基于自适应全变差的低剂量X射线图像重建

doi:10.19596/j.cnki.1001-246x.8639

摘要/Abstract

摘要：

针对传统全变差正则化方法对图像方向不敏感的问题, 引入方向梯度信息, 建立一种自适应方向全变差的低剂量稀疏角度X射线计算机断层成像图像的成像模型, 旨在降低辐射剂量的同时提高图像成像质量。为了自适应估计图像梯度的方向信息, 基于图像的几何矩, 利用椭圆可操纵核来估计图像边缘块的主方向。仿真和实际数据结果表明所提算法在图像细节保留、伪影减少、噪声抑制方面具有很好的效果。

关键词: 低剂量, X射线计算机断层成像, 稀疏成像, 方向梯度, 全变差

Abstract:

The radiation dose of X-ray CT is closely related to the abnormal metabolism of the living body and the induction of diseases such as cancer. At this stage, how to reduce the radiation dose of CT scans and ensure the quality of image reconstruction is a huge challenge for CT image reconstruction technology. The traditional total variation regularization method is not sensitive to the direction of the image. This paper introduces directional gradient information and establishes an adaptive directional total variation sparse angle CT image reconstruction model, which aims to better preserve image edges and improve image reconstruction quality. To adaptively estimate the direction information of the image gradient, based on the geometric moment of the image, an ellipse steerable kernel is used to estimate the main direction of the image edge block. Simulation and actual data results show that the proposed algorithm has good performance in image detail preservation, artifact reduction, and noise suppression.

Key words: low-dose, X-ray CT, sparse-view reconstruction, gradient direction, total variation

屈赵燕, 闫喜明, 席晓晶. 基于自适应全变差的低剂量X射线图像重建[J]. 计算物理, 2023, 40(5): 614-621.

Zhaoyan QU, Ximing YAN, Xiaojing XI. Low-dose X-ray CT Image Reconstruction Method Based on Adaptive Total Variation[J]. Chinese Journal of Computational Physics, 2023, 40(5): 614-621.

图/表 9

图1 TV算法成像结果(a)传统TV；(b)TV梯度方向为45°；(c)TV梯度方向为100°；(d)TV梯度方向为130°；(e)TV梯度方向为160°；(f)自适应梯度方向

Fig.1 Reconstruction results of TV algorithm (a) traditional TV; (b) TV gradient directions of 45°; (c) TV gradient directions of 100°; (d) TV gradient directions of 130°; (f) TV gradient directions of 160°; (f) adaptive TV

图2 自适应方向梯度算子

Fig.2 Adaptive gradient difference direction

图3 方向角对house不同区域的图像方向估计(a)house图像；(b)左对角边缘；(c)竖直边缘；(d)右对角边缘；(e)水平边缘；(f)平滑区域

Fig.3 Estimation of the direction of the house image block (a) house image; (b) left diagonal edge; (c) vertical edge; (d) right diagonal edge; (e) horizontal edge; (f) smooth area

图4 方向梯度算子的示意图

Fig.4 Graph of directional gradient operator

图5 医学数据重建结果的比较

Fig.5 Comparison of medical reconstruction results

图6 不同算法的PSNR当稀疏投影个数为(a) 30; (b)60; (c)90

Fig.6 PSNR of four different algorithms with the number of sparse views of (a) 30; (b) 60; (c) 90

图7 模型，灰度值范围为[0, 0.035]

Fig.7 Phantom, grayscale range [0, 0.035]

图8 图 7中蓝线的剖面图当稀疏投影个数为(a)30; (b)60; (c)90

Fig.8 Image profifiles marked by green lines in Fig. 7 with the number of sparse views of (a) 30; (b) 60; (c) 90

图9 图 7红线的剖面图当稀疏投影个数为(a) 30; (b) 60; (c) 90

Fig.9 Image profifiles marked by red lines in Fig. 7 with the number of sparse views of (a) 30; (b) 60; (c) 90

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