3D reconstruction of rib cage geometry from biplanar radiographs using a statistical parametric model approach

Abstract

Rib cage 3D reconstruction is an important prerequisite for thoracic spine modelling, particularly for studies of the deformed thorax in adolescent idiopathic scoliosis. This study proposes a new method for rib cage 3D reconstruction from biplanar radiographs, using a statistical parametric model approach. Simplified parametric models were defined at the hierarchical levels of rib cage surface, rib midline and rib surface, and applied on a database of 86 trunks. The resulting parameter database served to train statistical models which were used to quickly provide a first estimate of the reconstruction from identifications on both radiographs. This solution was then refined by manual adjustments in order to improve the matching between model and image. Accuracy was assessed by comparison with 29 rib cages from CT scans in terms of geometrical parameter differences and in terms of line-to-line error distance between the rib midlines. Intra and inter-observer reproducibility was determined for 20 scoliotic patients. The first estimate (mean reconstruction time of 2 min 30 s) was sufficient to extract the main rib cage global parameters with a 95% confidence interval lower than 7%, 8%, 2% and 4° for rib cage volume, antero-posterior and lateral maximal diameters and maximal rib hump, respectively. The mean error distance was 5.4 mm (max 35 mm) down to 3.6 mm (max 24 mm) after the manual adjustment step (3 min 30 s). The proposed method will improve developments of rib cage finite element modelling and evaluation of clinical outcomes.

Keywords::

• 3D reconstruction
• human rib cage
• adolescent idiopathic scoliosis
• clinical measurements
• statistical parametric model
• stereoradiography

Acknowledgements

This work was funded by Paris Tech BiomecAM chair on subject-specific muscular skeletal modelling, and the authors thank the chair founders: Cotrel foundation, Société Générale, Protéor Company and COVEA consortium. The authors also thank Alina Badina for medical imaging data, Alexandre Journé for his advices and Thomas Joubert for his technical support.