Algorithm

Engineering

Bayesian probability

Statistics

Computation

Bayesian optimization

Iterative reconstruction

Computer science

Mathematical optimization

Image resolution

Electrical impedance

Tomography

Physics

Pattern recognition (psychology)

Mathematics

Optics

Expectation–maximization algorithm

Artificial intelligence

Cluster analysis

Electrical impedance tomography

Maximum likelihood

Maximization

Bayesian inference

Electrical engineering

IEEE Transactions on Industrial Informatics

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.

Efficient Multitask Structure-Aware Sparse Bayesian Learning for Frequency-Difference Electrical Impedance Tomography