1. A Novel 3D Reconstruction Algorithm of Motion-Blurred CT Image
- Author
-
Sun Yu, Zhang Jing, Guo Qiang, Han Fang, Li Hong-An, and Li Zhanli
- Subjects
Deblurring ,Article Subject ,Computer science ,Computer applications to medicine. Medical informatics ,R858-859.7 ,02 engineering and technology ,Signal-To-Noise Ratio ,General Biochemistry, Genetics and Molecular Biology ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Motion ,0302 clinical medicine ,Signal-to-noise ratio ,Imaging, Three-Dimensional ,Isosurface ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Computer Simulation ,Marching cubes ,General Immunology and Microbiology ,Applied Mathematics ,3D reconstruction ,Liver Neoplasms ,Computational Biology ,General Medicine ,Fatty Liver ,Modeling and Simulation ,Image translation ,Radiographic Image Interpretation, Computer-Assisted ,020201 artificial intelligence & image processing ,Tomography ,Tomography, X-Ray Computed ,Normal ,Algorithm ,Algorithms ,Research Article - Abstract
The majority of medical workers are eager to obtain realistic and real-time CT 3D reconstruction results. However, autonomous or involuntary motion of patients can cause blurring of CT images. For the 3D reconstruction scene of motion-blurred CT image, this paper consists of two parts: firstly, a GAN image translation network deblurring algorithm is proposed to remove blurred results. This algorithm adopts the clear image to supervise the training process of the blurred image, which creates solutions that are close to the clear image. Secondly, this paper proposes a Marching Cubes (MC) algorithm based on the fusion of golden section and isosurface direction smooth (GI-MC) for 3D reconstruction of CT images. The golden section algorithm is used to calculate the equivalent points and normal vectors, which reduces the calculation numbers from four to one. The isosurface direction smooth algorithm computes the mean value of the normal vector, so as to smooth the direction of all triangular patches in spatial arrangement. The experimental results show that for different blurred angle and blurred amplitude, comparing the results of the Shannon entropy ratio and peak signal-to-noise ratio, our GAN image translation network deblurring algorithm has better restoration than other algorithms. Furthermore, for different types of liver patients, the reconstruction accuracy of our GI-MC algorithm is 9.9%, 7.7%, and 3.9% higher than that of the traditional MC algorithm, Li’s algorithm, and Pratomo’s algorithm, respectively.
- Published
- 2020