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Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Longwei Fang, Zuowei Wang, Zhiqiang Chen, Fengzeng Jian, Shuo Li and Huiguang He, "3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1124-1133, July 2020. doi: 10.1109/JAS.2019.1911528
Citation: Longwei Fang, Zuowei Wang, Zhiqiang Chen, Fengzeng Jian, Shuo Li and Huiguang He, "3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1124-1133, July 2020. doi: 10.1109/JAS.2019.1911528

3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model

doi: 10.1109/JAS.2019.1911528
Funds:  This work was supported in part by The National Key Research and Development Program of China (2018YFC2001302), the National Natural Science Foundation of China (61976209), CAS International Collaboration Key Project (173211KYSB20190024), and Strategic Priority Research Program of CAS (XDB32040000)
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  • Structure reconstruction of 3D anatomy from bi-planar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh. Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field. Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and 1.2 mm, separately. The average reconstruction time is 3 minutes.


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    • This paper introduces a novel method that use prior model and two x-ray images to reconstruct 3D vertebra.
    • We use the CT data of a vertebra specimen to provide both the shape mesh and the intensity model, and only one prior model used in our method.
    • We combine the elastic-mesh-based and statistical-intensity-model-based methods, which can provide efficient and robust 3D vertebra reconstruction.


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