KEYWORD |
Area Engineering
Quantifying Preoperative Spinal Stiffness in Adolescent Idiopathic Scoliosis
Thesis in external company
Thesis abroad
Reference persons LUIGI MAZZA, ANDREA MURA
Research Groups 04-Automazione e Robotica, 18-Progettazione e sperimentazione di organi di trasmissione
Thesis type DATA ANALYSIS AND STATISTICS, NUMERICAL MODELING, OPTIMIZATION ALGORITHM
Description The aims of this project are:
1) to develop an algorithm for accurately retrieving the 3D anatomy of the spine from biplanar EOS radiographic images.
2) to quantify patient-specific spinal stiffness following spinal traction.
Material and methods. Segmentation of EOS bi-planar X-rays will be conducted using a deep learning model. An algorithm for 2D/3D registration will align the coronal and sagittal segmentations with projections derived from an MRI-based 3D spinal model, enabling the determination of the 3D positions of the vertebras from the 2D radiographic images.
By analyzing the 3D positions of the vertebrae before and after applying traction force, the rotations of each vertebra relative to its neighboring vertebrae will be quantified along the anatomical axes (flexion, rotation, and bending). Numerical models will then be developed to correlate these rotational movements with the applied traction force, enabling the estimation of segmental stiffness for each spinal motion segment.
Deadline 14/01/2026
PROPONI LA TUA CANDIDATURA