Reconstruction from truncated projections in cone-beam CT using an efficient 1D filtering
Reconstruction from truncated projections in cone-beam CT using an efficient 1D filtering
复制标题
使用高效的一维滤波从锥束 CT 中的截断投影进行重建
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
J. Hornegger
中科院分区：
文献类型：
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作者：
Yan Xia;A. Maier;H. Hofmann;F. Dennerlein;K. Mueller;J. Hornegger
在X射线成像中，视场(FOV)的减小与辐射剂量的减小成比例。然而，由此产生的截断与传统的层析重建算法不兼容。这个问题已经得到了广泛的研究。最近，一种既不使用先验知识也不需要显式外推的截断投影重建感兴趣区域(ROI)的新方法被提出，称为用于计算机层析成像的近似截断稳健算法(ATRACT)。它基于将标准斜坡滤波器分解为2D拉普拉斯滤波(局部运算)和基于2D Radon的滤波步骤(非局部运算)。二维Radon滤波涉及多次内插，使ATRACT的滤波过程变得复杂，这在本质上限制了它的实用性。本文针对这一缺陷提出了一种优化方案。也就是说，将ATRACT应用于一维，这意味着我们将标准斜坡滤波器分解为一维拉普拉斯滤波器和一维基于卷积的滤波器。通过计算标准斜坡滤波的一维冲激响应，结合二阶反导数运算，数值确定了卷积核。使用重建基准测试、真实体模和临床数据集对所提出的算法进行了空间分辨率、计算效率和校正质量的稳健性评估。评价结果令人鼓舞。与2D ATRACT算法相比，所提出的算法在计算性能上有所改善，并且在存在数据截断的情况下保持了高精度的重建。
In X-ray imaging, a reduction of the field of view (FOV) is proportional to a reduction in radiation dose. The resulting truncation, however, is incompatible with conventional tomographic reconstruction algorithms. This problem has been studied extensively. Very recently, a novel method for region of interest (ROI) reconstruction from truncated projections with neither the use of prior knowledge nor explicit extrapolation has been published, named Approximated Truncation Robust Algorithm for Computed Tomography (ATRACT). It is based on a decomposition of the standard ramp filter into a 2D Laplace filtering (local operation) and a 2D Radon-based filtering step (non-local operation). The 2D Radon-based filtering that involves many interpolations complicates the filtering procedure in ATRACT, which essentially limits its practicality. In this paper, an optimization for this shortcoming is presented. That is to apply ATRACT in one dimension, which implies that we decompose the standard ramp filter into the 1D Laplace filter and a 1D convolutionbased filter. The convolution kernel was determined numerically by computing the 1D impulse response of the standard ramp filtering coupled with the second order anti-derivative operation. The proposed algorithm was evaluated by using a reconstruction benchmark test, a real phantom and a clinical data set in terms of spatial resolution, computational efficiency as well as robustness of correction quality. The evaluation outcomes were encouraging. The proposed algorithm showed improvement in computational performance with respect to the 2D ATRACT algorithm and furthermore maintained reconstructions of high accuracy in presence of data truncation.