer architecture in the denoising network. To control the microstructure characteristics of the synthetic trabecular bone samples, the model is conditioned on BV/TV, Tb.Th, and Tb.Sp. The training data involved 29898 256×256-pixel Regions of Interest (ROIs) extracted from micro-CT volumes ( Results: The model achieved good coverage of real-world bone microstructures and visual similarity to true trabecular ROIs. Pearson Correlations against the condition (target) metric values were high: 0.9540 for BV/TV, 0.9618 for Tb.Th, and 0.9835 Tb.Sp. Microstructural characteristics of the synthetic samples were stable across DiT realizations, with CV ranging from 3.37% to 11.78% for BV/TV, 2.27% to 3.22% for Tb.Th, and 2.53% to 5.00% for Tb.Sp. Conclusion: The proposed generative model is capable of generating realistic digital trabecular bones that can be precisely tuned to achieve specified microstructural characteristics. Possible applications include virtual clinical trials of new skeletal image biomarkers and establishing priors for advanced image reconstruction.

Indexed as

Bone MicrostructureDiffusion ModelSynthesisTrabecular BoneVirtual Imaging

Identifiers

PMID40735600
PMCPMC12302783

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.