Using Deep Neural Networks to Reconstruct Non-uniformly Sampled NMR Spectra.

ABSTRACT: Non-uniform and sparse sampling of multi-dimensional NMR spectra has over the last decade become an important tool to allow for fast acquisition of multi-dimensional NMR spectra with high resolution. The success of non-uniform sampling NMR hinge on both the development of algorithms to accurately reconstruct the sparsely sampled spectra and the design of sampling schedules that maximise the information contained in the sampled data. Traditionally, the reconstruction tools and algorithms have aimed at reconstructing the full spectrum and thus 'fill out the missing points' in the time-domain spectrum, although other techniques are based on multi-dimensional decomposition and extraction of multi-dimensional shapes. Also over the last decade, machine learning, deep neural networks, and artific

SUBMITTER: Hansen DF 

PROVIDER: S-EPMC6859195 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature