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Noninvasive Cardiac Transmembrane Potential Imaging via Global Features Based FISTA Network

Noninvasive electrophysiological imaging plays an important role in the clinical diagnosis and treatment of heart diseases over recent years. Transmembrane potential (TMP) is one of the most important cardiac physiological signals, which can be used to diagnose heart disease such as premature beat and myocardial infarction. Considering the nonlocal self-similarity of TMP distribution and integrating traditional optimization strategy into deep learning, we proposed a novel global features based Fast Iterative Shrinkage/Thresholding network, named as GFISTA-Net. The proposed method has two main advantages over traditional methods, namely, the l1-norm regularization helps to avoid overfitting the model on high-dimensional but small-training data, and facilitates embedded the spatio-temporal correlation of TMP. Experiments demonstrate the power of our method.