Neural Networks in MR Image Estimation from Sparsely Sampled Scans
This paper concerns a novel application of machine learning to Magnetic Resonance Imaging (MRI) by considering Neural Network models for the problem of image estimation 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 trajectories as possible. This approach, however, entails underdetermined equations and leads to poor image reconstruction. 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.
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