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dc.contributor.author
Thomsen, Felix Sebastian Leo  
dc.contributor.author
Iarussi, Emmanuel  
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Borggrefe, Jan  
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Boyd, Steven K.  
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Wang, Yue  
dc.contributor.author
Battié, Michele C.  
dc.date.available
2023-12-18T18:31:34Z  
dc.date.issued
2023-06  
dc.identifier.citation
Thomsen, Felix Sebastian Leo; Iarussi, Emmanuel; Borggrefe, Jan; Boyd, Steven K.; Wang, Yue; et al.; Bone‐GAN: Generation of virtual bone microstructure of high resolution peripheral quantitative computed tomography; American Association of Physicists in Medicine; Medical Physics; 50; 11; 6-2023; 6943-6954  
dc.identifier.issn
0094-2405  
dc.identifier.uri
http://hdl.handle.net/11336/220680  
dc.description.abstract
BackgroundData-driven development of medical biomarkers of bone requires a large amount of image data but physical measurements are generally too restricted in size and quality to perform a robust training.PurposeThis study aims to provide a reliable in silico method for the generation of realistic bone microstructure with defined microarchitectural properties. Synthetic bone samples may improve training of neural networks and serve for the development of new diagnostic parameters of bone architecture and mineralization.MethodsOne hundred-fifty cadaveric lumbar vertebrae from 48 different male human spines were scanned with a high resolution peripheral quantitative CT. After prepocessing the scans, we extracted 10,795 purely spongeous bone patches, each with a side length of 32 voxels (5 mm) and isotropic voxel size of 164 μm. We trained a volumetric generative adversarial network (GAN) in a progressive manner to create synthetic microstructural bone samples. We then added a style transfer technique to allow the generation of synthetic samples with defined microstructure and gestalt by simultaneously optimizing two entangled loss functions. Reliability testing was performed by comparing real and synthetic bone samples on 10 well-understood microstructural parameters.ResultsThe method was able to create synthetic bone samples with visual and quantitative properties that effectively matched with the real samples. The GAN contained a well-formed latent space allowing to smoothly morph bone samples by their microstructural parameters, visual appearance or both. Optimum performance has been obtained for bone samples with voxel size 32 × 32 × 32, but also samples of size 64 × 64 × 64 could be synthesized.ConclusionsOur two-step-approach combines a parameter-agnostic GAN with a parameter-specific style transfer technique. It allows to generate an unlimited anonymous database of microstructural bone samples with sufficient realism to be used for the development of new data-driven methods of bone-biomarkers. Particularly, the style transfer technique can generate datasets of bone samples with specific conditions to simulate certain bone pathologies.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
American Association of Physicists in Medicine  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
BONE MICROSTRUCTURE  
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GESTALT  
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PROGRESSIVE GENERATIVE ADVERSARIAL NETWORK  
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STRUCTURAL MORPHING  
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XTREMECT  
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Otras Ciencias de la Computación e Información  
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Ciencias de la Computación e Información  
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CIENCIAS NATURALES Y EXACTAS  
dc.title
Bone‐GAN: Generation of virtual bone microstructure of high resolution peripheral quantitative computed tomography  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2023-12-15T14:07:43Z  
dc.journal.volume
50  
dc.journal.number
11  
dc.journal.pagination
6943-6954  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
New York  
dc.description.fil
Fil: Thomsen, Felix Sebastian Leo. Ruhr Universität Bochum; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina  
dc.description.fil
Fil: Iarussi, Emmanuel. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
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Fil: Borggrefe, Jan. Ruhr Universität Bochum; Alemania  
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Fil: Boyd, Steven K.. University of Calgary; Canadá  
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Fil: Wang, Yue. Zhejiang University School Of Medicine; China  
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Fil: Battié, Michele C.. University of Alberta; Canadá  
dc.journal.title
Medical Physics  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.16482  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/mp.16482