Bostock M J, Holland D J, Nietlispach D
Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Old Addenbrooke's Site, Cambridge, CB2 1GA, UK.
Chemical and Process Engineering Department, University of Canterbury, Christchurch, New Zealand.
J Biomol NMR. 2017 Jun;68(2):67-77. doi: 10.1007/s10858-016-0062-9. Epub 2016 Sep 20.
NMR spectroscopy is central to atomic resolution studies in biology and chemistry. Key to this approach are multidimensional experiments. Obtaining such experiments with sufficient resolution, however, is a slow process, in part since each time increment in every indirect dimension needs to be recorded twice, in quadrature. We introduce a modified compressed sensing (CS) algorithm enabling reconstruction of data acquired with random acquisition of quadrature components in gradient-selection NMR. We name this approach random quadrature detection (RQD). Gradient-selection experiments are essential to the success of modern NMR and with RQD, a 50 % reduction in the number of data points per indirect dimension is possible, by only acquiring one quadrature component per time point. Using our algorithm (CS), high quality reconstructions are achieved. RQD is modular and combined with non-uniform sampling we show that this provides increased flexibility in designing sampling schedules leading to improved resolution with increasing benefits as dimensionality of experiments increases, with particular advantages for 4- and higher dimensional experiments.
核磁共振光谱学是生物学和化学中原子分辨率研究的核心。这种方法的关键是多维实验。然而，要获得具有足够分辨率的此类实验是一个缓慢的过程，部分原因是每个间接维度中的每次时间增量都需要以正交方式记录两次。我们引入了一种改进的压缩感知（CS）算法，能够重建在梯度选择核磁共振中通过随机采集正交分量获取的数据。我们将这种方法命名为随机正交检测（RQD）。梯度选择实验对于现代核磁共振的成功至关重要，通过RQD，每个间接维度的数据点数有可能减少50%，即每个时间点仅采集一个正交分量。使用我们的算法（CS），可以实现高质量的重建。RQD是模块化的，并且与非均匀采样相结合，我们表明这在设计采样时间表方面提供了更大的灵活性，随着实验维度的增加，分辨率提高，益处也增加，对于四维及更高维实验具有特别的优势。
