NDLI: Comparison of geometric torsion in scoliosis under Lenke classification

Content Provider	IEEE Xplore Digital Library
Author	Junhua Zhang Liang Lv
Copyright Year	2013
Description	Author affiliation: Radiol. Dept., First People's Hosp. of Yunnan Province, Kunming, China (Liang Lv) \|\| Electr. Eng. Dept., Yunnan Univ., Kunming, China (Junhua Zhang)
Abstract	The objective of this study is to investigate whether three dimensional (3D) variability exists within the Lenke classification, and to evaluate the correlations between the 3D features and the Cobb angle used in the Lenke classification. Forty-nine scoliotic patients with Lenke Type 1 curve were selected for analysis. For each patient, the 3D spine model was reconstructed from biplanar radiographs, and the geometric torsion was then calculated from the reconstructed spine model. An analysis of variance (ANOVA) was performed regarding the average torsion, the maximum torsion, and the Cobb angle, with the patients subdivided according to the torsion pattern. Results showed that a statistically significant difference was observed for the torsion parameters (i.e., the average torsion and the maximum torsion) between subgroups within the Lenke Type 1 curves while no statistically significant difference was found regarding the Cobb angle. The strengths of correlations between the torsion parameters and Cobb angle were stronger in the subgroup with torsion pattern of Type A. These results add the evidence that 3D geometric torsion reveals structural differences that are not apparent in the Cobb measurement.
Starting Page	2408
Ending Page	2411
File Size	514989
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610024
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three-dimensional displays Correlation Image reconstruction Solid modeling Diagnostic radiography Analysis of variance
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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