Evidence map›Paper›PMID 23635290›Full record

ArticleMedical physics2013

Potential of in vivo MRI-based nonlinear finite-element analysis for the assessment of trabecular bone post-yield properties.

Ning Zhang, Jeremy F Magland, Chamith S Rajapakse, Yusuf A Bhagat, Felix W Wehrli

Abstract read
In one paragraph

Article in Medical physics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Continuum finite element analysis generalizesBiomedical physics & engineering express · 2023
    Article
  4. Review
  5. Survey of MRI Usefulness for the Clinical Assessment of Bone Microstructure.International journal of molecular sciences · 2021
    Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. MRI assessment of bone structure and microarchitecture.Journal of magnetic resonance imaging : JMRI · 2017
    Review
  11. Advances in imaging approaches to fracture risk evaluation.Translational research : the journal of laboratory and clinical medicine · 2017
    Review
  12. Review
  13. Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ning ZhangLaboratory for Structural NMR Imaging, Department of Radiology, University of Pennsylvania Medical Center, 3400 Spruce Street, Philadelphia, Pennsylvania 19104, USA.
Jeremy F Magland
Chamith S Rajapakse
Yusuf A Bhagat
Felix W Wehrli

Funding

Structural MRI of trabecular bone for therapy response monitoringR01AR053156 · NIAMS · UNIVERSITY OF PENNSYLVANIA · PI WEHRLI, FELIX W · 2005 to 2009
$3.6M
Osteoporosis Treatment Response Assessed by Micromechanical Modeling of MRI DataR01AR055647 · NIAMS · UNIVERSITY OF PENNSYLVANIA · PI WEHRLI, FELIX W · 2007 to 2016
$3.6M
Computational Biomechanics for Prediction of Osteoporotic Vertebral Fracture RiskK25AR060283 · NIAMS · UNIVERSITY OF PENNSYLVANIA · PI RAJAPAKSE, CHAMITH SUDESH · 2011 to 2014
$491k
NIAMS NIH HHS K25 AR 060283NIAMS NIH HHS K25 AR060283NIAMS NIH HHS R01 AR053156NIAMS NIH HHS R01 AR055647NIAMS NIH HHS R01 AR53156NIAMS NIH HHS R01 AR55647
6 · The paper itself

Abstract

purposeBone strength is the key factor impacting fracture risk. Assessment of bone strength from high-resolution (HR) images have largely relied on linear micro-finite element analysis (μFEA) even though failure always occurs beyond the yield point, which is outside the linear regime. Nonlinear μFEA may therefore be more informative in predicting failure behavior. However, existing nonlinear models applied to trabecular bone (TB) have largely been confined to micro-computed tomography (μCT) and, more recently, HR peripheral quantitative computed tomography (HR-pQCT) images, and typically have ignored evaluation of the post-yield behavior. The primary purpose of this work was threefold: (1) to provide an improved algorithm and program to assess TB yield as well as post-yield properties; (2) to explore the potential benefits of nonlinear μFEA beyond its linear counterpart; and (3) to assess the feasibility and practicality of performing nonlinear analysis on desktop computers on the basis of micro-magnetic resonance (μMR) images obtained in vivo in patients.

methodsA method for nonlinear μFE modeling of TB yield as well as post-yield behavior has been designed where material nonlinearity is captured by adjusting the tissue modulus iteratively according to the tissue-level effective strain obtained from linear analysis using a computationally optimized algorithm. The software allows for images at in vivo μMRI resolution as input with retention of grayscale information. Associations between axial stiffness estimated from linear analysis and yield as well as post-yield parameters from nonlinear analysis were investigated from in vivo μMR images of the distal tibia (N = 20; ages: 58-84) and radius (N = 20; ages: 50-75).

resultsAll simulations were completed in 1 h or less for 61 strain levels using a desktop computer (dual quad-core Xeon 3.16 GHz CPUs equipped with 40 GB of RAM). Although yield stress and ultimate stress correlated strongly (R(2) > 0.95, p < 0.001) with axial stiffness, toughness correlated moderately at the distal tibia (R(2) = 0.81, p < 0.001) and only weakly at the distal radius (R(2) = 0.34, p = 0.007). Further, toughness was found to vary by up to 16% for bone of very similar axial stiffness (<2%).

conclusionsThe work demonstrates the practicality of nonlinear μFE simulations at in vivo μMRI resolution, as well as its potential for providing additional information beyond that obtainable from linear analysis. The data suggest that a direct assessment of toughness may provide information not captured by stiffness.

Indexed as

Finite Element AnalysisImage Processing, Computer-AssistedMagnetic Resonance ImagingAgedAged, 80 and overAlgorithmsBiomechanical PhenomenaFemaleHumansLinear ModelsMiddle AgedStress, MechanicalTibia

Identifiers

PMID23635290
PMCPMC3651211

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.