Automatic assessment of scoliosis surgery outcome on trunk shape using left-right trunk asymmetry.

PURPOSE: To present a novel set of Left-Right Trunk Asymmetry (LRTA) indices and use them to assess the postoperative appearance of the trunk in Adolescent Idiopathic Scoliosis (AIS) patients. METHODS: We hypothesize that LRTA measurements provide complementary information to existing trunk asymmetry indices when documenting the outcome of scoliosis surgery. Forty-nine AIS patients with thoracic curves who underwent posterior spinal fusion were included. All had surface topography scans taken preoperatively and at least 6 months postoperatively. We documented spinal curvature using Radiographic Cobb angles, scoliometer readings and coronal balance. To evaluate Global Trunk Asymmetry (GTA), we used the standard measures of Back Surface Rotation (BSR) and Trunk Lateral Shift (TLS). To measure LRTA, we identified asymmetry areas as regions of significant deviation between the left and right sides of the 3D back surface. New parameters called Deformation Rate (DR) and Maximum Asymmetry (MA) were measured in different regions based on the asymmetry areas. We compared the GTA and LRTA changes with those in spinal curvature before and after surgery. RESULTS: The GTA indices, mainly TLS, showed improvement for more than 75% of patients. There was significant improvement of LRTA in the shoulder blades and waist regions (95% and 80% of patients respectively). CONCLUSION: We report positive outcomes for LRTA in the majority of patients, specifically in the shoulder blades and waist, even when no reduction of BSR is observed. The proposed indices can evaluate local trunk asymmetries and the degree to which they are improved or worsened after scoliosis surgery.

Patient-specific anisotropic model of human trunk based on MR data.

There are many ways to generate geometrical models for numerical simulation, and most of them start with a segmentation step to extract the boundaries of the regions of interest. This paper presents an algorithm to generate a patient-specific three-dimensional geometric model, based on a tetrahedral mesh, without an initial extraction of contours from the volumetric data. Using the information directly available in the data, such as gray levels, we built a metric to drive a mesh adaptation process. The metric is used to specify the size and orientation of the tetrahedral elements everywhere in the mesh. Our method, which produces anisotropic meshes, gives good results with synthetic and real MRI data. The resulting model quality has been evaluated qualitatively and quantitatively by comparing it with an analytical solution and with a segmentation made by an expert. Results show that our method gives, in 90% of the cases, as good or better meshes as a similar isotropic method, based on the accuracy of the volume reconstruction for a given mesh size. Moreover, a comparison of the Hausdorff distances between adapted meshes of both methods and ground-truth volumes shows that our method decreases reconstruction errors faster.