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Background: Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMR. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration factor is high. Objective: To reconstruct high fidelity spectra from highly accelerated NMR and achieve much better quantitative measures. Methods: A virtual peak (VIP) approach is proposed to self-learn the prior spectral information, such as the central frequency and peak lineshape, and then feed these information into the reconstruction. The proposed method is further implemented with cloud computing to facilitate online, open, and easy access. Results: Results on synthetic and experimental data demonstrate that, compared with the state-of-the-art method, the new approach provides much better reconstruction of low-intensity peaks and significantly improves the quantitative measures, including the regression of peak intensity, the distances between nuclear pairs, and concentrations of metabolics in mixtures. Conclusion: Self-learning prior peak information can improve the reconstruction and quantitative measures of spectra. Significance: This approach enables highly accelerated NMR and may promote time-consuming applications such as quantitative and time-resolved NMR experiments.
For accelerated multi-dimensional NMR spectroscopy, non-uniform sampling is a powerful approach but requires sophisticated algorithms to reconstruct undersampled data. Here, we first devise a high-performance deep learning framework (MoDern), which s
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI). Cloud computing technologies have great advantages in building an easily accessible platform to deploy advanced algorithms
T2-Shuffling reconstructs multiple sharp T2-weighted images from a single volumetric fast spin-echo (3D-FSE) scan. Wave-CAIPI is a parallel imaging technique that achieves good reconstruction at high accelerations through additional sinusoidal gradie
Positron emission tomography, like many other tomographic imaging modalities, relies on an image reconstruction step to produce cross-sectional images from projection data. Detection and localization of the back-to-back annihilation photons produced
In 2000 V. Lomonosov suggested a counterexample to the complex version of the Bishop-Phelps theorem on modulus support functionals. We discuss the $c_0$-analog of that example and demonstrate that the set of sup-attaining functionals is non-trivial,