基于正弦图分区修复的稀疏角度CT重建算法

针对稀疏投影CT重建图像中的条形伪影问题,提出一种稀疏表示与低秩矩阵填充相结合的正弦图分区修复方法.首先,将正弦图子块依据灰度熵大小分为两类;然后,采用字典学习算法修复边界区域的正弦图子块,为了保留正弦图的内部结构,设计一种联合修复模型用于内部子块的修复,将正弦图的低秩特性融入稀疏表示模型中,以便引入非局部信息;最后,组成完整的正弦图并经滤波反投影(FBP)重建获得最终图像.实验结果表明,与经典算法相比,该算法在投影域与图像域皆有较优表现,能够较好地修复正弦图的结构,明显改善稀疏重建图像中的条形伪影及结构模糊问题.

Sparse-view CT reconstruction algorithm based on sinogram divisional inpainting

Aiming at the problem of streak artifacts due to sparse projections in CT reconstructed images, a sinogram inpainting method combining sparse representation and low-rank matrix filling was proposed. Firstly, the sinogram sub-blocks were divided into two categories according to their gray entropy. After that the dictionary learning algorithm was used to repair sinogram sub-blocks at the boundary. In order to preserve the internal structure of the sinogram, a combined inpainting model was designed to repair the internal sub-blocks, and the low-rank characteristics of the sinogram were incorporated into the sparse representation model so as to introduce the non-local information. Finally, the repaired sub-blocks were composed into a complete sinogram, and then the final image was reconstructed by the filtered back projection (FBP) algorithm. Experimental results show that compared with the classical algorithms, the proposed algorithm can achieve better performance in both the projection domain and the image domain. It can attain superior sinogram inpainting effect, lead to significant streak artifacts suppression and structure burring improvement of the sparse-view reconstructed image.