This paper presents a unit free-form deformation (FFD) method applied to rapid three-dimensioanl (3D) bone reconstruction, which was based on traditional FFD. With the femur as an example, we reconstructed a 3D model of femur from two X-ray images and a standardized model by taking advantage of unit FFD algorithm. The X-ray images and its parameters were taken by C-arm device. Those parameters and X-ray contour are contributed to 3D reconstruction. The out contours of X-ray image and standard model were connected by point matching algorithm. The unit-FFD lattice was built to reconstruct standard model and finally made the contour of X-ray image and standard model exactly the same. Experiments on shape accuracy, robustness and time consuming, carried out by 35 specimen from cadaver, showed that mean error of shape (0.52 mm) and mean construction time (112 s) were lower than those using traditional method (0.7-2.6 mm, 8-20 min). The method proposed in this paper shows a good prospect in clinical application and related research.
Considering the survival rate of small animals and the continuity of the experiments, high-dose X-ray shooting process is not suitable for the small animals in computed tomography (CT) experiments. But the low-dose process results with images might be polluted by noises which are not conducive for the experiments. In order to solve this problem, we in this paper introduce a global dictionary learning based denoising method to apply the promotion of the low dose CT image. We at first adopted the K-means singular value decomposition (K-SVD) algorithm to train a global dictionary based on the high dose CT image. Then, the noise image could be decomposed into sparse component which was free from noise through the orthogonal matching pursuit (OMP) algorithm. Finally, the noise-free image could be achieved by reconstructing the image only with its sparse components. The experiments results showed that the method we proposed here could decrease the noise efficiently and remain the details, and it would help promote the low dose image quality and increase the survival rate of the small animals.