Improved MR image reconstruction from sparsely sampled scans based on neural networks

摘要

This paper concerns a novel application of machine learning to magnetic resonance imaging (MRI) by considering neural network models for the problem of image reconstruction from sparsely sampled 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. The goal in such a case is to reduce the measurement time by omitting as many scanning tra- jectories as possible. This approach, however, entails underdetermined equations and leads to poor image recon- struction. It is proposed here that significant improvements could be achieved concerning image reconstruction if a procedure, based on machine learning, for estimating the missing samples of complex k-space were introduced. To this end, the viability of involving supervised and unsupervised 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.