Dominic Branchaud

Surface reconstruction from planar x-ray images using moving least squares

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.

Coupling 2D/3D registration method and statistical model to perform 3D reconstruction from partial x-rays images data

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.

3D shape reconstruction of bone from two x-ray images using 2D/3D non-rigid registration based on moving least-squares deformation

Several studies based on biplanar radiography technologies are foreseen as great systems for 3D-reconstruction applications for medical diagnoses. This paper proposes a non-rigid registration method to estimate a 3D personalized shape of bone models from two planar x-ray images using an as-rigid-as-possible deformation approach based on a moving least-squares optimization method. Based on interactive deformation methods, the proposed technique has the ability to let a user improve readily and with simplicity a 3D reconstruction which is an important step in clinical applications. Experimental evaluations of six anatomical femur specimens demonstrate good performances of the proposed approach in terms of accuracy and robustness when compared to CT-scan.

Fast 3D reconstruction of the spine from biplanar radiography: a diagnosis tool for routine scoliosis diagnosis and research in biomechanics

Reconstruction methods from biplanar radiography allow a 3D clinical analysis, for patients in standing position, with a low radiation dose. This low-dose and postural imaging modality is thus very interesting for scoliosis clinical diagnosis and research in biomechanics. Nevertheless, such applications require both accurate and fast 3D reconstruction methods. Fast approaches, based on statistical models (Pomero et al. 2004; Gille et al. 2007), allow to obtain an estimate of the spinal 3D reconstruction within 14min. However, this remains too tedious to be used in a clinical routine. The purpose of this study is to propose and evaluate a novel semi-automated 3D reconstruction method of the spine from biplanar radiography. This method relies on a parametric model of the spine using statistical inferences and automatic registration methods based on image processing. Two reconstruction levels are proposed: a first reconstruction level (‘Fast Spine’), providing a fast estimate of the 3D reconstruction and accurate clinical measurements, dedicated to a routine clinical use, and a more accurate second reconstruction level (‘Full Spine’) for applications in biomechanical research.