Kinam Kwon1, Dongchan Kim1, Hyunseok Seo1, Jaejin Cho1, Byungjai Kim1, and HyunWook Park1
A long imaging time has been regarded as a major drawback
of MRI, and many techniques have been proposed to overcome this problem.
Parallel imaging (PI) and compressed sensing (CS) techniques utilize different
sensitivity of multi-channel RF coils and sparsity of signal in a certain
domain to remove aliasing artifacts that are generated by subsampling,
respectively. In this study, an artificial neural networks (ANN) are applied to
MR reconstruction to reduce imaging time, and it is shown that the ANN model
has a potential to be comparable to PI and CS.
