Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications

Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications

Simant Prakoonwit (Department of Computer Science and Technology, University of Bedfordshire, Luton, Bedfordshire, UK)
Copyright: © 2014 |Pages: 19
DOI: 10.4018/ijacdt.2014010102
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This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone's edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems.
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2. Background

In an intraoperative environment where the scanning geometry of a CT or MRI is not suitable, a C-arm conventional X-ray system can be used to acquire a number of 2D images to reconstruct a full 3D volumetric description, in terms of voxels, of an object of interest, e.g. (Atesok, Finkelstein et al., 2007; Ritter, Orman et al., 2007; Zbijewski & Stayman 2007). However, to reconstruct at a reasonable resolution, the number of 2D images required is very high, e.g. 40 to 180 images, and to extract the surface of an object from the reconstructed voxels is very computationally expensive. Moreover, due to the large number of 2D X-ray images required, the patient is inevitably subjected to high dose of radiation.

Another approach is to use statistical shape analysis and modelling, e.g. Cootes, Taylor et al. (1995), Dryden and Mardia (1998)Rajamani, Styner et al. (2007), Zheng, Dong et al. (2007), Prakoonwit (2011) and Zhu and Li (2011), which has been an important tool in 3D model reconstruction from incomplete data. In this approach, only a small number of sparse landmark vertices on the surface of an object, e.g. a bone, are needed to be determined. Those sparse landmark vertices alone contain inadequate information for the complete 3D surface reconstruction of an object. Hence, a priori knowledge is required. A statistical model can be reconstructed from a set of training surfaces representing reasonable variations of the surfaces of an object of interest. In intraoperative applications, the statistical model is then used as prior knowledge in the reconstruction process to fit to the patient anatomy using intraoperatively acquired sparse landmark vertices. Thus, in conclusion, the aim of statistical shape model fitting is to extrapolate from an extremely sparse and incomplete set of 3D landmark vertices to a complete and reasonably accurate 3D anatomical surface. The fitting process aligns and deforms the statistical shape model to fit the sparse landmark vertices. Therefore the model-based approach is widely accepted due to their ability to effectively represent objects Morooka, Nakamoto et al. (2013).

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