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Neural network reconstruction of MR images from noisy and sparse k-space samples

This paper concerns a novel application of artificial neural networks (ANN) to magnetic resonance imaging (MRI) by considering models for solving the problem of image estimation from sparsely sampled and noisy k-space. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. It is proposed here that significant improvements could be achieved concerning image reconstruction if a procedure, based on interpolating ANNs, for estimating the missing samples of complex k-space were introduced. To this end, the viability of involving supervised neural network algorithms for such a problem is considered and it is found that their image reconstruction results are very favorably compared to the ones obtained by the trivial zero-filled k-space approach or traditional more sophisticated interpolation approaches.