Enhanced spectral reconstruction of ultrafast spatiotemporal encoded 2D NMR spectroscopy

Nuclear Magnetic Resonance (NMR) is extensively utilized in research as a non-invasive technique for investigating molecular structures and composite components. The spatiotemporal encoding (SPEN) technique effectively accelerates multi-dimensional NMR experiments. In ultrafast SPEN NMR, the acquired data are divided into odd and even segments corresponding to the positive and negative gradients during the decoding stage, respectively. However, the interlaced Fourier transform (FT) method used to reconstruct a full-width spectrum from these segments often suffers from severe noise contamination, necessitating the development of a more effective spectrum reconstruction method.

In this work, we analyze the noise amplification effect of the interlaced FT and find that the noise is most significant in two edge regions of the spectrum along the indirect dimension due to the relatively small time offset differences between odd and even segments in those regions. Consequently, we develop an iterative optimization method to obtain the full-width spectrum while mitigating the noise. The proposed method incorporates the odd and even data segments into an objective function with sparsity regularization to simplify the spectrum, which is then refined iteratively during the optimization. As a result, the reconstructed spectrum is significantly cleaner and maintains the full spectral width. Experimental results demonstrate a remarkable improvement in the readability and interpretability of SPEN data, evidenced by clearer signal peaks and reduced background noise.

The proposed reconstruction method provides a reliable approach for processing SPEN 2D NMR data, effectively addressing the low sensitivity issue in the joint reconstruction on odd and even segments. Combining SPEN's ultrafast data acquisition with the proposed high-sensitivity spectrum reconstruction method enhances the utility of NMR for more accurate molecular structure analysis and component identification in composite samples, particularly promoting NMR research in rapid reaction systems.