Abstract

Development of multidimensional NMR is essential to many applications, for example in high resolution structural studies of biomolecules. Multidimensional techniques enable separation of NMR signals over several dimensions, improving signal resolution, whilst also allowing identification of new connectivities. However, these advantages come at a significant cost. The Fourier transform theorem requires acquisition of a grid of regularly spaced points to satisfy the Nyquist criterion, while frequency discrimination and acquisition of a pure phase spectrum require acquisition of both quadrature components for each time point in every indirect (non-acquisition) dimension, adding a factor of 2N-1 to the number of free- induction decays which must be acquired, where N is the number of dimensions. Compressed sensing (CS) ℓ1-norm minimisation in combination with non-uniform sampling (NUS) has been shown to be extremely successful in overcoming the Nyquist criterion. Previously, maximum entropy reconstruction has also been used to overcome the limitation of frequency discrimination, processing data acquired with only one quadrature component at a given time interval, known as random phase detection (RPD), allowing a factor of two reduction in the number of points for each indirect dimension (Maciejewski et al. 2011 PNAS 108 16640). However, whilst this approach can be easily applied in situations where the quadrature components are acquired as amplitude modulated data, the same principle is not easily extended to phase modulated (P-/N-type) experiments where data is acquired in the form exp (iωt) or exp (-iωt), and which make up many of the multidimensional experiments used in modern NMR. Here we demonstrate a modification of the CS ℓ1-norm approach to allow random coherence order selection (RCS) for phase modulated experiments; we generalise the nomenclature for RCS and RPD as random quadrature detection (RQD). With this method, the power of RQD can be extended to the full suite of experiments available to modern NMR spectroscopy, allowing resolution enhancements for all indirect dimensions; alone or in combination with NUS, RQD can be used to improve experimental resolution, or shorten experiment times, of considerable benefit to the challenging applications undertaken by modern NMR.

Highlights

  • NMR spectroscopy and MRI both suffer limitations due to the measurement time required

  • Since Compressed sensing (CS) relies on sparsity in the frequency domain, extending a spectrum into higher dimensions increases the sparsity reducing the number of points required to reconstruct the spectrum

  • The previously introduced random phase detection (RPD) reduces the number of points required for frequency discrimination by a factor of two in each indirect dimension where the data is recorded as cosine and sine components [31]

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Summary

Introduction

NMR spectroscopy and MRI both suffer limitations due to the measurement time required. NMR experiments may be defined as “sampling limited” or “sensitivity limited” [1]. In the former, the length of the experiment is determined by the need to sample multiple indirect dimensions, often to high resolution; in this situation, signal-to-noise is ample, and so time is spent acquiring points on a Nyquist grid to achieve appropriate resolution in the various indirect. Published under licence by IOP Publishing Ltd doi:10.1088/1742-6596/699/1/012004

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