Accelerated Structure-Aware Sparse Bayesian Learning for Three-Dimensional Electrical Impedance Tomography
Accelerated Structure-Aware Sparse Bayesian Learning for Three-Dimensional Electrical Impedance Tomography
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DOI：
10.1109/tii.2019.2895469
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发表时间：
2019-09-01
影响因子：
12.3
通讯作者：
Jia, Jiabin
中科院分区：
文献类型：
--
作者：
Liu, Shengheng;Wu, Hancong;Jia, Jiabin
在本文中，我们考虑使用电阻抗断层扫描（EIT）技术重建三维（3-D）电导率分布。提出了一种高分辨率、高效的求解EIT逆问题的算法。该算法是在最近提出的一种基于结构感知稀疏贝叶斯学习（SA-SBL）的EIT重建方法的基础上进行扩展的。为了提高重建精度，将三维几何中近层之间的相关性纳入到结构中。此外，提出了一种基于近似消息传递的有效方法来加速大规模三维学习过程。为了验证该算法，利用实测数据进行了数值实验。视觉和定量度量比较表明，在所有测试用例中，所提方法在重建精度和计算复杂度方面都优于现有方法。基于sa - sbl的重建方法可以保留医学体的三维结构，减少系统伪影，提高计算效率。
In this paper, we consider the reconstruction of three-dimensional (3-D) conductivity distribution using electrical impedance tomography (EIT) technique. A high-resolution and efficient algorithm is developed to solve the EIT inverse problem. The presented algorithm is extended upon a recently proposed novel EIT reconstruction approach based on structure-aware sparse Bayesian learning (SA-SBL). The correlation between proximal layers in the 3-D geometry are incorporated into the structure prior to improve the reconstruction accuracy. In addition, an efficient approach based on approximate message passing is developed to accelerate the large-scale 3-D learning process. To validate the algorithm, numerical experiments using real recorded data are conducted. The visual and quantitative-metric comparisons show that the proposed method outperforms the existing methods in terms of reconstruction accuracy and computational complexity in all test cases. The SA-SBL-based reconstruction approach can preserve the 3-D structure of medical volume, reduce the systematic artifacts, and improve the computational efficiency.