Hengfa Lu

TL;DR: Experimental results on simulated and real magnetic resonance spectroscopy data show that the proposed approach can successfully recover full signals from very limited samples and is robust to the estimated tensor rank.

TL;DR: In this article, the authors presented a proof-of-concept of the application of deep learning and neural networks for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data.

TL;DR: It is shown that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for large volume of realistic training data usually required in the deep learning approach.

TL;DR: A low-rank matrix factorization method that avoids singular value decomposition is introduced to enable fast MRS reconstruction without sacrificing the spectra quality and enables reconstructing the challenging 3-D MRS within 15 minutes.

TL;DR: The proposed method has been shown to reconstruct high quality MRS spectra from non-uniformly sampled data in the hybrid time and frequency plane, and outperforms the state-of-the-art compressed sensing approach on recovering low-intensity spectral peaks and robustness to different sampling patterns.