  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 address the multi-frequency setup. In this paper, we present a
Multiple Measurement Vector (MMV) model based learning algorithm named MMV-Net
to solve the mfEIT image reconstruction problem. MMV-Net takes into account the
correlations between mfEIT images and unfolds the update steps of the
Alternating Direction Method of Multipliers (ADMM) for the MMV problem. The
non-linear shrinkage operator associated with the weighted l2_1 regularization
term is generalized with a cascade of a Spatial Self-Attention module and a
Convolutional Long Short-Term Memory (ConvLSTM) module to capture intra- and
inter-frequency dependencies. The proposed MMVNet was validated on our
Edinburgh mfEIT Dataset and a series of comprehensive experiments. All
reconstructed results show superior image quality, convergence performance and
noise robustness against the state of the art.
