3D surface topographic measurements for idiopathic scoliosis are highly correlative to patient self-image questionnaires

PURPOSE

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine that results in external asymmetry of the torso in the shoulder, waist, and rib hump. Several patient reported outcome measures (PROMS) including the Trunk Appearance Perception Scale (TAPS) and SRS-22r self-image domain are used to measure the patient's self-perception. The purpose of this study is to investigate the relationship between objective surface topographic measurements of the torso to subjective patient self-perception.

METHODS

131 AIS subjects and 37 controls participated in this study. All subjects completed TAPS and SRS-22r PROMS followed by whole body 3d surface topographic scanning. An automated analysis pipeline was used to compute 57 measurements. Multivariate linear models were developed to predict TAPS and SRS-22r self-image using each unique combination of 3 parameters and leave one out validation where the best combinations were selected.

RESULTS

Back surface rotation, waist crease vertical asymmetry and rib prominence volume were most predictive of TAPS. The final predicted TAPS values from leave one out cross validation was correlated to ground truth TAPS scores with an R value of 0.65. Back surface rotation, silhouette centroid deviation, and shoulder normal asymmetry were most predictive of SRS-22r self-image with a correlation of R = 0.48.

CONCLUSION

Surface topographic measurements of the torso are correlated to TAPS and SRS-22r self-image scores in AIS patients and controls, with TAPS exhibiting a stronger relationship, better reflecting the patient's external asymmetries.

3D Reconstruction of Scoliotic Spines from Stereoradiography and Depth Imaging

Spine shape can be reconstructed from stereoradiography, but often requires specialized infrastructure or fails to account for subject posture. In this paper a protocol is presented for stereo reconstructions that integrates surface recordings with radiography and naturally accounts for variations in patient posture. Low cost depth cameras are added to an existing radiographic system to capture patient pose. A statistical model of human body shape is learned from public datasets and registered to depth scans, providing 3D correspondence across images for stereo reconstruction of radiographic landmarks. A radiographic phantom was used to validate these methods in vitro with RMS 3D landmark reconstruction error of 2.0 mm. Surfaces were automatically and reliably registered, with SD 12 mm translation disparity and SD .5° rotation. The proposed method is suitable for 3D radiographic reconstructions and may be beneficial in compensating for involuntary patient motion.