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13 March 2009 3D reconstruction of the human spine from radiograph(s) using a multi-body statistical model
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Abstract
Three-dimensional models of the spine are very important in diagnosing, assessing, and studying spinal deformities. These models are generally computed using multi-planar radiography, since it minimizes the radiation dose delivered to patients and allows them to assume a natural standing position during image acquisition. However, conventional reconstruction methods require at a minimum two sufficiently distant radiographs (e.g., posterior-anterior and lateral radiographs) to compute a satisfactory model. Still, it is possible to expand the applicability of 3D reconstructions by using a statistical model of the entire spine shape. In this paper, we describe a reconstruction method that takes advantage of a multi-body statistical model to reconstruct 3D spine models. This method can be applied to reconstruct a 3D model from any number of radiographs and can also integrate prior knowledge about spine length or preexisting vertebral models. Radiographs obtained from a group of 37 scoliotic patients were used to validate the proposed reconstruction method using a single posterior-anterior radiograph. Moreover, we present simulation results where 3D reconstructions obtained from two radiographs using the proposed method and using the direct linear transform method are compared. Results indicate that it is possible to reconstruct 3D spine models from a single radiograph, and that its accuracy is improved by the addition of constraints, such as a prior knowledge of spine length or of the vertebral anatomy. Results also indicate that the proposed method can improve the accuracy of 3D spine models computed from two radiographs.
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Jonathan Boisvert, Farida Cheriet, Xavier Pennec, and Nicholas Ayache "3D reconstruction of the human spine from radiograph(s) using a multi-body statistical model", Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 72612D (13 March 2009); https://doi.org/10.1117/12.813580
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