Image Reconstruction For Sparse Angular And Low-Dose Computed Tomography

In the recent years, X-Ray Computed tomography has been widely applied in clinical domain, especially in the field of diagnosis and treatment. CT has higher resolution than other imaging methods, such as X-ray machine. And CT is an outstanding representative of modern imaging. However CT used a higher dose for improving the image quality. So it makes more and more focus on the potential radiation harm to human. The major companies, including GE, Toshiba and Philips, promoted new CT with dose controlling technology to reduce the radiation injury for patients, in recently. Limiting the tube current and reducing the sampling view can aim to reduce the radiation dose. But, at the same time, these ways introduce many of noise into the projection data and make a serious degradation of the image, which contains many of streak artifacts. So, minimizing x-ray exposure to the patients and improving the image has been one of the major efforts in the CT fields.Recently, with the rapid development of hardware, a number of new algorithms and ideas appear from the field of image denoising and restoration, and this is provided new opportunities to improve the quality of low-dose CT images and sparse angular CT images. In this article, we will review the field of image denoising and make a sense of the current non-local, multi-scale algorithm, then apply the new ideas to the field of sparse angular CT image reconstruction and low-dose CT image reconstruction.The work we do on the reconstruction algorithms of sparse angular CT image and low-dose CT image are as following: (1) Based on the fact that nonlocal means (NL-means) filtered image can likely produce an acceptable priori solution, in this paper, a sparse angular CT projection onto convex set (POCS) reconstruction using NL-means iterative modified is proposed. The new reconstruction scheme consists of two components:POCS and NL-means filter. In each phase of the sparse angular CT iterative reconstruction, we first use POCS algorithm to meet the identity and non-negativity of projection data, then we perform NL-means filter to the image obtained by POCS method for image quality improvement. Simulation experiments show that the proposed POCS scheme can significantly improve the quality of sparse angular CT image in suppressing the noise and removing the streak-artifacts.(2) To improve the quality of low-dose computed tomography (CT) image, a novel projection data recovery induced non-local means for low-dose CT reconstruction is proposed. The presented method can take the advantages of data recovery methods in two domains (projection domain and image domain). Specially, the projection data is first transformed from Poisson distribution to Gaussian distribution using the nonlinear Anscombe transform in order to easily filter the noise of projection data. Second, after Anscombe transformed data is filtered, Anscombe inverse transform is performed, and the reconstructed image is achieved using the classical filtered back projection (FBP) method from filtered projection data. Last, non-local means (NL-means) weights of FBP image are computed from the restored projection data to induce the NL-means filtering of directly reconstructed FBP image from the un-restored projection data. Simulated and clinical experimental results demonstrate that the proposed method performs very well in lowering the noise and preserving the image edge.