Computationally-optimized bone mechanical modeling from high-resolution structural images.

Image-based mechanical modeling of the complex micro-structure of human bone has shown promise as a non-invasive method for characterizing bone strength and fracture risk in vivo. In particular, elastic moduli obtained from image-derived micro-finite element (μFE) simulations have been shown to corr...

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Autores principales:	Jeremy F Magland, Ning Zhang, Chamith S Rajapakse, Felix W Wehrli
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b0cd378633784362b6d291ae37198970
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spelling	oai:doaj.org-article:b0cd378633784362b6d291ae371989702021-11-18T07:20:59ZComputationally-optimized bone mechanical modeling from high-resolution structural images.1932-620310.1371/journal.pone.0035525https://doaj.org/article/b0cd378633784362b6d291ae371989702012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22558164/?tool=EBIhttps://doaj.org/toc/1932-6203Image-based mechanical modeling of the complex micro-structure of human bone has shown promise as a non-invasive method for characterizing bone strength and fracture risk in vivo. In particular, elastic moduli obtained from image-derived micro-finite element (μFE) simulations have been shown to correlate well with results obtained by mechanical testing of cadaveric bone. However, most existing large-scale finite-element simulation programs require significant computing resources, which hamper their use in common laboratory and clinical environments. In this work, we theoretically derive and computationally evaluate the resources needed to perform such simulations (in terms of computer memory and computation time), which are dependent on the number of finite elements in the image-derived bone model. A detailed description of our approach is provided, which is specifically optimized for μFE modeling of the complex three-dimensional architecture of trabecular bone. Our implementation includes domain decomposition for parallel computing, a novel stopping criterion, and a system for speeding up convergence by pre-iterating on coarser grids. The performance of the system is demonstrated on a dual quad-core Xeon 3.16 GHz CPUs equipped with 40 GB of RAM. Models of distal tibia derived from 3D in-vivo MR images in a patient comprising 200,000 elements required less than 30 seconds to converge (and 40 MB RAM). To illustrate the system's potential for large-scale μFE simulations, axial stiffness was estimated from high-resolution micro-CT images of a voxel array of 90 million elements comprising the human proximal femur in seven hours CPU time. In conclusion, the system described should enable image-based finite-element bone simulations in practical computation times on high-end desktop computers with applications to laboratory studies and clinical imaging.Jeremy F MaglandNing ZhangChamith S RajapakseFelix W WehrliPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 4, p e35525 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jeremy F Magland Ning Zhang Chamith S Rajapakse Felix W Wehrli Computationally-optimized bone mechanical modeling from high-resolution structural images.
description	Image-based mechanical modeling of the complex micro-structure of human bone has shown promise as a non-invasive method for characterizing bone strength and fracture risk in vivo. In particular, elastic moduli obtained from image-derived micro-finite element (μFE) simulations have been shown to correlate well with results obtained by mechanical testing of cadaveric bone. However, most existing large-scale finite-element simulation programs require significant computing resources, which hamper their use in common laboratory and clinical environments. In this work, we theoretically derive and computationally evaluate the resources needed to perform such simulations (in terms of computer memory and computation time), which are dependent on the number of finite elements in the image-derived bone model. A detailed description of our approach is provided, which is specifically optimized for μFE modeling of the complex three-dimensional architecture of trabecular bone. Our implementation includes domain decomposition for parallel computing, a novel stopping criterion, and a system for speeding up convergence by pre-iterating on coarser grids. The performance of the system is demonstrated on a dual quad-core Xeon 3.16 GHz CPUs equipped with 40 GB of RAM. Models of distal tibia derived from 3D in-vivo MR images in a patient comprising 200,000 elements required less than 30 seconds to converge (and 40 MB RAM). To illustrate the system's potential for large-scale μFE simulations, axial stiffness was estimated from high-resolution micro-CT images of a voxel array of 90 million elements comprising the human proximal femur in seven hours CPU time. In conclusion, the system described should enable image-based finite-element bone simulations in practical computation times on high-end desktop computers with applications to laboratory studies and clinical imaging.
format	article
author	Jeremy F Magland Ning Zhang Chamith S Rajapakse Felix W Wehrli
author_facet	Jeremy F Magland Ning Zhang Chamith S Rajapakse Felix W Wehrli
author_sort	Jeremy F Magland
title	Computationally-optimized bone mechanical modeling from high-resolution structural images.
title_short	Computationally-optimized bone mechanical modeling from high-resolution structural images.
title_full	Computationally-optimized bone mechanical modeling from high-resolution structural images.
title_fullStr	Computationally-optimized bone mechanical modeling from high-resolution structural images.
title_full_unstemmed	Computationally-optimized bone mechanical modeling from high-resolution structural images.
title_sort	computationally-optimized bone mechanical modeling from high-resolution structural images.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/b0cd378633784362b6d291ae37198970
work_keys_str_mv	AT jeremyfmagland computationallyoptimizedbonemechanicalmodelingfromhighresolutionstructuralimages AT ningzhang computationallyoptimizedbonemechanicalmodelingfromhighresolutionstructuralimages AT chamithsrajapakse computationallyoptimizedbonemechanicalmodelingfromhighresolutionstructuralimages AT felixwwehrli computationallyoptimizedbonemechanicalmodelingfromhighresolutionstructuralimages
_version_	1718423619210575872

Computationally-optimized bone mechanical modeling from high-resolution structural images.

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