Probabilistic model of bone structure based on CT scan data and validation of simulation results using the Finite Cell Method

Luque, Jesus; Papaioannou, Iason; Straub, Daniel; Ruess, Martin; Schillinger, Dominik

doi:10.1201/b16387-294

Cited by 2 publications

(2 citation statements)

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“…Another study showed that trabecular structure can be generated by an inverse Monte Carlo simulation on Voronoï cells, which exhibited a good match with trabecular morphology [17]. In the study by Luque et al [18], a density random field of a trabecular region of interest (ROI) was modeled with directionally separable autocorrelation functions based on computer tomography (CT). So far, this study by Luque et al [18] can be considered the first and also only one that considers density as a random field.…”

Section: Introductionmentioning

confidence: 99%

“…In the study by Luque et al [18], a density random field of a trabecular region of interest (ROI) was modeled with directionally separable autocorrelation functions based on computer tomography (CT). So far, this study by Luque et al [18] can be considered the first and also only one that considers density as a random field. However, the conclusions in their study are difficult to generalize to the whole bone because they were derived from a bone sample of small size under stationarity conditions.…”

Section: Introductionmentioning

confidence: 99%

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Bone mineral density modeling via random field: normality, stationarity, sex and age dependence

Henyš

Vořechovský

Kuchař

et al. 2021

Preprint

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Population variability and correlations in bone mineral density can be described by a spatial random field, which can be inferred from the routine computed tomography (CT) data. Random fields were simulated by transforming pairwise uncorrelated Gaussian random variables into correlated variables through the spectral decomposition of age-detrended correlation matrix estimated from CT. The validity of random field model was demonstrated on spatio-temporal analysis of bone mineral density and bone mineral content. The similarity of CT samples and that generated from random fields was analyzed with energy distance metric. It was found that the bone mineral density random field was approximately Gaussian/slightly left-skewed/strongly right-skewed in various locations. However, bone mineral content could be well simulated with the proposed Gaussian random field and that of energy distance, i.e., a measure to quantify discrepancies between two distribution functions, is convergent with respect to the number of correlation eigenpairs. The proposed random field allows for enriching computational biomechanical models with variability in bone mineral density, which could increase the model usability and provide a step forward in digital twin paradigm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%