Efficient Multitask Structure-Aware Sparse Bayesian Learning for Frequency-Difference Electrical Impedance Tomography
Efficient Multitask Structure-Aware Sparse Bayesian Learning for Frequency-Difference Electrical Impedance Tomography
复制标题
DOI：
10.1109/tii.2020.2965202
复制
发表时间：
2021-01-01
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;Huang, Yongming;Jia, Jiabin
频率差电阻抗断层成像（fdEIT）最初是为了减轻基线数据集不可用时建模误差引起的系统性伪影而开发的。代替精细的解剖成像，在当前的fdEIT研究中仅解决了粗略的异常检测，主要是由于其低空间分辨率。另一方面，对于fdEIT重建算法的研究也不够深入。在这篇文章中，我们提出了一个有效的和高空间分辨率的算法，同时重建多个fdEIT帧对应的注入电流与多个频率。电阻抗断层成像重建问题被认为是在一个分层贝叶斯框架内，任务内的空间聚类和任务间的依赖性自动学习和利用在一个无监督的方式。通过采用改进的边际似然最大化方法来加速计算。通过实际数据实验验证了该算法的恢复性能。
Frequency-difference electrical impedance tomography (fdEIT) was originally developed to mitigate the systematic artifacts induced by modeling errors when a baseline dataset is unavailable. Instead of fine anatomical imaging, only coarse anomaly detection has been addressed in current fdEIT research mainly due to its low spatial resolution. On the other hand, there has been not enough study on fdEIT reconstruction algorithm as well. In this article, we propose an efficient and high-spatial-resolution algorithm for simultaneously reconstructing multiple fdEIT frames corresponding to inject currents with multiple frequencies. The electrical impedance tomography reconstruction problem is considered within a hierarchical Bayesian framework, where both intratask spatial clustering and intertask dependency are automatically learned and exploited in an unsupervised manner. The computation is accelerated by adopting a modified marginal likelihood maximization approach. Real-data experiments are conducted to verify the recovery performance of the proposed algorithm.