ABSTRACT
3D reconstructions of the spine from a frontal and sagittal radiographs is extremely challenging. The overlying features of soft tissues and air cavities interfere with image processing. It is also difficult to obtain information that is accurate enough to reconstruct complete 3D models. To overcome these problems, the proposed method efficiently combines the partial information contained in two images from a patient with a statistical 3D spine model generated from a database of scoliotic patients. The algorithm operates through two simultaneous iterating processes. The first one generates a personalized vertebra model using a 2D/3D registration process with bone boundaries extracted from radiographs, while the other one infers the position and the shape of other vertebrae from the current estimation of the registration process using a statistical 3D model. Experimental evaluations have shown good performances of the proposed approach in terms of accuracy and robustness when compared to CT-scan.
Subject(s)
Artificial Intelligence , Imaging, Three-Dimensional/methods , Pattern Recognition, Automated/methods , Radiographic Image Enhancement/methods , Radiographic Image Interpretation, Computer-Assisted/methods , Subtraction Technique , Tomography, X-Ray Computed/methods , Algorithms , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
Planar radiographs still are the gold standard for the measurement of the skeletal weight-bearing shape and posture. In this paper, we propose to use an as-rigid-as-possible deformation approach based on moving least squares to obtain 3D personalized bone models from planar x-ray images. Our prototype implementation is capable of performing interactive rate shape editing. The biplane reconstructions of both femur and vertebrae show a good accuracy when compared to CT-scan.
