Shape morphing technique can accurately predict pelvic bone landmarks

An automated landmark method to describe geometric changes in the human mandible during growth

OBJECTIVE

The principal aim of this study was to assess an automatic landmarking approach to human mandibles based on the atlas method. The secondary aim was to identify the areas of greatest variation in the mandibles of middle-aged to older adults.

DESIGN

Our sample consisted of 160 mandibles from computed tomography scans of 80 men and 80 women aged between 40 and 79 years. Eleven anatomical landmarks were placed manually on mandibles. The automated landmarking through point cloud alignment and correspondence (ALPACA) method implemented in 3D Slicer was used to automatically place landmarks to all meshes. Euclidean distances, normalized centroid size, and Procrustes ANOVA were calculated for both methods. A pseudo-landmarks approach was followed using ALPACA to identify areas of changes among our sample.

RESULTS

The ALPACA method showed significant differences in Euclidean distances for all landmarks compared to the manual method. A mean Euclidean distance of 1.7 mm was found for the ALPACA method and 0.99 mm for the manual method. Both methods found that sex, age, and size had a significant effect on mandibular shape. The greatest variations were observed in the condyle, ramus, and symphysis regions.

CONCLUSION

The results obtained using the ALPACA method are acceptable and promising. This approach can automatically place landmarks with an average accuracy of less than 2 mm, which may be sufficient in most anthropometric analyses. In the light of our results, however, odontological application such as occlusal analysis is not recommended.

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

BACKGROUND AND OBJECTIVE

Capturing the population variability of bone properties is of paramount importance to biomedical engineering. The aim of the present paper is to describe variability and correlations in bone mineral density with a spatial random field inferred from routine computed tomography data.

METHODS

Random fields were simulated by transforming pairwise uncorrelated Gaussian random variables into correlated variables through the spectral decomposition of an age-detrended correlation matrix. The validity of the random field model was demonstrated in the spatiotemporal analysis of bone mineral density. The similarity between the computed tomography samples and those generated via random fields was analyzed with the energy distance metric.

RESULTS

The random field of bone mineral density was found to be approximately Gaussian/slightly left-skewed/strongly right-skewed at various locations. However, average bone density could be simulated well with the proposed Gaussian random field for which the energy distance, i.e., a measure that quantifies discrepancies between two distribution functions, is convergent with respect to the number of correlation eigenpairs.

CONCLUSIONS

The proposed random field model allows the enhancement of computational biomechanical models with variability in bone mineral density, which could increase the usability of the model and provides a step forward in in-silico medicine.

Mechanical metric for skeletal biomechanics derived from spectral analysis of stiffness matrix

A new metric for the quantitative and qualitative evaluation of bone stiffness is introduced. It is based on the spectral decomposition of stiffness matrix computed with finite element method. The here proposed metric is defined as an amplitude rescaled eigenvalues of stiffness matrix. The metric contains unique information on the principal stiffness of bone and reflects both bone shape and material properties. The metric was compared with anthropometrical measures and was tested for sex sensitivity on pelvis bone. Further, the smallest stiffness of pelvis was computed under a certain loading condition and analyzed with respect to sex and direction. The metric complements anthropometrical measures and provides a unique information about the smallest bone stiffness independent from the loading configuration and can be easily computed by state-of-the-art subject specified finite element algorithms.