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Comparison of morphing techniques to develop subject-specific finite element models of vertebrae.

Rafael I RubensteinMitesh LalwalaKaran DevaneBharath KoyaBahram KianiAshley A Weaver
Published in: Computer methods in biomechanics and biomedical engineering (2022)
This study compared two morphing techniques (and their serial combination) to create subject-specific finite element models of 15 astronaut vertebrae. Surface deviations of the morphed models were compared against subject geometries extracted from medical images. The optimal morphing process yielded models with minimal difference in root-mean-square (RMS) deviation (C3, 0.52 ± 0.14 mm; T3, 0.34 ± 0.04 mm; L1, 0.59 ± 0.16 mm) of the subject's vertebral geometry. <1% of model elements failed quality checks and compression simulations ran to completion. This research lays the foundation for the development of subject-specific finite element models to quantify musculoskeletal changes and injury risk from spaceflight.
Keyphrases
  • finite element
  • healthcare
  • deep learning
  • machine learning
  • convolutional neural network
  • quality improvement
  • breast cancer risk