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Undersampling can accelerate the signal acquisition but at the cost ofbringing in artifacts. Removing these artifacts is a fundamental problem insignal processing and this task is also called signal reconstruction. Throughmodeling signals as the superimposed exponential functions, deep learning hasachieved fast and high-fidelity signal reconstruction by training a mappingfrom the undersampled exponentials to the fully sampled ones. However, themismatch, such as the sampling rate of undersampling, the organ and thecontrast of imaging, between the training and target data will heavilycompromise the reconstruction. To address this issue, we propose AlternatingDeep Low-Rank (ADLR), which combines deep learning solvers and classicoptimization solvers. Experiments on the reconstruction of synthetic andrealistic biomedical magnetic resonance signals demonstrate that ADLR caneffectively mitigate the mismatch issue and achieve lower reconstruction errorsthan state-of-the-art methods.

Alternating Deep Low-Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications