MMV-Net: A Multiple Measurement Vector Network for Multifrequency Electrical Impedance Tomography

Abstract

Multi-frequency Electrical Impedance Tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical applications. Conventional model-based image reconstruction methods suffer from low spatial resolution, unconstrained frequency correlation and high computational cost. Deep learning has been extensively applied in solving the EIT inverse problem in biomedical and industrial process imaging. However, most existing learning-based approaches deal with the single-frequency setup, which is inefficient and ineffective when extended to the multifrequency setup. This paper presents a Multiple Measurement Vector (MMV) model based learning algorithm named MMV-Net to solve the mfEIT image reconstruction problem. MMV-Net considers the correlations between mfEIT images and unfolds the update steps of the Alternating Direction Method of Multipliers for the MMV problem (MMV-ADMM). The non-linear shrinkage operator associated with the weighted l2,1 regularization term of MMV-ADMM is generalized in MMV-Net with a cascade of a Spatial Self-Attention module and a Convolutional Long Short-Term Memory (ConvLSTM) module to better capture intra-and inter-frequency dependencies. The proposed MMV-Net was validated on our Edinburgh mfEIT Dataset and a series of comprehensive experiments. The results show superior image quality, convergence performance, noise robustness and computational efficiency against the conventional MMV-ADMM and the state-of-the-art deep learning methods.

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

“…Comparing the results with those obtained in other works [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] in the field of EIT reconstruction using machine learning methods, it can be concluded that at least some of the models presented in this work (especially the CART model) dominate the published achievements in terms of the obtained measures of reconstruction quality.…”

Advances of deep learning in electrical impedance tomography image reconstruction

“…The DNN method proposed by Fan et al can be used for both 2D and 3D imaging of EIT ( Fan and Ying, 2020 ). In addition, for the needs of 3D cell culture process monitoring, researchers have also proposed numerous deep learning-based methods that could be extended to 3D, such as SADB-Net ( Chen and Yang, 2021 ), GCNM ( Herzberg et al, 2021 ), MSFCF-Net ( Liu et al, 2022a ), M-STGNN ( Chen et al, 2022a ),MMV-Net ( Chen et al, 2022b ), etc. , which provide a large number of algorithmic bases for future 3DEIT reconstruction studies.…”

“…Exploiting the frequency and spatial correlation is an impressive technique for improving the image quality of multi-frequency electromagnetic tomography ( Xiang et al, 2020 ). Based on this ideology, Chen et al (2022b) proposed a multiple measurement vector network (MMV-Net) that integrated the advantages of the traditional Alternating Direction Method of Multipliers for the MMV problem (MMV-ADMM) and deep learning. By adding a spatial self-attention module and a convolutional long short-term memory module, which can adequately capture the intra-frequency and inter-frequency dependencies, it enhances picture quality, generalization ability, noise robustness, and convergence performance.…”

Communication-Efficient Federated Learning for Power Load Forecasting in Electric IoTs

“…Recently, data-driven machine learning [4][5][6] has been widely used in power load forecasting and achieves marvelous prediction accuracy. Generally, the power grid company (the server) needs to collect massive power load data from enterprises or individuals (the clients), as shown in Fig.…”