Bayesian Image Reconstruction Using Weighted Laplace Prior for Lung Respiratory Monitoring With Electrical Impedance Tomography
Bayesian Image Reconstruction Using Weighted Laplace Prior for Lung Respiratory Monitoring With Electrical Impedance Tomography
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DOI：
10.1109/tim.2022.3220279
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发表时间：
2023
影响因子：
5.6
通讯作者：
Yang Wu;Bai Chen;Kai Liu;Shan Huang;Yan Li;J. Jia;Jiafeng Yao
中科院分区：
文献类型：
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作者：
Yang Wu;Bai Chen;Kai Liu;Shan Huang;Yan Li;J. Jia;Jiafeng Yao
电阻抗断层扫描 (EIT) 的空间分辨率较差，由于其高度非线性和不适定性质，限制了其在医疗设备中的使用。设计了一种新颖的分层块稀疏贝叶斯学习 (BSBL) 方法，用于 EIT 肺呼吸监测。正是其出色的建模能力和噪声鲁棒性使得 BSBL 能够自适应地探索和利用内部电导率分布，例如块稀疏性和块内相关性。首先，采用$K$-最近邻（KNN）策略根据聚类属性自动对每个块进行分组，并根据空间距离约束块内相关矩阵。其次，考虑使用加权拉普拉斯（WL）先验的三层分层 BSBL 模型来增强恢复性能。最后，对贝叶斯推理进行有效的边界优化（BO）方法，避免了繁琐的参数调整。这提高了恢复性能、算法鲁棒性和计算效率。此外，均方根误差（RMSE）、图像相关系数（ICC）和相对尺寸覆盖率（RCR）用于图像质量的定量比较。数值模拟和体内肺呼吸实验表明，所提出的 KNN-BSBL-WL 方法在重建精度和计算时间方面优于现有的参考方法。平均而言，KNN-BSBL-WL 获得 RMSE $= 0.07\,\,\pm \,\,0.02$ 、ICC $= 0.96\,\,\pm \,\,0.01$ 和 RCR $= 0.98\,\,\pm \,\,0.05$ ，它们最接近实验中获得的记录快照的真实值。同时，KNN-BSBL-WL的平均时间为0.16 s，与参考的KNN-BSBL方法相比，实现了接近$\sim 3$的加速比。因此，该方法是一种高效、鲁棒的影像重建手段，进一步提高了EIT在呼吸系统临床应用中的可行性。
The poor spatial resolution of electrical impedance tomography (EIT) limits its use in medical devices because of its highly nonlinear and ill-posed nature. A novel hierarchical block sparse Bayesian learning (BSBL) method is designed for lung respiratory monitoring with EIT. It is its excellent modeling capability and noise robustness that allows BSBL to adaptively explore and exploit the internal conductivity distribution, e.g., block sparsity and intra-block correlation. First, the $K$ -nearest neighbor (KNN) strategy is employed to automatically group each block based on the clustering property and incorporated to constrain the intra-block correlation matrix based on the spatial distance. Second, a three-layer hierarchical BSBL model using weighted Laplace (WL) prior is considered to enhance the recovery performance. Finally, an efficient bound optimization (BO) method is performed for Bayesian inference, avoiding the tedious parameter adjustment. This leads to improvements in recovery performance, algorithm robustness, and computational efficiency. Moreover, root mean square error (RMSE), image correlation coefficient (ICC), and relative size coverage ratio (RCR) are applied for quantitative comparisons of image quality. Numerical simulations and in vivo lung respiratory experiments showed that the proposed KNN-BSBL-WL method outperforms existing referenced methods in terms of reconstruction accuracy and computational time. On average, KNN-BSBL-WL obtains the RMSE $= 0.07\,\,\pm \,\,0.02$ , ICC $= 0.96\,\,\pm \,\,0.01$ , and RCR $= 0.98\,\,\pm \,\,0.05$ , which are closest to the true values of the recorded snapshots obtained in the experiments. Meanwhile, the average time of KNN-BSBL-WL is 0.16 s, achieving an acceleration ratio close to $\sim 3$ compared with the referenced KNN-BSBL method. Therefore, the proposed method is an efficient and robust means of imaging reconstruction, which further improves the feasibility of EIT in respiratory clinical applications.