Severe axial vertebral rotation treated with a modified Boston brace: a case report

We report the case of a 13-year-old Caucasian girl suffering from severe axial rotation of the T5 to L4 vertebrae. The patient (initially examined during a school screening study) was at first considered to be suspicious of suffering from scoliosis due to a highly positive Adam's forward bending test. However, her radiographic evaluation revealed the existence of axial rotation in 12 of her vertebrae, without inclination in the sagittal and coronal planes. After an observation period of 12 months and due to the fact that both her physical appearance and the measured vertebral rotation deteriorated, the patient was given a modified thoracolumbar Boston brace that had an immediate positive derotational effect on all but two vertebrae. Twenty four months later, the progress of the vertebral rotation(s) seems to have been halted and most affected vertebrae appear to be stabilized in their new, 'post-brace', reduced position, with better results shown when the Boston brace is worn. The patient remains under constant medical observation. The application of a modified Boston brace seems to have served well (so far) a useful purpose for reducing and stabilizing this case of severe axial vertebral rotation, providing less deformity and (possibly) offering a better final cosmetic result

Articulated Statistical Shape Models of the Spine

The spine is a complex assembly of rigid vertebrae surrounded by various soft tissues (ligaments, spinal cord, intervertebral discs, etc.). Its motion for a given individual and its shape variations across a population are greatly influenced by this fact. We show in this chapter how statistical shape models can be constructed, used, and analyzed while taking into account the articulated nature of the spine. We begin by defining what articulated models are and how they can be extracted from existing 3D reconstructions or segmented models. As an example, we use data from scoliotic patients that have been reconstructed in 3D using bi-planar radiographs. Articulated models naturally belong to a manifold where conventional statistical tools are not applicable. In this context, a few key concepts allowing the computation of statistical models on Riemannian manifolds are presented. When properly visualized, the resulting statistical models can be quite useful to analyze and compare the shape variations in different groups of patients. Two different approaches to visualization are demonstrated graphically. Finally, another important use of statistical models in medical imaging is to constrain the solution of inverse problems. Articulated models can readily be used in this context, we illustrate this in the context of 3D model reconstruction using partial data. More precisely, we will show the benefits of integrating a simple regularization term based on articulated statistical models to well known algorithms.Peer reviewed: YesNRC publication: Ye