The CT Image Reconstruction Algorithms Based On Nonlinear Diffusion

Image reconstruction is widely used in the detection and observation of the internal structure of the object. It will not cause any physical damage to the object but can obtain the image of the internal structure of the object, and CT technology is a more typical application.CT image reconstruction is the core of CT technology, the quality of the reconstruction algorithm is directly related to the accuracy of the detection results. Due to the presence of noise in the projection data, the process of image reconstruction will be subject to noise interference and the image quality is affected as a result, which makes feature extraction,edge detection, image segmentation, compression and coding difficult to analyze in the practical application. Therefore, image denoising is required to improve the quality of image reconstruction.In this paper, we firstly expounds the principle of CT imaging, iterative method, analytic method and two methods of objectively evaluating the quality of image reconstruction,compares several iterative algorithms, and shows effects of different reconstruction algorithm by virtue of simulation experiments. Secondly, the paper describe some basic theories of nonlinear diffusion and the application of nonlinear diffusion in image denoising. Based on the original denoising algorithm, we propose a new algorithm and the effectiveness of this algorithm is verified by experiments. At last, we propose a modified SART algorithm to improve the quality of the image reconstruction, which constructs a new diffusion coefficient and improves the denoising algorithm by the PM model, and makes the SART algorithm and PM model alternately according to the denoising principle of PM model. Experimental results demonstrate that the scheme can reduce the effect of noise on image quality substantially,increase the value of SNR and PSNR, and improve the performance of image reconstruction significantly.