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dc.contributor.author
Baravalle, Rodrigo Guillermo  
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Thomsen, Felix Sebastian Leo  
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Delrieux, Claudio Augusto  
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Lu, Yongtao  
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Gómez, Juan Carlos  
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Stošić, Borko  
dc.contributor.author
Stošić, Tatijana  
dc.date.available
2018-04-26T19:18:03Z  
dc.date.issued
2017-12  
dc.identifier.citation
Baravalle, Rodrigo Guillermo; Thomsen, Felix Sebastian Leo; Delrieux, Claudio Augusto; Lu, Yongtao; Gómez, Juan Carlos; et al.; Three-dimensional multifractal analysis of trabecular bone under clinical computed tomography; American Association of Physicists in Medicine; Medical Physics; 44; 12; 12-2017; 6404-6412  
dc.identifier.issn
0094-2405  
dc.identifier.uri
http://hdl.handle.net/11336/43581  
dc.description.abstract
Purpose: An adequate understanding of bone structural properties is critical for predicting fragility conditions caused by diseases such as osteoporosis, and in gauging the success of fracture prevention treatments. In this work we aim to develop multiresolution image analysis techniques to extrapolate high-resolution images predictive power to images taken in clinical conditions. Methods: We performed multifractal analysis (MFA) on a set of 17 ex vivo human vertebrae clinical CT scans. The vertebræ failure loads (FFailure) were experimentally measured. We combined bone mineral density (BMD) with different multifractal dimensions, and BMD with multiresolution statistics (e.g., skewness, kurtosis) of MFA curves, to obtain linear models to predict FFailure. Furthermore we obtained short- and long-term precisions from simulated in vivo scans, using a clinical CT scanner. Ground-truth data - high-resolution images - were obtained with a High-Resolution Peripheral Quantitative Computed Tomography (HRpQCT) scanner. Results: At the same level of detail, BMD combined with traditional multifractal descriptors (Lipschitz-Hölder exponents), and BMD with monofractal features showed similar prediction powers in predicting FFailure (87%, adj. R2). However, at different levels of details, the prediction power of BMD with multifractal features raises to 92% (adj. R2) of FFailure. Our main finding is that a simpler but slightly less accurate model, combining BMD and the skewness of the resulting multifractal curves, predicts 90% (adj. R2) of FFailure. Conclusions: Compared to monofractal and standard bone measures, multifractal analysis captured key insights in the conditions leading to FFailure. Instead of raw multifractal descriptors, the statistics of multifractal curves can be used in several other contexts, facilitating further research.  
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-nc-nd/2.5/ar/  
dc.subject
Bone  
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Failure Load  
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Multifractal  
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Three-Dimensional Multifractal Analysis  
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Ciencias de la Computación  
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Ciencias de la Computación e Información  
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CIENCIAS NATURALES Y EXACTAS  
dc.title
Three-dimensional multifractal analysis of trabecular bone under clinical 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
2018-04-16T17:43:02Z  
dc.journal.volume
44  
dc.journal.number
12  
dc.journal.pagination
6404-6412  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
Nueva York  
dc.description.fil
Fil: Baravalle, Rodrigo Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina  
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Fil: Thomsen, Felix Sebastian Leo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina  
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Fil: Delrieux, Claudio Augusto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
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Fil: Lu, Yongtao. Dalian University of Technology; China  
dc.description.fil
Fil: Gómez, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina  
dc.description.fil
Fil: Stošić, Borko. Universidade Federal Rural Pernambuco; Brasil  
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Fil: Stošić, Tatijana. Universidade Federal Rural Pernambuco; Brasil  
dc.journal.title
Medical Physics  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1002/mp.12603  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.12603