有限投影数据CT图像迭代重建技术研究

发布时间：2017-12-30 20:38

  本文关键词：有限投影数据CT图像迭代重建技术研究　出处：《南方医科大学》2017年博士论文　论文类型：学位论文

  更多相关文章： CT成像 稀疏角度 有限角度 全变差 非局部平均 迭代初始图像

【摘要】：计算机断层成像(Computed Tomography,CT)中,过高的X射线剂量危害人体。增大间隔采样投影数据可以达到降低剂量的目的,但不再满足经典FBP解析精确重建条件,而迭代重建方法表现出它的优势。Sidky等人提出了 TV最小化约束ART重建图像的算法(简称ART-TV),但TV算法存在的问题是,重建图像会出现阶梯伪影。另外,非局部平均(NLM)滤波算法去噪效果优于TV,前人将NLM滤波融入在CT重建中,取得优于ART-TV的重建结果。少于180°扫描范围的有限角度成像也是一种降低剂量手段。FBP算法重建图像含有明显条形伪影,并不均匀分布在图像中。迭代方法可以重建出更好质量图像,但仍存在不足。尚无NLM算法在有限角度CT重建成功应用的报道。本文研究工作包括以下四个方面:第一,改进并实现一种快速优质FBP重建算法。全角度CT重建中,考虑投影数据中零数据代表对应的X射线只穿过空气,因此,在对每个重建像素进行反投影操作时,依次判断重建像素点对应的探测器线积分投影数据是否为零,若是,直接对像素点置零,不必进行剩余反投影操作。实验表明,算法速度更快,空气像素值更准。有限角度CT中,若物体轴对称,利用FBP重建图像中无伪影的物体边缘确定和修补另一对侧结构信息,此确定对侧边缘过程用到以上所提算法,根据对称轴找到对侧轮廓并对对称轮廓信息进行恢复。优化的FBP图像作为ART-TV算法初始图像,实验表明,迭代收敛速度更快。第二,改进并实现一种ART-NLM算法用于有限角度CT重建。根据局部伪影在图像中的位置,判断重建像素位于哪一区域,若位于伪影区域,则利用其对称象限内的像素进行NLM滤波,否则利用重建像素周围的像素进行NLM滤波,有效恢复伪影区域内的正常信息。如果物体对称关系不好,则在后续迭代重建中改用ART-TV有效避免伪像产生。实验结果表明,提出的ART-NLM/TV在伪影消除方面效果更佳。第三,改进并实现一种ART-ATpV算法用于稀疏角度CT重建。提出基于自适应TpV的迭代重建算法,改进算法为梯度图像的p(0≤p≤l)范数,根据每个重建像素的属性确定自身p值。当像素点位于均匀区域时,p接近1,有效去除噪声,而当像素点位于边缘区域,p接近0,有效保护边缘结构信息。实验结果表明,新方法可以有效去除噪声的同时最大程度保护边缘结构信息。第四,改进并实现一种ART-TV/SL0算法用于稀疏角度CT图像迭代重建。提出基于TV结合平滑L0范数(Smooth L0,SL0)的迭代重建算法,即,ART重建和非负约束后,进行TV最小化优化图像,之后采用梯度图像的SL0最小化进一步去除噪声和伪影。实验结果证明,改进算法平衡了噪声和伪影去除以及结构边缘信息保护。
[Abstract]:In computed Tomography (CTT), too high X-ray dose is harmful to human body. Increasing the interval sampling projection data can achieve the purpose of reducing the dose. But it no longer satisfies the classical FBP analytical exact reconstruction condition. The iterative reconstruction method shows its advantages. Sidky et al. put forward a TV minimization constrained ART image reconstruction algorithm (referred to as ART-TVT), but the problem of TV algorithm is. Step artifacts appear in reconstructed images. In addition, the non-local average NLM filter algorithm is better than TV-based filtering algorithm in denoising effect. Previous researchers incorporated NLM filter into CT reconstruction. The finite angle imaging with less than 180 掳scanning range is also a method of reducing dose. The iterative method can reconstruct the image with better quality. However, there are still some shortcomings. There is no report on the successful application of NLM algorithm in finite angle CT reconstruction. The research work in this paper includes the following four aspects: first. Improve and implement a fast and high quality FBP reconstruction algorithm. In full angle CT reconstruction, the zero data in projection data represent the corresponding X-ray only through the air, so. When each reconstructed pixel is backprojected, the detector line integral projection data corresponding to the reconstructed pixel point is judged to be zero or not, and if so, the pixel point is directly set to zero. The experiments show that the algorithm is faster and the air pixel value is more accurate. In finite angle CT, if the object is axisymmetric. Using FBP to reconstruct the edge of the object without artifacts in the image to determine and repair another pair of side structure information, this determination of the opposite edge of the process using the above algorithm. According to the symmetry axis to find the opposite contour and restore the symmetrical contour information. The optimized FBP image as the initial image of the ART-TV algorithm, the experimental results show that the iterative convergence speed is faster. Second. A ART-NLM algorithm is improved and implemented for finite angle CT reconstruction. According to the location of local artifacts in the image, the reconstruction pixels are located in which region, if located in the artifact region. Then the pixels in the symmetric quadrant are used for NLM filtering, otherwise, the pixels around the reconstructed pixels are used for NLM filtering, which can effectively restore the normal information in the artifact region, if the symmetry relation of the object is not good. The experimental results show that the proposed ART-NLM/TV is more effective in artifact elimination. An improved and implemented ART-ATpV algorithm for sparse angle CT reconstruction is proposed. An iterative reconstruction algorithm based on adaptive TpV is proposed. The improved algorithm is the p0 鈮，

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