Method to geometrically personalize a detailed finite element model of the spine

Volume: 60, Issue:7, Page(s):2014-2021

Nadine M. Lalonde, Yvan Petit, Carl-Eric Aubin, Eric Wagnac, Pierre-Jean Arnoux
Volume: 60, Issue:7, Page(s):2014-2021

July featured story 4

To date, developing geometrically personalized and detailed solid finite element models of the spine remains a challenge, notably due to multiple articulations and complex geometries. To answer this problem, a methodology based on kriging (a free form deformation technique) was developed to deform a detailed reference finite element mesh of the spine (initially developed for general accidentology and biomedical evaluations) to the patient-specific geometry of 10 and 82-year old asymptomatic spines. As opposed to deformation techniques based on contours extracted from CT or MRI-based images, the present method uses control points, and is thus compatible with other imaging technologies generating such data, notably X-ray reconstructions, frequently used with scoliotic patients. Different kriging configurations were tested: with or without smoothing (nugget effect), and control points on or surrounding the entire mesh. Based on the results, it is recommended to use surrounding control points and smoothing. The mean node to surface distance between the deformed and target geometries was 0.3 mm ± 1.1. Most elements met the mesh quality criteria (95%) after deformation, without interference at the articular facets. The method’s novelty lies in the deformation of the entire spine at once, as opposed to deforming each vertebra separately, with surrounding control points and smoothing. This enables the transformation of a reference spine to obtain complete and personalized finite element models with minimal post-processing to optimize the mesh. This method is therefore one of the few that enables the generation of complete personalized solid spinal models including discs, ligaments, and articular facets. The method should be tested with complex spinal curvatures, notably scoliotic patients. Accurate personalized models could be used to develop pre-operative strategies.

 

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